research on online communities

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• Published: 02 August 2024

Online communities come with real-world consequences for individuals and societies

• Atte Oksanen   ORCID: orcid.org/0000-0003-4143-5580 1 ,
• Magdalena Celuch   ORCID: orcid.org/0000-0001-8941-0396 1 ,
• Reetta Oksa   ORCID: orcid.org/0000-0002-8007-4653 1 &
• Iina Savolainen   ORCID: orcid.org/0000-0002-8811-965X 1  

Communications Psychology volume  2 , Article number:  71 ( 2024 ) Cite this article

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Online communities have become a central part of the internet. Understanding what motivates users to join these communities, and how they affect them and others, spans various psychological domains, including organizational psychology, political and social psychology, and clinical and health psychology. We focus on online communities that are exemplary for three domains: work, hate, and addictions. We review the risks that emerge from these online communities but also recognize the opportunities that work and behavioral addiction communities present for groups and individuals. With the continued evolution of online spheres, online communities are likely to have an increasingly significant role in all spheres of life, ranging from personal to professional and from individual to societal. Psychological research provides critical insights into understanding the formation of online communities, and the implications for individuals and society. To counteract risks, it needs to identify opportunities for prevention and support.

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Introduction.

Online communities are social networks on the internet that utilize technology for interaction. They began to gain popularity in the 1990s with the development of the internet and information and communications technologies 1 , 2 , 3 . The emergence of online communities was further accelerated by Web 2.0 and social media starting in the mid-2000s 4 . Social media platforms provide users fast access to likeminded others, and they speed up communication and offer new ways for interaction 5 , 6 , 7 .

The progress of these interactive technologies has been remarkably fast, and they carry both opportunities and risks. The work context is a good example of the complexity of online communities. On one hand, online communication is flexible, fast, and effective regardless of location, and it provides workers with new ways to collaborate and socialize with each other 8 . On the other hand, online communication bears risks, such as workplace cyberbullying 9 , 10 and misinterpretation of messages and feedback, endangering the mental well-being of employees by inducing technostress, psychological distress, and work exhaustion 11 , 12 . We recognize that online communities can be supportive and enhance well-being in many ways, but there are also online communities that carry risks for participants and wider society such as hate and addiction communities.

In this perspective article, we review the characteristics of online communities along with the opportunities and risks they present to their users. We cover the role of online communities in the contexts of 1) work, 2) hate and harassment, and 3) addiction, as three domains of outstanding relevance for the society that showcase the multifaceted nature of online communities. These diverse domains provide an excellent starting point for the theoretical overview of online communities. The first topic of work recognizes that online communication and online communities have a growing importance in today’s work life. The second topic concerns research evidence of online hate communities that are based on harmful ideas and actions against other people. This topic has had massive implications on political and societal discussions starting in the 2010s. The third topic talks about online communities in the context of addictions. The online dissemination and proliferation of various views and behaviors have further led to people discovering new, potentially harmful activities or becoming excessively engaged in the digital world. This has prompted significant research into online addictions 13 , 14 , 15 .

How and why online communities form

Human beings are inherently social and seek companionship and social engagement whenever possible 16 . Online communication responds to this social need of belonging. Online communities emerge and thrive in digital spaces, comprised of members who engage in active communication in a shared topic or interest area 17 , 18 . Online communities can form in a variety of online contexts, including but not limited to social media platforms, discussion forums, and chatrooms 19 . Online communities are significant for finding companionship, fostering connection, accessing information, and receiving support 20 , 21 . Like any group or community, online communities vary in size, cohesion, and network and focus area. The degree of members’ anonymity may also vary considerably 4 , 22 . A distinct feature of the global online sphere is that no matter how unusual or rare one’s interests are, they are likely to find others with similar interests 23 . Members of online communities are often heterogenous in their social characteristics, including socioeconomic status, life stage, ethnicity, and gender, but tend to be likeminded and homogeneous in terms of their shared interests and attitudes 24 , 25 .

The online sphere provides an ideal environment for the building of networks that hold significance for the social and personal identity of their participants 5 . Social identity theory (SIT), as initially proposed by Tajfel and Turner 26 , describes a process in which an individual’s identity is partially shaped by their sense of belonging to preferred social groups. This concept is often measured by evaluating an individual’s subjective feeling of being a part of the desired groups 27 . Theories and models that use the social identity approach 28 , 29 are very relevant for understanding online communities and online group behavior.

One of the most important models proposed over the years has been the social identity model of deindividuation effects (SIDE) 30 , 31 . The model was originally motivated by the topic of online communication’s anonymity – an issue that had already drawn the attention of social psychologists in the early 1980s 32 . According to the SIDE model, the deindividuation effect of social identification is especially prevalent in online interactions that are characterized by at least a certain level of anonymity, as it promotes a shift from individual to group self and therefore facilitates behaviors benefiting the group as well as stereotyping outgroup members and viewing them as a representative of their group rather than an individual 33 , 34 , 35 , 36 , 37 . Relatedly, lack of social cues, such as eye contact, in online interactions has been found to lead to behavioral disinhibition through the so-called online sense of unidentifiability 38 .

Context collapse occurs very commonly in social media. This means that boundaries between different social spheres blur together (e.g. interacting with people from different life spheres such as work, family, and friends on the same platform). This can influence and challenge the users’ self-presentation and their navigation within different online discussions and audiences 39 . In these diverse social contexts, which can vary greatly in terms of values and norms, users may need to balance their personal authenticity based on their audience expectations. Additionally, they can face situations that compromise their privacy or necessitate self-censorship 40 , 41 . However, social media simultaneously provides multiple features or opportunities (i.e., affordances) for users to control and maintain their public identities and social networks. In other words, users can manage how they present themselves to the public online and how they interact with their social networks through the tools and functions provided by social media platforms. Typically, these tools include the customization of ones’ profile, maintaining visibility, allowing access, editing content, and providing links and connections to other platforms 39 , 42 , 43 , 44 . Affordances essentially link to the question of how users maintain and create smaller groups or wider communities within certain platforms. This depends on the features and design of each social media platform or internet site.

Characteristics of both internet platforms and applications strongly influence online human behavior. In particular, commercial platforms are designed to attract people’s interests in the content. Moral and emotional contents spread faster on social media as they capture users’ attention more effectively as compared to neutral content 45 . The process is also facilitated by the algorithms of social media platforms 5 , 46 , and social influence, which lead people to engage with content that is already popular 47 . This has a profound impact on online communities and the way they communicate, especially as these effects have been found to be much stronger within networks comprised of individuals who share similar views than between such networks 48 . Thus, a tendency can be induced toward likeminded individuals who support certain opinions and behaviors, strengthening their existing attitudes and providing a stronger identification with the ingroup 49 , 50 . In this context, even exposure to differing opinions may primarily serve as a tool to further distinguish between “us” and “them” and hinder productive dialogue 4 , 51 .

These mechanisms are further captured by the Identity Bubble Reinforcement Model (IBRM), which focuses on explaining how characteristics of online communication facilitate the formation of tight-knit networks, namely social media identity bubbles 4 , 5 . Such bubbles or echo chambers are characterized by three mutually reinforcing features: high identification with the other members (ingroup), homophily or the strong tendency to interact with likeminded others, and information bias, namely heavy reliance on information obtained from the community 4 , 5 . Involvement in such communities has been found to be associated with compulsive internet use 5 , cyberaggression 52 and problem gambling 53 . At the same time, involvement in online identity bubbles facilitates social support and may buffer mental well-being in some situations 54 .

In summary, there are three core aspects to consider in online communities. First, technological design is a critical component that impacts what people can do within online communities. The phenomenon of online communities has existed as long as the internet 2 , but current social media platforms use different interfaces and AI algorithms than their earlier versions, being essentially engineered to provide content for users. This technological side impacts significantly how people behave and react online. Second, contextual issues are highly important in online communities. Generally, contexts in the online sphere may collapse easily. At the same time, the internet and social media platforms facilitate development of very closed online communities that are based on shared interests. These interests may sometimes be very specific. Core social psychological group theories and their updates provide good tools for understanding evolving online group behavior. SIDE and IBRM are examples of theories that have been proven very useful in empirical research.

Social media communities at work

The accelerated development of information technology in recent decades has significantly reshaped the workplace. The foundation for this technological advancement was laid in the 1960s with the development of ARPANET, a forerunner to the internet 4 . The same era also witnessed the birth of the Open Diary – an internet-based diary community allowing user participation through messages, thereby serving as a precursor to social media 55 . During the emergence of the internet in the 1990s, known now as the Web 1.0 era, personal web pages, content creation, and numerous work communication tools, such as online telecommunications and email, became prevalent 55 , 56 . The term Web 2.0 was first coined in 2004, concurrently when Facebook became popular, symbolizing the internet’s transition to a more socially diverse and interactive era 55 . The change in user behavior from passive web content consumers to active, bidirectional information creators and editors was an evident part of the transition from Web 1.0 to Web 2.0 4 .

In recent years, there has been a substantial increase in the use of digital communication technologies in the workplace, primarily driven by advancements of the internet and social media services 12 , 57 . Numerous expert organizations are now leveraging corporate social media platforms, such as Microsoft Teams and Workplace from Meta, for their communication needs 8 , 58 . Networking in enterprise social media platforms facilitates real-time messaging, task organisation, and formal and informal team collaboration, synchronously and asynchronously across organisational groups and in different geographic locations 8 , 59 , 60 . Work communication also unfolds through instant-messaging applications, such as WhatsApp, and general social media platforms, such as Facebook, X (formerly Twitter), and LinkedIn, which are being utilised for professional purposes. These social platforms have the potential to encourage professionals to engage in collaboration, share information and ideas, and expand their expertise on a global scale, extending beyond their specific job responsibilities and organisational boundaries 8 , 61 . Notably, social communication tools have expanded to encompass traditional white-collar environments and now provide value for blue-collar workers as well, for example, as a medium for communication and task organisation 11 .

Social media messaging and networking for professional purposes not only enhance knowledge transfer and flow but also nurture the human need for social belonging 62 , 63 . Given the growing prevalence of remote and hybrid forms of work, social media has the potential to maintain and foster social interactions regardless of location 57 , 64 . Remote and hybrid work arrangements can, however, reduce the chances of establishing and nurturing high-quality work relationships 65 , 66 . Recent studies have also indicated a link between resistance to remote work and having quality workplace relationships 67 . Indeed, working far from the physical work community can increase the growing phenomenon of loneliness at work 65 , 68 , 69 . At the same time, in some circumstances, online networking among colleagues nurtures social connections and alleviates feelings of loneliness 54 . Social connections and feelings of belongingness in the work community and one’s professional circles are vital to support employees’ mental well-being and combat loneliness at work 54 , 70 . Feelings of loneliness at work can, for example, lower professionals’ work engagement, increase their dissatisfaction at work 71 and burnout 72 .

The use of social media platforms for professional objectives can enrich communication and foster meaningful connections 8 , 73 . Professional online relationships can be formed and maintained individually person to person or as a part of bigger professional online communities. In the professional sphere, online communities are commonly referred to as communities of practice due to their origins within the cultural framework of either virtual or traditional organisations 74 . Communication visibility in these online communities of practice can foster knowledge sharing and social learning, trust, and innovation 75 , 76 , 77 . The sense of belonging and togetherness with colleagues can also be enhanced in these online communities 8 , 78 . Online communities of practice can be a source of affective social support that promotes experiencing group identification and meaningfulness, which in turn can foster employees’ engagement in their work 79 . Employees’ social media collaboration is also associated with increased team and employee performance 78 , and employees perceived social media–enabled productivity 80 . Both formal and informal online communities are known to accelerate professional development 8 , 81 , 82 .

However, online communities at work can have downsides. These include tensions within the organisation due to employees sharing nonwork-related information that can tighten the bonds and build trust but, interestingly, can also hinder work-related information sharing 76 . Furthermore, stress arising from technology use (i.e., technostress), psychological distress, and burnout are pervasive challenges of professional online collaboration in technologised work environments 11 , 12 . Concentration problems can emerge, and the boundaries of work and private life can also be blurred and stimulate conflicts 83 , 84 . In addition, social relationships at work can be challenged 85 by discrimination, ostracism, and face-to-face bullying. These issues are also present in online communication, where they take on new forms and meanings. Work-related cyberbullying 9 , 10 , 86 and hate and harassment, which may also come from fellow work community members, can be detrimental for the targets and lead to lowered well-being 87 .

Hate communities

The ease of online communication facilitates the dissemination and proliferation of negative and dangerous views and behaviors. Subsequently, online hate (i.e., cyberhate) and online hate crime have emerged as a prominent area of research in the context of online communication, with the same ease of access contributing to their prevalence 88 , 89 . Online hate covers a wide range of intensive and hostile actions that target individuals or groups based on their beliefs and demographic factors, such as ideology, sexual orientation, ethnic background, or appearance 90 . The rise of hostile online communication has been considered a growing societal concern over the past decade 4 , 87 , 91 , 92 , 93 .

The history of hate in online communication goes back to the first internet networks. Organised hate groups have always been interested in the latest technologies to recruit new members and disseminate information. For instance, White supremacists in the US were pioneers in adopting electronic communication networks during the 1980s. Notably, in 1983, neo-Nazi publisher George P. Dietz established the first dial-up bulletin board system (BBS), marking an early utilisation of online communication methods 94 . Shortly after the inception of the World Wide Web, hate groups marked their online presence. Stormfront.org, launched in 1995, was one of the first and most important hate sites during the Web 1.0. era 95 . Since then, over the past 30 years, continuous technological advancements have significantly enhanced their communication capabilities 4 .

Particularly the rise of social media since the mid-2000s was an important game changer in the dissemination and development of online hate. Foxman and Wolf 96 (p. 11) summarized this change concerning the Web 2.0 era of social media: “In the interactive community environment of Web 2.0, social networking connects hundreds of millions of people around the globe; it takes just one ‘friend of a friend’ to infect a circle of hundreds or thousands of individuals with weird, hateful lies that may go unchallenged, twisting mind in unpredictable ways.” The last 10 years of the internet have been, however, striking, as online hate has lurked from the margins and started to become a tool of political populists in the Western world 1 , 97 . Uncertainty of the times with various crises related to terrorism, economy, and the global COVID-19 pandemic have also accelerated the phenomenon.

Research on online hate associated with the COVID-19 pandemic has suggested that, in crisis situations, hate communities can organise quickly and rapidly develop new narratives 98 , 99 , reactively focusing on recent and highly debated issues 100 . Such hateful messages spread most effectively in smaller, hierarchical, and isolated online communities 99 , highlighting the dangers of online echo chambers or identity bubbles 4 , 5 , 101 . Even if hateful narratives are not endorsed by most users on the platform, the flow of such information tends to be sustained over time, as members of echo chambers encourage each other and amplify their shared worldview 102 . This is often done by referring to and contesting opposing views in a marginalising and undermining way, making counter-messaging ineffective or even counter-effective 103 . Various options of demonstrating (dis)agreement and promoting content on social media are used for creating echo chambers and disseminating hateful content 104 . However, it is worth noting that even on social media sites derived of content-promoting algorithms and vanity metrics present on many of the major platforms, users can quickly learn to recognise and promote extremist content as important and worthy of attention 105 .

The example of COVID-19-related hateful activity showed how hate communities effectively spread malicious content across various social media sites, incapacitating moderation attempts of any single platform 98 . Gaming sites are another type of environment where hate and extremist communities organise, recruit, and communicate. It has been argued that the development and characteristics of the gaming industry and the games themselves make online gaming platforms a suitable place for spreading hateful ideologies 106 . Hate communities also commonly use less moderated online spaces as an alternative to mainstream social media platforms, moving toward the creation of parallel ecosystems 107 , 108 , 109 . The need to leave mainstream spaces due to the risk of moderation and censorship is often used for community building by means of leveraging the sense of online persecution and victimisation 109 , 110 .

Hate communities, especially their influential members, use various other techniques and activities for community building. These activities include, for example, the development and promotion of jargon and coded language that underline the “us vs. them” dichotomy, often using derogatory and offensive phrasing 100 , 104 , 105 , 106 , 110 , 111 , 112 , 113 as well as the use of various audiovisual and interactive materials to capture the recipients’ attention 106 , 107 , 109 . These strategies can be used differently in different contexts and adapted to groups’ needs 114 . The incel (i.e., “involuntarily celibate males”) online community is an interesting example of how these strategies are used in practice. According to research, all active participants of online incel discussions commonly use derogatory terms to refer to women 115 and they create powerful dichotomies between themselves and outgroups: both women and society at large, using memes, reels, and other forms of online content to carry their message 115 , 116 .

Another commonly utilised method is ironic and humorous messaging in the form of memes and jokes that further allows for the spread of radical ideologies using seemingly unserious content 104 , 106 , 117 , 118 , 119 . Such jokes and memes are often part of conspiracy talk, which is a type of everyday discourse common among hate communities, referring to conspiracy theories through implicit references and anecdotal evidence from community members’ own experiences, often in reaction to news coverage from mainstream sources 119 , 120 . Research has suggested a strong community-building potential of this type of online discourse, as it allows users to share their concerns and worries and make sense of their experiences 119 . These uncertainties are used by extremist groups to create new anxieties and introduce new problems, as well as to strengthen the community, as evoking feelings of threat can boost the sense of belonging and reinforce the ingroup’s worldview 100 . This is especially concerning considering evidence on the associations of supporting far-right ideologies with distrust toward traditional media outlets 121 . Individuals distrustful toward established broadcasters may be motivated to search for alternative sources of information and, as a result, get involved in online hate communities, where they may become further radicalised through community-building practices such as those described above 107 .

Research has suggested that, over time, as online communities develop, both positive and negative sentiments in their content increase, and this effect may be stronger in hate communities than in comparable non-hateful groups 111 . This is attributed to the group-formation processes as shared outgroups are established, leading to more negative emotions being expressed. Simultaneously, involvement in a likeminded community results in more positive affect 111 . Interestingly, influential users in online hate communities commonly use seemingly neutral and value-free language, often referring to news from mainstream sources. This is, however, done in a way that is meant to evoke emotion and provoke hateful discussion 122 . This helps to avoid content deletion or user suspension and may further endanger new users looking for alternative sources of information by exposing them to hateful discussions and possibly fostering their radicalisation and involvement in the community 123 .

Hateful online content is likely to increase as a result of offline hateful acts 124 , 125 and local socio-political events that are significant to the group and their worldviews. Together, these can have long-term effects on online hate communities, resulting in increased activity and group cohesion 126 . Although online communities might avoid encouraging offline violence for fear of the discussion being moderated or even completely banned by site administrators 104 , they nevertheless contribute to the creation of an environment where hate – both online and offline – is seen as more acceptable and justified 119 , 127 , 128 . Indeed, perpetrators of violent extremist acts offline have been previously found to be involved in extremist online communities prior to the act 129 , and the spread of hateful content in social media has been tied to subsequent offline hate crimes 93 , 130 .

Addiction and online communities

There is a complex relationship between addiction and online communities which can be explained through three core factors. First, fast internet connection and mobile devices have enabled unlimited, easy, and continuous online access. Studies have reported that heavy online use symptoms are comparable to substance-related addiction, including mood modification, withdrawal symptoms, conflict, and relapses 15 , 131 . Second, major social media sites use algorithms to attract and engage their users 4 . Connectedness to others and positive emotions arising from actions and vanity metrics, such as “likes” and supportive comments, reinforce usage and can lead people to become addicted 131 , 132 . Third, participation in online communities has addictive power. For instance, Naranjo-Zolotov and colleagues 133 , who investigated Latin American individuals, found that the sense of a virtual community was the primary factor fueling addiction to social media usage. There is a symbiotic relationship between online communities and technology: technology provides the means for a wide range of activities and it’s those activities, rather than the devices themselves, that users typically become addicted to. These online activities often concern the most recognised behavioral addictions such as sex, shopping, gaming or gambling.

When discussing addiction related to online use, it should be acknowledged that, in current terminology, there is a wide variety of terms expressing the excessive use of the internet or social media. For example, compulsive internet use and problematic internet use are commonly used 134 , 135 , 136 . Technological devices and social media sites are designed to be as engaging as possible. Features such as notifications, personalised content, and interactive elements are strategically implemented to capture users’ attention and encourage prolonged usage 137 . These devices and the features within have greatly transformed social interactions, especially in technologically advanced countries and particularly among younger generations who have grown up with smart technology. Current reviews underline a need to build a more complex understanding of different ways of social media use 138 . This involves investigating the geographical, sociocultural, and digital environments within which problematic behaviors arise and unfold 15 .

In this Perspective, our focus is on exploring the role of online communities in reinforcing certain problem behaviors. Our examples come from gambling and digital gaming online communities. Online communities centered around gambling and digital gaming are growing in popularity, drawing users to engage and exchange ideas and experiences with others who share similar interests in these activities 21 , 139 . Online gambling communities usually manifest independently from the actual games, often taking the form of discussion forums dedicated to all aspects of gambling. These forums serve as platforms for participants to engage in dialogues typically including the exchange of tips, strategies, and personal experiences related to gambling 139 . A review of research on online gambling communities indicates that content on these types of online platforms commonly presents gambling in a predominantly positive light 21 . This positive portrayal also seems to resonate with individuals who have a preexisting affinity for gambling, drawing them to participate in the communities online. Joining gambling communities online also appears to be a socially transmitted behavior, as existing members frequently invite their friends or online contacts to join these communities, often through social media where gambling operators also admin and promote communities for their followers 21 , 140 . The existence of online communities dedicated to gambling provides a convenient platform for gamblers to express interests and emotions they might otherwise hesitate to share in face-to-face interactions. The risk associated with online communities, like those that unite individuals based on a common interest, goals, and norms, is that they might normalise gambling activities and encourage the development of new gambling habits and behaviors. Notably, research has linked active participation in online gambling communities to an increased risk of problem gambling 21 , 139 , 141 , 142 .

Online gaming communities are distinct from gambling communities as they inherently exist within the games they are tied to 139 . Virtual social groups that form within games tend to be persistent, and players utilise them to collaborate with each other and enhance their in-game success 143 . Within these communities, members freely exchange skills, knowledge, and virtual assets, including currency used in the game. Players can have different roles and responsabilities within gaming communities. These include sharing responsibilities and communal resources such as in-game items and money 139 . Gaming communities can significantly contribute to the construction of gamers’ online identities, which could explain the remarkable success of these communities. This process acts as a validating influence, enabling players to reintegrate themselves through features like avatars and virtual belongings within their communities 144 . Social engagement with fellow players serves as a primary motivator for gaming and can lead to positive social capital gains 145 , 146 , but it can also immerse players in the games, which can lead to excessive time spent on gaming and even to online gaming addiction 147 , 148 . Further, some in-game activities, such as forms of microtransactions that bear resemblance to gambling, seem to gain support within the gaming community, posing challenges to prevention 149 .

Although involvement in various online communities can potentially lead to harmful behaviors and even the initiation or maintenance of addiction, it is crucial to recognise that these communities also serve as a valuable resource for their users. For instance, gamers who harness social bonds within video games often report favorable social outcomes, including support from in-game friends 150 . Online discussion forums have proven to be a valuable source of support for gamblers, especially those experiencing gambling-related problems or harms 21 . Engaging in conversations online with peers who share similar experiences provides a natural and easily accessible safe space where they can narrate experiences without the fear of judgment. Participants can openly discuss how behaviors like gambling have impacted their lives and share their current self-perceptions. Members of communities focusing on recovery actively exchange information about available resources and offer insights into how to effectively utilise online forums that aid and encourage the recovery process 21 , 139 .

Growing relevance of online communities

Online communities have growing importance in people’s lives today. We are in the middle of remarkable technological change with increasingly ubiquitous computing, which includes major leaps in the development of artificial intelligence technologies and extended realities 151 , 152 . In some visions, the metaverse is the future of the internet and the 3D model of the internet. The term has been hyped during the early 2020 s, partially so because one of the biggest technology companies, Facebook, renamed itself to Meta and envisioned a metaverse-integrated, immersive ecosystem 152 . Part of the development of the metaverse is tied to technologies and gadgets, but it is hardware independent and functions globally, also within the mobile devices we already use in 2024 152 , 153 . At this point, it is too early to say how important the metaverse will be in the forthcoming years 154 , but it is certain that online communities will play a role in any future development of the internet.

Online communities are fundamentally enabled by the human need for social relatedness 16 , 155 . Social psychological evidence has shown that group formation takes place easily in any context – also online 2 , 26 , 156 , 157 . This has been shown in both the SIDE and IBRM 4 , 37 . Characteristics of online communication are tied to the mediated nature of the communication, but, with the help of advanced technologies, the line between on- and offline has become increasingly blurred. Today’s research evidence emphasizes the increasing significance of online communities in shaping social connections within both work and everyday life. However, the full extent of this impact is challenging to predict due to the rapid development of internet and social media platforms. Going forward, social psychological theory stands as a cornerstone in understanding the intricate mechanisms of online communities. However, it is crucial to maximise its significance by integrating and considering methodologies and findings from other disciplines of psychology.

In this Perspective, we focused on online communities at work, online hate communities, and online communities based on addiction, and how they contribute to both benefits and risks of human interaction, behavior, and well-being, and what implications such communities hold for the society at large. In the context of work, online communities can facilitate efficient collaboration, knowledge transfer, and social belonging. However, virtual workplace environments may also lead to exclusion, cyberbullying, psychological distress, and technology-induced technostress. Online hate communities pose a worrisome phenomenon, spreading extremist ideas, false information, and conspiracy theories. These activities can have real-world consequences, including increased distrust in institutions and offline deviant behavior. Additionally, online communities related to addiction impact users’ time, sleep, relationships, and finances. Despite challenges, online communities offer potential for intervention and support. Research in this multidisciplinary field is urgently relevant, considering technological, societal, and psychological aspects.

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Oksanen, A., Celuch, M., Oksa, R. et al. Online communities come with real-world consequences for individuals and societies. Commun Psychol 2 , 71 (2024). https://doi.org/10.1038/s44271-024-00112-6

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Emergence of online communities: Empirical evidence and theory

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Online communities, which have become an integral part of the day-to-day life of people and organizations, exhibit much diversity in both size and activity level; some communities grow to a massive scale and thrive, whereas others remain small, and even wither. In spite of the important role of these proliferating communities, there is limited empirical evidence that identifies the dominant factors underlying their dynamics. Using data collected from seven large online platforms, we observe a relationship between online community size and its activity which generally repeats itself across platforms: First, in most platforms, three distinct activity regimes exist—one of low-activity and two of high-activity. Further, we find a sharp activity phase transition at a critical community size that marks the shift between the first and the second regime in six out of the seven online platforms. Essentially, we argue that it is around this critical size that sustainable interactive communities emerge. The third activity regime occurs above a higher characteristic size in which community activity reaches and remains at a constant and higher level. We find that there is variance in the steepness of the slope of the second regime, that leads to the third regime of saturation, but that the third regime is exhibited in six of the seven online platforms. We propose that the sharp activity phase transition and the regime structure stem from the branching property of online interactions.

Citation: Dover Y, Kelman G (2018) Emergence of online communities: Empirical evidence and theory. PLoS ONE 13(11): e0205167. https://doi.org/10.1371/journal.pone.0205167

Editor: Atte Oksanen, Tampereen Yliopisto, FINLAND

Received: December 11, 2017; Accepted: September 20, 2018; Published: November 14, 2018

Copyright: © 2018 Dover, Kelman. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: The data underlying this study is available on Figshare under the DOI: 10.6084/m9.figshare.7152386 .

Funding: This work was partially supported by the Israel Science Foundation grant no. 1124/16 to YD. The funder had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: The authors have declared that no competing interests exist.

Introduction

Peer-to-peer group interactions are prevalent in online platforms. People regularly participate in online groups and communities, interact with other members, and are affected by their peers [ 1 – 4 ]. Still, there is little empirical evidence that pins down the factors that determine whether a community will keep thriving with activity or fail to attract, or retain, active members. The extant literature discusses several factors that are important in maintaining meaningful social group action [ 5 – 11 ]. First and foremost are the number of committed group members at a given time. This is a prominent indicator of an active community, even if the commitment levels are heterogeneous [ 5 ]. The second factor is the minimal level of interdependence required between group members to induce any interaction within the community [ 12 , 13 ]. Third, the marginal returns on contribution should be non-decreasing [ 5 ]. Other factors such as group context and social network structure [ 14 ] have also been surveyed in the theoretical literature, but some studies [ 8 ] suggest that these effects are “second order.” Here, we wish to gain empirical insight into online communities, stability by investigating the relationship between activity and size. It is not immediately clear from the literature what the expected exact activity–size relationship should be, although some studies suggest that this relationship should strongly depend on the underlying production function, context, competition [ 15 , 16 ], and heterogeneity [ 5 , 11 ].

Because our exploration and data is extracted from a variety of contexts in the online world, it is important to clarify what we mean by “online community.” Note, also, that the term “community” is usually used in the context of the network analysis and community detection literature (e.g., [ 17 ]), which is not the meaning we ascribe to it here. A robust definition of the term “online community” has been the subject of heated debate and controversy, since the emergence of computer-mediated communications (see, e.g., [ 18 ] for an overview). In some instances, online communities are defined by ad hoc community-life parameters (e.g., shared interests, self-reported group affiliation), but a prevalent and more general approach in the literature is to define groups of people as communities if member-to-member interactions exist within it, i.e., that there is some sort of social network of interactions between members (e.g., [ 19 , 20 ]). A more detailed definition is suggested in [ 21 ] and [ 22 ]: “An online community is a group of people, who come together for a purpose, online, and who are governed by norms and policies and supported by software.” Notably, this definition includes weaker forms of interaction, such as interaction around specific tasks or content, which are also a subject of investigation and interest in the literature. Here, we will use the term “online community” to denote a group of people who interact digitally around a common theme, or purpose, within a relatively confined and defined context. The definitions we cite consider communities to be separate and distinct groups of people. But, our data is secondary and extracted from the online world and, therefore, we have to make a simplifying assumption. Throughout the paper, we assume that when a group of people congregates around a common theme (e.g., within a topical discussion forum or around a piece of online content), they form for this purpose an ad-hoc community that is separate from other communities on the platform. We ignore, in that sense, the fact that within and across platforms, the membership of communities may overlap. We hope that future research will tackle this distinction and explore the consequences of making this assumption.

For example, a group of users in a specific discussion forum will be considered an online community because they are a group of people who interact within the interface confines of the forum and, potentially, around its proclaimed theme. In that same spirit, authors of a Wikipedia page timeline interact with one another around the common goal of creating and maintaining the page. Commenters on a YouTube video page also, presumably, interact around the theme of the video. In some of these contexts, the social interactions may be considered weak, in others, more intensive. Generally, the communities we explored all fall under the above definition of “online communities.”

To investigate the relationship between activity and size across a variety of types of online communities, we collected and analyzed several rich datasets that contain hundreds of thousands of online communities, spanning a time frame of more than a decade. (cf. the Materials and methods section that gives detail of the data collection and processing across platforms). In what follows, we detail the patterns observed in the relationship between community activity and community size.

Activity–size phase transition and regime structure

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The thick solid line visualizes the median activity across community-size bins. The shaded areas mark the regions between the 25th to 75th percentiles.

https://doi.org/10.1371/journal.pone.0205167.g001

Regime I spans small communities of up to about 20 members. In this regime, activity is sporadic, and, across the lifetime of a community, the mean number of posts per user is low, at around two posts per user. The slope of the dependence of mean activity on community size is 0.086 (with a std. error of 0.018). Analysis shows that, in this regime, a community requires more than 12 new users just to “encourage” community members to increase their posting rate by one additional message. Thus, the effect of community size on participation is very small. A sharp transition to Regime II occurs in communities of about 20 members, and ranges up to group sizes of about 50. This type of sharp transition around a critical mass, which we denote here by N crit , is theorized in the literature [ 23 ], but empirical evidence for actual relevant examples are scarce. Below, in the Model section, we explain how this sharp transition likely results from the branching property of discussion trees. In short, activity within an online community is essentially a collection of messages embedded within interaction (discussion) trees.

These trees of interaction can either grow multiplicatively, if there is a minimal number of members willing to interact, or they can remain shallow and limited if the number of members is small and the members are non-responsive. Consequently, the trees’ branching property creates a situation in which small variations around a critical community size, will result in a dramatic difference between a regime with rare interactions and one in which interactions are abundant such that discussion trees can grow exponentially. The activity–size slope within Regime II is 0.91 (SE 0.085), meaning that it only takes one additional user to the community to be associated with an increase of one additional message to the posting rate of the typical user. This effect is an order of magnitude greater than in Regime I . In other words, a meaningful effect of community size on participation emerges in Regime II . The transition into a third regime, Regime III , takes place in communities that roughly number 50 members or more. Here, like in Regime I , the slope is very small (0.022, SE 0.002). It seems, therefore, that there is a cap on community effects above a certain size, which we denote by N max . In the Model section, we estimate N max and N crit among other parameters.

Notably, Fig 1 summarizes the dynamics of communities over their entire lifetime. A lifetime in these data can span a few weeks or a few years, depending on activity levels and the time point at which activity ceases. In order to rule out a scenario where the three-regime structure is an artifact of some complex long-term dynamics, Fig 2 shows the same empirical dependency, but for varying community lifetime stages. Communities were divided into four groups of lifetime brackets so that the sample sizes in each group are large enough to exhibit a full size–activity curve. Very young communities, of up to three months of activity, are denoted by the black dotted curve, mid-life communities are denoted by the blue dashed curve (three to six months) or solid green curve (six to twelve months), and the oldest communities which have existed for more than a year, are denoted by a solid red line. Notably, communities that are three months or older exhibit a distinct three-regime pattern. While mid-life communities show a high slope in Regime II , similar to that exhibited in Fig 1 , the oldest communities show an even-steeper slope. On the other hand, for very young communities the sloping transition from Regime I to Regime II and Regime III is smoother. Our interpretation of this is that even though the regime structure exists throughout an online community’s life cycles, the older the community is, the stronger the phase activity–size phase transition. While we can only speculate, we assume that a possible explanation is that, for various reasons discussion trees occur more efficiently in older communities; therefore, the phase transition between Regimes I and II is more pronounced. While this development explanation may be appealing in the case of the TAP dataset, we do not see consistent patterns in other data (see S1 File for more detail).

(a) younger than 3 months (black dotted curve), (b) between three and six months (blue dashed curve), (c) between six months and a year (green solid line) and (d) more than a year (red solid line).

https://doi.org/10.1371/journal.pone.0205167.g002

Another concern is that Regime II may be a spurious result of, in fact, two regimes, I and III, between which there is a discontinuous jump, but with heterogeneous critical points. First, this scenario is not consistent with the results shown below, in which all three regimes are exhibited even for narrow ranges of critical points. Second, when inspecting the distribution of jumps in the activity of communities, it turns out that only 8.6% of them exhibit a discontinuous jump in their mean activity of > 10 posts per member, and only 2.9% of communities show a jump of activity that is higher than 20 posts per member. In contrast, the gap between Regime I and Regime III (e.g., in Fig 1 ) is larger than 20. Therefore, it is not probable that the observed Regime II is spurious, i.e., a result of heterogeneity of critical points.

Activity–size phase transition is also exhibited in discussion trees structure

Online peer-to-peer interactions can be thought of as trees of messages and replies (see for example, [ 24 ]). In these trees, messages are nodes and are connected by links that represent which message was addressed as a reply to which other previous message. This discussion tree begins with an initial seed message posted by a user. Other users can then post a reply to the seed message, i.e., link their messages to the initial one, creating a two-level tree. This tree can branch out further with replies to the replies at deeper levels, and so on. As we outline in the Model section, the rate of growth of a discussion tree depends solely on the distribution of the number of offspring, or replies to each message. If the mean number of replies per message is higher than one, the tree grows multiplicatively. If the mean number of replies is lower than one, the growth of the tree effectively decays geometrically. Therefore, we expect a sharp phase transition of discussion tree sizes, or number of messages, to occur as the mean response rate increases linearly within a community. We argue that the phase transition observed in Fig 1 , across communities, stems from this branching property of peer-to-peer discussion trees. As a community grows in size, response rates also grow, and above the critical point of one reply per post, a sharp phase transition of multiplicative tree growth occurs. Evidence that this is indeed the actual scenario can be seen in Fig 3 . This figure sketches typical discussion trees sampled from the data and arranged by community size. Each displayed tree is representative of the median depth at the given community size in the TAP dataset. The figure shows that the activity phase transition between Regime I and Regime II is strongly correlated with a sharp increase of tree depth. The mean response rate ( q ⋅ N ) at a given community size, is also displayed in Fig 3 as the color of the illustrated tree. Consistent with our theory (see Model section), the figure does show that the transition occurs around the offspring rate of unity.

The nodes mark response posts and the root is the initial “seed” post. Links between nodes are the association of post and reply. The illustrated trees are color-coded according to the mean rate of replies to messages (mean offspring rate) within each community size tier.

https://doi.org/10.1371/journal.pone.0205167.g003

The relationship between responsiveness and critical community size

The inset shows, for a ten-fold partition of q values (deciles) the N crit as a function of within-decile q .

https://doi.org/10.1371/journal.pone.0205167.g004

Activity–size patterns across platforms: Do they share common features?

To test whether the three-regime pattern is unique to the TAP platform or is a more general phenomenon, we collected data from six additional online platforms. For consistency, we chose platforms that enable users to post messages and replies within distinct predefined communities (see the Methods section). The activity–size profiles for the additional platforms are laid out in Fig 5 and in S1 File .

(a) Boards.ie (BRDS), (b) YouTube (YOUT), (c) Wikipedia (WIKI) and (d) Goodreads (GOODR). For visualizations of other platforms see Figs A and B in S1 File .

https://doi.org/10.1371/journal.pone.0205167.g005

Next, Panel Fig 5(c) shows user discussions in a random collection of 21,000 Wikipedia talk pages. Here, a main difference of this case from the others is that we do not observe a third regime. A possible explanation is that, in this platform, community sizes are typically small [ 25 ], and substitution of members often supersedes growth. For our data, size does not exceed 80 users per community, and the median is N authors < 10, so it may be the case that communities large enough to exhibit activity saturation, do not exist. The transition between Regimes I and II occurs in communities consisting of about 3–4 authors, suggesting high responsiveness. Lastly, Panel Fig 5(d) shows the activity profile of 8,040 Goodreads discussion communities randomly collected and analyzed. Although Regimes II and III are distinguishable in this panel, Regime I is missing. This is a possibility that is also captured in our tree discussion model. It occurs in the case of high levels of responsiveness. If responsiveness is high enough, the critical point will be so low that Regime I will be too small to be observed. In general, it is important to note that our branching model, outlined in the next section, inherently accommodates the scenario in which Regime I or Regime III are not observed. Low values for critical size are expected for tightly-knit or highly interactive communities in which the responsiveness is high. The occurrence of Regime III depends on whether communities reach sizes of the order of N max and grow beyond them. If, however, we only observe communities smaller than N max , Regime III will not be observed, as we suspect is the case for the Wikipedia talk pages.

In summary, some aspects of the empirical patterns repeat across platforms, i.e., the Regime I -to- II phase transition and the existence of the third regime. With others, we exhibit higher variance across platforms, e.g., the slope of Regime II and the activity levels within Regime III . The full three-regime structure is observed in five of the seven platforms: TAP, BRDS, YOUT, RED, and HI5. In the other two platforms, WIKI and GOODR, we only observe two regimes. We speculate that these differences stem from differing platform contexts and sampling constraints. To also test whether the existence of the three regimes depends on community lifetime stage in other platforms, Figs I and J in S1 File are reconstructions of Fig 2 for YOUT and BRDS. They too show that the existence of the three-regime pattern does not depend much on community life-cycle stage, across platforms. We hope that future research will explore the effect of platform context on the activity–size relationship further.

Online communities as a collection of branching discussion trees

We use a branching process model [ 26 ] to explain the observed activity–size patterns in which the three-regime structure is exhibited. A variety of generative models of online discussions have been suggested in the past. Some of these models assume that a preferential-attachment like growth is at the base of the growth of discussion trees. Another common factor in these works is the “age” of a message on the discussion tree. The longer the time that has passed since a message is posted, the lower the probability that it will receive responses. Time could either be discrete [ 24 ] or continuous [ 27 ]. Other works also refine and put additional behavioral traits to capture more realistic aspects of real-life discussion trees. For example, the root-bias: the tendency of the root post to attract more responses than its leaves [ 28 ] or reciprocity between users [ 29 ]. This behavioral trait is well-established in the digital world. Further works also model social influence and the effect of various roles of users (for a comprehensive review, see [ 30 ]).

Here, our focus is on theorizing and modeling the three-regime structure that naturally arises when users interact and generate joint discussion trees. Our unit of analysis is the community of users. In our model, a community consists of N interacting members that generate trees of messages and their responses.

Where Z 0, m = 1 initially because discussion trees initiate with one message. Now, we denote by Γ = Γ( s , g max | N , q , Φ( κ )) the probability to observe a tree of size s and maximal depth g max .

Phase transition of discussion trees

Eq (4) demonstrates that the mean size of the tree is “geometrically sensitive” to the first moment of the distribution of replies, μ ( N , q ). The critical point of growth occurs for μ ( N , q ) = 1. If μ ( N , q ) > 1, a super-critical branching process is in effect, and so tree posts will geometrically grow across generations. On the other hand, in the sub-critical case, μ ( N , q ) < 1, the expected number of replies shrinks geometrically. This is a known property of branching trees [ 26 ].

An approximation for the critical community size

Essentially, ( 6 ) shows that the critical size of a community is determined, in this simple scenario, by the level of responsiveness. The higher the inherent responsiveness of the community is, the lower the critical threshold.

Estimating the model

We use a Maximum Likelihood Estimator (MLE) to test several configurations of our model and find the one that shows the best fit to the data. We use the TAP data, where we have the best user- and message-level resolution (see S1 File for complete details). Table 1 lists the four models that we test. The MLE, in practice, renders a statistical estimation of the offspring distribution parameters Φ( κ | N , Q ( g )), namely, the responsiveness paramater, q , N max , and a third parameter, λ, that is used to model the decline of response probability with increasing tree depth. For simplicity, we choose the Binomial distribution as the basis of the offspring distribution. For robustness, in S1 File , we also present estimations using two other distributions: Poisson and Negative Binomial. These estimations show that the results of the Binomial distribution are mostly replicative (see S1 File for details). Per each observation, in order to estimate the parameters, we use the observed number of offspring and the number of users that were active, N . To reduce noise, in each observation, we count the number of users active in the community within a time window of three months, centered around the observation time. The results are qualitatively similar for a variety of time windows choices. We test four configurations of our model, from a simple single-parameter configuration to a full-featured three-parameter one. Model 1 estimates only the responsiveness, q , i.e., a model that does not include a third regime or tree-depth dependent decay. Model 2, on the other hand, incorporates the third regime, but not the tree-depth decay. Finally, Models 3 and 4 include both the third regime and the tree-depth decay. The difference between Models 3 and 4 is the functional form of the tree-depth decay, namely, an exponential decay or power-law decay, respectively.

https://doi.org/10.1371/journal.pone.0205167.t001

For robustness, we also estimated the set of models for two more random samples (see Tables A and B in S1 File ). Interestingly, the model showing the lowest information criterion is Model 4, our full model, which includes a power law decay of the probability of response. Model 4 was the best fit model also across all our robustness checks (see S1 File ). Exponential decay seems to produce a worse fit, across the board, also supported by an instability of parameter values in the robustness checks. Adding the third regime ( N max ) in the model increases the fit considerably. Including the tree-depth decay in the model (λ) also increases the fit.

The model fit is shown in red and the respective percentile envelope overlay the data, as in 1 .

https://doi.org/10.1371/journal.pone.0205167.g006

https://doi.org/10.1371/journal.pone.0205167.t002

To test whether the goodness of fit replicates with the other data sets, we conducted similar estimations for the rest of our data sets. The full detail of the procedure and the results for each data set is given in S1 File . In general, the fit to Model 4 ( Table 1 ) is signifiin cantly better than the benchmark. Notably, while the model fits TAPUZ, YOUT, BRDS, GOODR and HI5 quite well (see S1 File ), it does provide a lesser fit to both the WIKI and RED data sets. Still, we argue that these findings establish the fact that even though the phase-transition patterns are not strictly universal, the model is useful to account for the activity-size patterns across most of the platforms we investigated.

Conclusions

Our findings provide insight into the factors that underlie the emergence and sustainability of online communities. We find that the relationship between activity levels and size in these communities exhibits a three-regime pattern that mostly repeats across platforms and time, with exceptions. Further, we observe a sharp transition between two of the regimes and evidence for the existence of a critical community size. Below that critical size, member activity is largely uncorrelated, and so activity remains low and sporadic. Above that critical size, member activity becomes increasingly correlated, and an interactive community emerges. We argue that the regimes’ structure and this sharp transition can be explained by a dynamic model of peer-to-peer communications that generate trees of interactions. The model explains the sharp transition as the result of the multiplicative nature of the interaction trees. Above a certain member-to-member reply rate, trees grow geometrically. In the context of online communities, the geometric growth results from an interplay between community size and the ambient level of responsiveness. The characteristic scale of the critical point of growth, is determined by the level of member-to-member responsiveness in a community. The higher the responsiveness, the lower the critical point which defines the size above which interactions boom in the community.

A limitation of our findings is that we only observe correlations and are not able, in this non-experimental context, to demonstrate that size actually causes the transition between regimes. Having said that, we find that the model fits the data well (e.g., in Fig 6 ), in spite of its relative simplicity, including the observed regimes and the sharp transition patterns. Further, throughout the paper, we present indirect and corroborating evidence for the suggested theory. Future work could investigate more complex forms of the model, such as we reviewed in the model section, and the implications of some of our simplifying assumptions. Another limitation is that we do not model the differences across platforms which are, presumably, the underlying reasons for the differences in empirical patterns. The question of which specific difference between platforms can explain the the empirical observation differences is an opportunity for more further research. Furthermore, our paper contributes to the computational social science literature. While sharp phase transitions in social systems were hypothesized, mainly by theory [ 23 ], the empirical evidence to support these conceptualizations was, so far, lacking. Here, we are among the first to present direct evidence for sharp transitions of collective social behavior. Finally, it is known that within communities, there exists heterogeneity of contribution [ 5 ]. This heterogeneity most likely affects the propensity of a community to thrive or fail. Further research should empirically investigate the sources and outcomes of contribution heterogeneity within online communities.

Materials and methods

Online discussion groups are constantly being created and maintained by members in designated online platforms. Generally, a discussion topic initiates with a single message posted by a user on the platform. Other users can post replies to that message or to the following messages such that a tree of posts and replies develops. We collected time-stamped group discussion comments at random from context-free platforms such as Tapuz, Goodreads, hi5.com, boards.ie, YouTube, and focal group websites like the Wikipedia article talk pages or the technologically oriented Reddit. For each dataset:

• Data were scraped from publicly available online web pages or online services.
• Datasets included a time stamp per each comment/post that was posted on the respective platform, the unique user id of the user who posted it, the specific community that the comment was posted on, and where available which comment the focal comment replied to.
• In some cases, if the exact network of posts and replies was required, the records were processed to locate parent–child (directional) links between pairs of comments. In some platforms (e.g., Tapuz), the structure of a discussion page is such that users can choose to respond to a certain post and create a clear thread where each “child” is directly connected to their “parent” response. In other platforms, the child–parent relationship is approximated by either marking the immediate following message, or better still, a user from a preceding comment (the parent comment) may be referenced using hash symbols, similar to the re-tweet mechanism in Twitter. Some platforms (e.g., MediaWiki) may further convert these name mentions to user-page links.

The full data then include the time–stamped records of posts and replies that collectively thread into a tree graph of discussions that has measurable depth (maximal thread path length), volume (number of comments), breadth (number of leaves), community size (number of unique participants), and activity level (number of comments per time unit).

In terms of ensuring that data sampling methods are consistent across platforms, in order to rule out sample selection and to create consistent data formats, our general rule was the following; We sampled from each platform community either by collection of all complete activity within a given and lengthy (several years) period time slice where possible, or when collecting the complete data was prohibitive, to collect a large sample of communities at random. Per each community, of course, all activity was collected up to the time of collection. For example, for the Tapuz data ( www.tapuz.co.il/communa ), we collected all “communes” (user-generated discussion forums) that were active between the years 2004 and 2016. For the case of Wikipedia, we used the “random page” function in MediaWiki to sample 21,000 pages. In hi5.com, all the discussion topics from 2009 to 2016 were collected. Similarly, we downloaded YouTube video page data using a third-party tool ( www.npmjs.com/package/youtube-random-video and tools.digitalmethods.net/netvizz/youtube/mod_video_info.php ) to sample 10,000 videos at random. Table 3 provides a descriptive summary of the collected data. The empirical analyses performed on the data are explained in detail within the text or in S1 File (e.g., the Maximum Likelihood Estimation).

https://doi.org/10.1371/journal.pone.0205167.t003

Supporting information

S1 file. supporting information file..

Contains More patterns of activity vs. size, discussions on the dependence of discussion tree growth on its depth, the maximum likelihood estimations and fit (description of the procedure), and robustness checks of the MLE.

https://doi.org/10.1371/journal.pone.0205167.s001

Acknowledgments

We wish to thank Daniel Shapira, Jacob Goldenberg, and Miki Assaf for fruitful discussions.

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Home Communities

What is Online Community: Definition, Methods and Advantages

Community research is becoming increasingly popular for carrying out market research, especially since companies have discovered how to use the collected data in the world of marketing. Online community research is a relatively new research technique. In this article, we will introduce you to online community research techniques, as well as all details about creative implementation into practice.

We believe that this short read will help you understand the essence of online community research and allow you to have a better understanding of the possibilities you can get from it.

What is Online Community Research?

Online community research is a community deployed online to carry out research using various market research methods . Online community research is a “buzz” that has changed the face of research to a larger extent.

The GreenBook Research Industry Trends report has shown that online research communities have been the most successful and most widely adopted new research methodology for the last few years. This speaks volumes about their importance in the research space and the vast insights that researchers get from them. We are now in a situation where most of the top companies in each industry, from banking to CPG, use and get valuable data from online research communities.

Don’t miss out on the only market research tool you’ll need and request a free demo: Online Community Software

Three Reasons Why Online Research Communities Are Dominating the Research World

As you can see, these types of communities are taking over the world. But why? Let’s take a closer look at the 3 main reasons that have led to the widespread adoption of online community research.

Online Research Community gives Access to Customers.

Online research communities give a company long-term access to its customers’ thoughts, insights, and feelings. In addition, companies can get more profiling information about their customers than they could in any CRM system. The customers are giving this additional information because they are a part of this community. In addition, these companies have instant access to their customers, and they can conduct research very quickly and get their results in less time as well. Furthermore, the additional tools available, like Ideation or Discussions, allow for even more insights and ideas from customers.

Engagement Through Collaboration

With the additional tools available, like the Discussion or Idea Board module, the community sees higher engagement levels and collaboration between the community members. This helps to tease out even more insights as you now have members collaborating and offering their opinions and feelings, so you get really good organic conversation and discovery as well. This allows the community manager to assess better the popularity or viability of the idea or opinion when you have their more qualitative tools available. A side benefit is those insights are gained from questions or ideas that companies never thought to ask their customers in many cases.

Adoption of Social Media Networks

The rise and widespread adoption of social media networks have likely been the most significant reason online research communities dominate. Essentially, social media networks like Facebook and Twitter are a community where people can come together to connect and share their feelings and opinions. Their voice can be heard on these platforms. Perhaps more importantly, they can connect with like-minded people and those that share similar interests. This undoubtedly has helped online research communities in that people are familiar with and like the concept of being in a community. They are used to participating in polls , posting topics, and commenting on other posts. It’s a positive experience and one that has a high level of intrinsic value.

Benefits and types of an Online Community Research

Over the past few years, QuestionPro has worked closely with organizations such as Zynga , Potbelly, McGraw Hill, and Gannett to create and grow successful customer insight communities. A customer insight community web portal is a stratified sampling panel of customers with the objective of gaining input from community research members to aid in both strategy and future decision-making.

Stratified: All organizations have some sort of segmentation/stratification – high-value customers versus low-value customers. Understanding the perceptions of your high-value clients versus your low-value clients is paramount for effective decision-making.

Ease of Access and Delivery: The ability to access community members rapidly and seamlessly. The community is ready to go anytime, and members have digital access (online member portal, email, SMS, etc.). As community members, they have consented to receive surveys and notifications and agree to participate in digital focus groups and usability testing scenarios.

Self-Service and Digital: Customer communities run predominantly on a digital platform. Members have a member portal where they can log in, take surveys and quick online polls , and participate in ad-hoc real-time focus groups. In addition, there are more advanced qualitative tools like IdeaBoard. Members can post ideas and vote on each other’s ideas without the involvement of the organization representative, which gives the system scalability.

Mobile Focused: Members have real-time and mobile-optimized access to the research platform. In addition, there is a mobile app that members can download and participate in research . The app has all the tools – surveys, polls, online discussions, IdeaBoard, etc.

Internal Team Access: Multiple people within the organization should have direct access to the platform for conducting research. This should be self-service or a controlled service where community managers are enabling researchers to run ad-hoc surveys within hours, not days. This is an extremely important characteristic; without democratizing access, organizations will not be able to gain the efficiency they want. Think of a customer insight community as a productive tool – insights and access to data are no different than productivity tools such as Excel, Word, and PowerPoint.

Online Community Research Methods

• Integrated Survey Research & Community Platform

We’ve witnessed tremendous advantage and success with companies with an integrated system/platform for their research and panel/community needs. It is imperative for survey research platforms to be able to “communicate” with the community platform seamlessly and share information back and forth. Having an integrated Community and Survey Research platform reduces risks and also allows for smooth transfer of data, and enables more scenarios to be executed.

Learn more: Community survey software platform

• Quick Polls & Engagement

One of the many advantages of having a community platform for insights is the ability to capture insights from panelists on an ad hoc basis with low operational overhead. Quick Polls, or single question polls , are a great way to keep the community engaged while at the same time providing valuable insights and benefits to the decision-making processes of an organization.

5 Practical Steps to Democratizing Decision Making with Online Community Research

• Identify and group the “class” of customers you want to represent. In some cases, it may make sense to have different communities for each of the cohorts of customers you have. In many cases, it may only make sense to group your high/value customers into a panel , which creates focus and forces the company to listen to their high-value customers.
• Setting up a community on the QuestionPro Community platform is really simple and can be up and running in under an hour! Work on the marketing aspects and design aspects of the messaging. Do you call it a customer advisory board? Come up with a catchy name for the community and one that is in line with your company’s branding.
• Invite and encourage your customers to participate and join the panel. Email invitations and web intercepts are common ways for you to bring customers into the community. In addition, customer lists, social media, and member referrals are some more great ways to get a member into your insights community.
• Enable access and training to key decision support individuals in the organization on the research platform. This would give everyone exposure to how to create and deploy a survey to the community in a short amount of time. In many cases, the research teams can act as best-practice advisors and gatekeepers of the insight community software. We recommend that this practice is followed if survey researchers are not versed with a panel/community platform management platform.
• Train and showcase non-survey-related research programs such as online focus groups, chat rooms, and qualitative research modules. Not all decisions need to be based on a survey; for example, focus groups and one-on-one conversations yield equally effective results based on the problem and task at hand.

You might be interested in QuestionPro’s Online Communities Software: Q&A’s .

Advantages of Online Community Research

• Technology Driven: Online community research allows members to participate in using hand-held devices like tablets, Ipads, mobile phones, and similar devices. They can share their opinions and insights from anywhere, anytime, using any convenient device. Since online community research is technology-driven, it is an advantage for researchers. For example, consider hypothetically, you are a researcher, and you want to collect insights from the participants about a specific product, and you wish to have real-time insights; it is possible with online community research, where you can ask the participants to go into the store, check the product on the aisle and give their insights right from within the store. Give the participants a link and ask them for their feedback.
• Targeting Millennials: Although not all studies or research is focused on or involves millennials, some researchers focus specifically on them. Since millennials are well-versed in technology, it is easy for them to respond online. They can easily engage and respond quickly online. Moreover, the natural habit of millennials is they think digital.
• Can Engage Multiple Segments: Online community research allows a researcher to engage 50-150 participants across 4-5 different segments at one given point in time in one study. So, once you have completed your online community research, you can compare opinions and ideas instantly.
• More Participants, Better Insights: A single online community research can provide more insight than five focus groups combined together. While engaging 100+ people are practically impossible in a focus group , online community research can easily accommodate 100-150 participants. A good number of participants mean more and better data.

More than just software, Online Community Software is a powerful platform that enables you to analyze market performance through accurate data collection and customer interaction. Turn data into valuable insights and make better business decisions!

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Researching Within Online Communities

• First Online: 03 January 2020

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• Susannah Oddi 4  

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This chapter examines how a flexible approach to researching within online social communities may provide for a connected and creative research journey. Social media platforms are low cost and accessible tools that afford global networking potential and can engage wider audiences for research findings. Digital platforms annul temporal and geographical boundaries and allow for productive meanderings with readers. For sole researchers, research teams, or as forms of data collection, social media and other online platforms can be a useful digital research tool; however, they can require careful navigation. Be it within Facebook, Twitter, a niche online community, or a blog; the role of researcher and participant is dynamic, and quantitative and qualitative data provides direct feedback. Designing effective methodologies for digital community research requires an interdisciplinary approach that recognises ethical and practical considerations ranging from consent, privacy, moderation and potential incivility. This chapter will map, using a reflective case study, practical strategies for minimising risk and managing methodologies. Also discussed will be how ongoing reflection on online relationships can shape the research process and final work, how evolving digital communities can expand traditional academic forums, and some current trends in open-access publishing.

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Oddi, S. (2019). Researching Within Online Communities. In: Brien, D.L., Batty, C., Ellison, E., Owens, A. (eds) The Doctoral Experience. Palgrave Macmillan, Cham. https://doi.org/10.1007/978-3-030-18199-4_12

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10 Studying Populations of Online Communities

Benjamin Mako Hill is an Assistant Professor at the Department of Communication in the University of Washington, Seattle, WA.

Aaron Shaw is an Assistant Professor in the Department of Communication Studies at Northwestern University, Evanston, IL.

• Published: 04 September 2019
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While the large majority of published research on online communities consists of analyses conducted entirely within individual communities, this chapter argues for a population-based approach, in which researchers study groups of similar communities. For example, although there have been thousands of papers published about Wikipedia, a population-based approach might compare all wikis on a particular topic. Using examples from published empirical studies, the chapter describes five key benefits of this approach. First, it argues that population-level research increases the generalizability of findings. Next, it describes four processes and dynamics that are only possible to study using populations: community-level variables, information diffusion processes across communities, ecological dynamics, and multilevel community processes. The chapter concludes with a discussion of a series of limitations and challenges.

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Numbers, Facts and Trends Shaping Your World

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• Online Communities

Table of Contents

• Part 1: Background
• Part 2: The Internet, Communities, and the Virtual “Third Place”
• Part 3: New Community Participants
• Part 4: The Differences Among Online Group Members
• Part 5: The Internet and the Local Scene
• Methodology

The vibrant social universe online

In recent years, there has been concern about the social impact of the Internet on several levels. One major worry was that use of the Internet would prompt people to withdraw from social engagement and become isolated, depressed, and alienated.  A related fear was that Internet users might abandon contact with their local communities as they discovered how easy it is to go online to communicate with those in other parts of the world and get information from every point on the planet.

We surveyed 1,697 Internet users in January and February to explore the breadth and depth of community online. Our findings suggest that the online world is a vibrant social universe where many Internet users enjoy serious and satisfying contact with online communities. These online groups are made up of those who share passions, beliefs, hobbies, or lifestyles. Tens of millions of Americans have joined communities after discovering them online. And many are using the Internet to join and participate in longstanding, traditional groups such as professional and trade associations.  All in all, 84% of Internet users have at one time or another contacted an online group.

The pull of online communities in the aftermath of the September 11 attacks shows how Americans have integrated online communities into their lives.  In the days following the attacks, 33% of American Internet users read or posted material in chat rooms, bulletin boards, or other online forums.  Although many early posts reflected outrage at the events, online discussions soon migrated to grieving, discussion and debate on how to respond, and information queries about the suspects and those who sponsored them.  With the dramatic displays of community spirit around the country following September 11, there are hopes that Americans’ repulsion and shock the attacks might have sparked a renewal of civic spirit in the United States.  The existing vibrancy of online communities profiled in this report suggests that Internet groups can play a supporting role in any enduring boon to community life in the aftermath of the attacks.

Our winter survey also showed that many Americans are using the Internet to intensify their connection to their local community. They employ email to plan church meetings, arrange neighborhood gatherings, and petition local politicians. They use the Web to find out about local merchants, get community news, and check out area fraternal organizations. Moreover, there is evidence that this kind of community engagement is particularly appealing to young adults.

Sociologist Barry Wellman argues that many new social arrangements are being formed through “glocalization” – the capacity of the Internet to expand users’ social worlds to faraway people and simultaneously to bind them more deeply to the place where they live. This report illustrates how widely “glocalization” is occurring. The Internet helps many people find others who share their interests no matter how distant they are, and it also helps them increase their contact with groups and people they already know and it helps them feel more connected to them.

90 million Americans have participated in online groups

• 84% of Internet users, or about 90 million Americans, say they have used the Internet to contact or get information from a group. We call them “Cyber Groupies.”
• 79% of Cyber Groupies identify at least one particular group with which they stay in regular contact.
• 49% of Cyber Groupies say the Internet has helped them connect with groups or people who share their interests.
• Cyber Groupies try out different groups; the average Cyber Groupie has contacted four online groups at one time or another.

Use of the Internet often prompts Americans to join groups. More than half of Cyber Groupies (56%) say they joined an online group after they began communicating with it over the Internet. This includes those who joined traditional groups whose existence predated the Internet, such as professional or fraternal groups. In other words, Internet access is helping people join all kinds of communities, including those that are not exclusively virtual communities.

• 40% of Cyber Groupies say the Internet has helped them become more involved with groups to which they already belong.

28 million have used the Internet to deepen their ties to their local communities

In addition to helping users participate in communities of interest that often have no geographical boundaries the Internet is a tool for those who are involved with local groups, particularly church groups.

• 26% of Internet users have employed the Internet to contact or get information about local groups. That comes to 28 million people.

Virtual third places

In the face of widespread worries that community activity is ebbing in the United States, these findings demonstrate that the Internet, while not necessarily turning the tide, has become an important new tool to connect people with shared interests globally and locally. In some ways, online communities have become virtual third places for people because they are different places from home and work. These places allow people either to hang out with others or more actively engage with professional associations, hobby groups, religious organizations, or sports leagues.

Online communities foster chatter and connection

These groups are lively online communities. People exchange emails, hash out issues, find out about group activities, and meet face-to-face as a result of online communities. Approximately 23 million Americans are very active in online communities, meaning that they email their principle online group several times a week.

• 60% of Cyber Groupies say they use email to communicate with the group; of these emailers 43% email the group several times a week.
• 33% of the 28 million Local Groupies who use email send email to their main local organization several times a week.

More contact with different pople

Many Cyber Groupies and Local Groupies say that online communities have spurred connections to strangers and to people of different racial, ethnic, and economic backgrounds.

• 50% of Cyber Groupies say that participation in an online community has helped them get to know people they otherwise would not have met.
• 35% of Local Groupies say that participation in an online community has helped them get to know people they otherwise would not have met. This lower number relative to Cyber Groupies may be due to the fact that Local Groupies probably were acquainted already with members of the online group.
• 37% of Cyber Groupies say the Internet has helped them connect with people of different ages or generations.
• 27% of Cyber Groupies say the Internet has helped them connect with people from different racial, ethnic, or economic backgrounds.

The types of connections people establish depend on the kind of group to which they belong.  Members of some cyber groups go to their groups to establish personal relationships, while others just want to keep up with group news and activities.

• Members of belief groups, ethnic online groups, and especially online groups oriented to lifestyle issues are most interested in using the Internet to establish personal relationships.
• Members of entertainment, professional, and sports online groups tend to use email in group activities less often than those who belong to other kinds. They focus their online activities on getting information about popular culture.
• Men tend to be drawn to online groups involving professional activities, politics, and sports.
• Women tend to be drawn to online medical support groups, local community associations that are online, and cyber groups relating to entertainment.

Joiners of online groups differ from those who belonged to the group prior to participating in it via the Internet

There are differences between those who have used the Internet to join a group and those who use the Internet to participate in groups to which they already belong. Many who join online groups are relative newcomers to the Internet. They tend to be urban dwellers, young adults, and less well-educated than the typical Internet user. As a cohort they are more ethnically diverse than other Internet users, and more likely to be interested in online groups relating to fun activities.

The 56% of Cyber Groupies who joined a group after having first contacted it through the Internet have very different tastes in online groups than the “Long-timers” who belonged to the group before engaging with it online. Joiners of Cyber Groups identify hobby groups as the online community that they contact most, followed closely by trade or professional associations. A significant number of joiners also say they contact online fan group of an entertainer or TV show. In contrast, Long-timers are most likely to say they are most closely in touch with trade or professional groups online.

At the local level, Long-timers are anchored in faith-based and community groups, while the joiners—who make up 20% of the Local Groupie population—show a greater tendency toward groups devoted to sports or with an explicitly social orientation.

Net Joiners of local groups are  demographically diverse. They also tend to be highly experienced Internet users. This suggests that the Internet use is drawing new and different kinds of people to local groups.  Once people have found local groups online and joined them, they report high levels of community involvement.

Civic involvement by the young

These differences among Joiners—particularly their relative youth, newness to the Internet, and racial diversity—suggests that the Internet may be drawing a segment of the population to community engagement who have not been very tied to civic activities. Political scientist Robert Putnam has argued that one major reason for the decline in civic engagement in the United States is the reluctance among younger people to participate in community groups. 1 Our findings indicate that many young people are turning to the Internet as an outlet for community activity.  Although young people tend to focus on online groups that involve hobbies, they also are much more likely than other users to report that the Internet has helped them become more involved organizations in their community and connect with people of different generations, economic backgrounds, and ethnic groups.  In other words, the primary draw to online communities for young people appears to be hobby groups; however, a secondary outcome, as young people surf to other online communities, is to connect many to groups that help foster civic engagement.

The Internet’s role in local engagement

At the local level, people use the Internet mainly as an information utility to find out about local merchants and community activities. The Internet’s role in public deliberation is modest. Public access to the Internet is only moderately available throughout the United States.

• 41% Internet users say that they “often” or “sometimes” go online to seek out information about local stores or merchants.
• 35% of Internet users “often” or “sometimes” go online for news about their local community or to find out about community events.
• 30% go online “often” or “sometimes” for information about local government.
• 24% go online “often” or “sometimes” to get information about local schools.
• 13% of Internet users say that they “often” or “sometimes” email public officials. This low rate may be because only half of all Internet users say their town has a Web site, and few Internet users find the town’s Web site very useful.
• 11% of Internet users say that they are aware of at least one local issue in which the Internet played a role in organizing citizens to communicate with public officials. However, this percentage doubles to 22% for Internet users who are active members of online communities.
• 51% percent of all Americans know of a place in their community where the Internet is publicly available. Overwhelmingly, these places are public libraries. African-Americans are the most likely to say that their community lacks public access to the Internet; 42% of African-Americans say their community does not have publicly available Internet terminals somewhere, compared with 29% of whites and 33% of Hispanics.
• Robert D. Putnam, Bowling Alone: The Collapse and Revival of American Community . New York: Simon & Schuster, 2000, pp. 33-35. ↩

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