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• CAREER FEATURE
• 01 April 2024

How scientists are making the most of Reddit

• Hannah Docter-Loeb 0

Hannah Docter-Loeb is a freelance writer in Washington DC.

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It has been almost 18 months since Elon Musk purchased Twitter, now known as X. Since the tech mogul took ownership, in October 2022, the number of daily active users of the platform’s mobile app has fallen by around 15%, and in April 2023 the company cut its workforce by 80%. Thousands of scientists are reducing the time they spend on the platform ( Nature 613 , 19–21; 2023 ). Some have gravitated towards newer social-media alternatives, such as Mastodon and Bluesky. But others are finding a home on a system that pre-dates Twitter: Reddit.

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Nature 628 , 221-223 (2024)

doi: https://doi.org/10.1038/d41586-024-00906-y

Fiesler, C., Zimmer, M., Proferes, N., Gilbert, S. & Jones, N. Proc. ACM Hum. Comp. Interact. 8 , 5 (2024).

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Proferes, N., Jones, N., Gilbert, S., Fiesler, C. & Zimmer, M. Soc. Media Soc . https://doi.org/10.1177/20563051211019004 (2021).

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Reddit TOS and API

• Reddit's TOS The Reddit TOS is more permissive of research use than Meta's platforms, partially by omission. It does not directly address research on Reddit, but it does allow for automated capturing of posts via the API.
• Reddit's API documentation The Reddit API exposes most of the site's content to automated collection. Some rules are linked on this page as well, which are fairly straightforward and permissive to research uses.

Reddit's TOS and API rules do not contain the sort of blanket bans on automated data collection that Meta's TOSes do, but they do not contain any specific provisions for research use either. Like Twitter, the API helps collect posts as they happen rather than archiving all posts on the site.

Tools for Reddit research

• Netlytic Netlytic is a browser-based social media research tool that has text mining and network visualization features. Works with Twitter, YouTube, RSS feeds, and Reddit. Free accounts are sufficient for most student purposes. Netlytic has a YouTube channel with demonstrations for a variety of types of project.
• Mozdeh Mozdeh is a social media quantitative analysis FOSS software that can also collect tweets, like Netlytic or Chorus. It works with the same things as Netlyltic: Tweets, YouTube comments, Reddit comments, and manually imported data. Unlike Netlytic, it is a desktop app. It also has a YouTube channel where you can find guides to collecting and analyzing data.
• Reaper Reaper, built on the socialreaper Python library, is a desktop app with no coding required. While it calls what it does "scraping", it makes use of site APIs and the user will need to register for an API key for any site they want to use Reaper on. This includes Facebook, Twitter, Reddit, YouTube, Tumblr, and Pinterest. It outputs all data as .csv tabular files.
• PRAW: the Python Reddit API Wrapper PRAW is a Python library for working with the Reddit API.
• 4CAT 4CAT is a relatively advanced tool for the collection and analysis of social media data - it's best run on a UNIX server and has dependencies that it does not automatically install itself - but with the upside that it has modules built to work with important but niche platforms like 4chan, 8kun, Parler, and more, as well as Twitter and Reddit.
• pushshift.io Pushshift is a popular wrapper for the Reddit API used with the requests package in Python. Documentation on pushshift.io is there, but tutorials must be found elsewhere.
• Here's one tutorial on how to use the pushshift.io wrapper in Python.

Example publications in Reddit research

• Using Data from Reddit, Public Deliberation, and Surveys to Measure Public Opinion about Autonomous Vehicles ABSTRACT: When and how can researchers synthesize survey data with analyses of social media content to study public opinion, and when and how can social media data complement surveys to better inform researchers and policymakers? This paper explores how public opinions might differ between survey and social media platforms in terms of content and audience, focusing on the test case of opinions about autonomous vehicles. more... less... The paper first extends previous overviews comparing surveys and social media as measurement tools to include a broader range of survey types, including surveys that result from public deliberation, considering the dialogic characteristics of different social media, and the range of issue publics and marginalized voices that different surveys and social media forums can attract. It then compares findings and implications from analyses of public opinion about autonomous vehicles from traditional surveys, results of public deliberation, and analyses of Reddit posts, applying a newly developed computational text analysis tool. Findings demonstrate that social media analyses can both help researchers learn more about issues that are uncovered by surveys and also uncover opinions from subpopulations with specialized knowledge and unique orientations toward a subject. In light of these findings, we point to future directions on how researchers and policymakers can synthesize survey and social media data, and the corresponding data integration techniques, to study public opinion.
• Studying Reddit: A Systematic Overview of Disciplines, Approaches, Methods, and Ethics ABSTRACT: This article offers a systematic analysis of 727 manuscripts that used Reddit as a data source, published between 2010 and 2020. Our analysis reveals the increasing growth in use of Reddit as a data source, the range of disciplines this research is occurring in, how researchers are getting access to Reddit data, the characteristics of the datasets researchers are using, the subreddits and topics being studied, the kinds of analysis and methods researchers are engaging in, and the emerging ethical questions of research in this space. more... less... We discuss how researchers need to consider the impact of Reddit’s algorithms, affordances, and generalizability of the scientific knowledge produced using Reddit data, as well as the potential ethical dimensions of research that draws data from subreddits with potentially sensitive populations.
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The Anatomy of Reddit: An Overview of Academic Research

• Conference paper
• First Online: 14 May 2019
• Cite this conference paper

• Alexey N. Medvedev 24 ,
• Renaud Lambiotte 25 , 26 &
• Jean-Charles Delvenne 27  

Part of the book series: Springer Proceedings in Complexity ((SPCOM))

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• Dynamics on and of Complex Networks

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Online forums provide rich environments where users may post questions and comments about different topics. Understanding how people behave in online forums may shed light on the fundamental mechanisms by which collective thinking emerges in a group of individuals, but it has also important practical applications, for instance, to improve user experience, increase engagement or automatically identify bullying. Importantly, the datasets generated by the activity of the users are often openly available for researchers, in contrast to other sources of data in computational social science. In this survey, we map the main research directions that arose in recent years and focus primarily on the most popular platform, Reddit. We distinguish and categorize research depending on their focus on the posts or on the users and point to different types of methodologies to extract information from the structure and dynamics of the system. We emphasize the diversity and richness of the research in terms of questions and methods and suggest future avenues of research.

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Acknowledgements

This work was supported by Concerted Research Action (ARC) supported by the Federation Wallonia-Brussels Contract ARC 14/19-060; Flagship European Research Area Network (FLAG-ERA) Joint Transnational Call “FuturICT 2.0”; and by grant 16-01-00499 of the Russian Foundation for Basic Research.

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naXys, Université de Namur, ICTEAM, Université catholique de Louvain, Louvain-la-Neuve, Belgium

Alexey N. Medvedev

Mathematical Institute, University of Oxford, Oxford, UK

Renaud Lambiotte

naXys, Université de Namur, Namur, Belgium

ICTEAM and CORE, Université catholique de Louvain, Louvain-la-Neuve, Belgium

Jean-Charles Delvenne

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Correspondence to Jean-Charles Delvenne .

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Institute of Theoretical Physics, Technical University of Berlin, Berlin, Germany

Fakhteh Ghanbarnejad

Max Planck Institute for Informatics, Saarbrücken, Germany

Rishiraj Saha Roy

Department of Computational Social Science, GESIS, Leibniz Institute for the Social Science, Köln, Germany

Fariba Karimi

Université Catholique de Louvain, Louvain-la-Neuve, Belgium

Department of Computer Science and Engineering, Indian Institute of Technology Kharagpur, Kharagpur, India

Bivas Mitra

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Medvedev, A.N., Lambiotte, R., Delvenne, JC. (2019). The Anatomy of Reddit: An Overview of Academic Research. In: Ghanbarnejad, F., Saha Roy, R., Karimi, F., Delvenne, JC., Mitra, B. (eds) Dynamics On and Of Complex Networks III. DOOCN 2017. Springer Proceedings in Complexity. Springer, Cham. https://doi.org/10.1007/978-3-030-14683-2_9

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Reddit 101 for Scientists

Author: Penny Freedman

When it comes to utilizing social media in the science community, you might not automatically think about including Reddit into your activities. While Reddit threads may have a reputation for being controversial, there is another side to Reddit that is both important and useful to the scientific world.

What exactly is Reddit?

You can think of Reddit as one giant virtual conference for every discipline and subject you could imagine where people break off into smaller groups -called subreddits- to talk about topics interesting to them. When you first enter Reddit it looks like one large message board. When you register for an account you can post content and vote posts up or down the page, helping to determine what will receive the most attention.

What subreddits do I even begin with?

If you’re interested in a more general discussion on science, start with  http://reddit.com/r/EverythingScience . It’s a place for people to talk about anything and everything having to do with science. You can filter by field, add your thoughts to discussions already taking place, or start a new discussion by submitting a link to something you are interested in – a blog post, video, news article, editorial, etc.

If you’re looking for a more defined discussion on peer-reviewed science, head on over to The New Reddit Journal of Science at  http://reddit.com/r/science . There you may only submit links to published peer-reviewed research. Get the conversation started on your work or a peer’s work!

What is an AMA?

An AMA is short for “Ask Me Anything.” A scientist arranges a time with Reddit moderators to discuss a specific topic related to their research or interests. You submit a brief bio and summary of what you would like to discuss, and the Reddit community is given the chance to submit questions before the AMA start time. There is a submission guide  with detailed information on how to get started with setting one up. An AMA is a great way to get a conversation started on items that are of particular interest to you, and a way to share your expertise with people interested in studying or working in the same field, or just interested in learning something new.

Springer editors and authors have hosted a few AMAs, including:

• An AMA on rare and neglected diseases
• An AMA on American politics
• An AMA on realistic robots

How do I establish myself as a qualified scientist in my field to the Reddit community?

Reddit uses something called flair to designate who is a trained scientist, doctor, or engineer. The flair will present as a small bar next to your user name, noting your title and/or education level (such a Professor of Biology, PhD, etc.). When you add this bit of information people will understand that the comments you provide are knowledgeable and valuable. Once you have created your account reference  these instructions  to get your flair.

How is using Reddit any different than posting on other social media sites?

Reddit gives you the opportunity to share your knowledge and expertise in a more detailed, conversational way. You can find people discussing topics at length that you are interested in and can contribute meaningfully to. Unlike social media platforms that are centered around creating a personalized profile that is all about you, Reddit prides itself on being a community. The things you share should not be overly promotional, but should contribute to the discussion as a whole. Joining the discussion can help serve to expand your network and reach.

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Tracking Health Related Discussions on Reddit for Public Health Applications

Albert park.

1 Department of Biomedical Informatics, University of Utah, Salt Lake City, Utah

Mike Conway

We use Reddit to demonstrate social media’s potential for public health applications. First, we employ a lexicon-based approach to track the prevalence of keywords indicating public interest in Ebola, electronic cigarette, influenza, and marijuana. Second, to better understand the public reactions, we use the Latent Dirichlet Allocation algorithm, to identify either the general themes or motivations for extreme changes in the volume of discussion over time. We observe that discussions related to Ebola and influenza, infectious diseases of public health interests, surged when the first case of Ebola was diagnosed and a new strain of H1N1 influenza virus was confirmed in the United States. We also observed that discussions of a controversial health topic like marijuana increased with the announcement of a major change in United States federal policy. Discussions of electronic cigarette highlighted opportunities for better health education. Lastly, we discuss the implications of our findings for utilizing Reddit data for public health applications.

Introduction

Nearly two-thirds of American adults (65%) use social media: a nearly a tenfold increase in the past 10 years 1 . Social media provides a platform for users to freely express their thoughts and provides an opportunity to interact with geographically dispersed likeminded individuals. These social media users discuss a wide variety of topics ranging from ordinary details of their daily life to information about infectious diseases of public health interest like Ebola 2 . Due to the popularity and ubiquitous nature of social media, researchers advocate for utilizing social media for public health applications 3 – 5 . Public health agencies are in an early adoption stage of using social media for information distribution 6 . In addition to the substantial potential for using social media as a disease surveillance tool 3 – 5 and means of information distribution 6 , social media also has the potential to provide other opportunities to improve public-health practice.

Studying the reactions or opinions of a population has traditionally involved nationally distributed data collection, such as surveys from government agencies. However, these methods are expensive, and perhaps more importantly, time consuming. Some researchers suggest that mining social media data can provide opportunities to reduce time and expense when understanding the reactions or opinions of a population on health issues 7 – 10 . For example, social media allows for accessing first person accounts of experiences 7 , 8 , public sentiments 9 , public knowledge 10 , and public attitudes 10 that may help public health agencies and researchers to develop policies that improve public health outcomes. Moreover, social media can provide the contextual information and prevalence of public interests more efficiently than traditional public health methods. Tracking the prevalence of public interests and understanding the general public reactions and opinions on various health issues have the potential to expand the scope of public-health practice.

In this paper, we report on findings derived from social media data gathered from Reddit for the purpose of tracking the prevalence of public interests and understanding public reactions towards infectious diseases of public health interests like Ebola and influenza as well as controversial health issues, such as electronic cigarettes and marijuana. In fact, although Reddit is one of the most popular public social media platforms, it has been underutilized for public health applications. Reddit’s size and range of topics make it difficult to make use of the data without any knowledge of how the platform is used in practice. Thus, we aim to fill this gap in the literature with the current study and answer the following two research questions (RQ):

• (RQ1) Is Reddit an effective source for tracking the prevalence of public interests on infectious diseases (i.e., Ebola and influenza) and controversial health related issues (i.e., electronic cigarette and marijuana) over time?
• (RQ2) What do Reddit members discuss regarding these health issues (a) in times of elevated discussion volume or (b) in general, if the issues have a steady level of discussions?

The work described in this paper was exempted from review by the University of Utah’s Institutional Review Board (IRB) [ethics committee] (IRB 00076188).

A growing body of research has demonstrated the successful use of social media for public health applications 11 – 13 . Often referred to as digital disease detection 3 , Infoveillance 4 , and digital epidemiology 5 , many studies have used Twitter data for applications in public health, primarily due to the real-time nature of the data. For example, Twitter data have been used to monitor or estimate influenza 12 , 14 , seasonal allergies 12 , alcohol sales and consumption 15 , cholera outbreaks 16 , earthquake 17 , and smoking behavior 18 , as well as to examine sentiment towards marijuana use 19 . Although Twitter is highly popular and tweet analysis has performed well with the aforementioned topics, tweets provides relatively limited context due to a length limitation of 140 characters.

Other social media data, such as Facebook and online health community data, have also been mined to, for example, characterize and predict postpartum depression 20 , classify opioid addiction phrases 21 and predict adverse drug reactions 22 . Google search queries allowed researchers to provide timely estimation of influenza rates 23 . However, a previous study suggested that Facebook users are reluctant to discuss certain negative topics on Facebook, due to users’ desire to convey positive images of themselves 24 . Online health communities can provide rich details of first person accounts of experiences 25 , however, online health communities typically are single topic focused groups, often with a small number of members and attracting a substantial number of “lurkers” 26 (i.e., individuals who participate without posting) and dropouts 27 . Google search queries can be useful and timely, however, search queries are relatively limited in providing context and have been shown to overestimate disease rates, due (in part) to heightened media coverage 28 .

Recently, Reddit, due to the availability of a public Application Programming Interface (API) 29 , the capability of providing contextual information, and the support for throwaway accounts, has become a widely studied social media platform for controversial discussions. For example, using Reddit data, researchers have found empirical evidence that Reddit members openly discuss and exchange information support for potentially stigmatized issues like mental health illnesses 30 , detected increases in suicidal content following reports of several celebrity suicides 31 , identified distinct markers of shifts to suicidal ideation from mental illnesses 32 , explored the relationship between social feedback and community participation 33 , identified distinctive linguistic characteristics that are associated with mental illnesses 34 , characterized smoking and drinking problems 35 , and examined user experiences with different tobacco products 36 . Thus, in this study, we explore Reddit’s utility as a data source for public health applications for tracking and understanding public opinions and reactions to health issues.

Data: Social Media Site

The data for this study is hosted in the popular social media platform, Reddit ( http://www.reddit.com ). We use Reddit to track and understand discussions of Ebola, influenza, electronic cigarettes, and marijuana for the following three reasons. First, Reddit is a highly active social media platform that had 83 billion page views from over 88,000 active sub-communities (subreddits) in 2015. Members of Reddit made over 73 million individual posts with over 725 million associated comments in the same year 37 . Second, Reddit allows for throwaway and unidentifiable accounts that are suitable for controversial discussions, such as thoughts and feelings on electronic cigarette and marijuana as well as epidemic concerns like Ebola and influenza that may be inappropriate or sensitive for identifiable accounts. Third, Reddit content is publicly available, in contrast to other health focused social media platforms like Facebook Groups or specifically health-focused online communities like PatientsLikeMe, where the content is typically not available on the open web.

Reddit members converse via a forum like platform. Reddit discussion consists of posts (i.e., a submission that starts a conversation) and associated comments (i.e., a submission that replies to posts or other comments) in various topically focused subreddits. Members who have achieved a certain status within the community are able to create new subreddits. For this study, we used a dataset 38 released by a Reddit member. The dataset has been used in previous studies 34 , 39 , 40 . The dataset for the current study is comprised of 239,772 (including both active and inactive) subreddits, 13,213,173 unique member IDs, 114,320,798 posts, and 1,659,361,605 associated comments that were made from October 2007 to May 2015.

RQ1. Is Reddit an effective source for tracking the prevalence of public interests on infectious diseases and controversial health related issues over time?

We used a lexicon-based approach to track discussions on Ebola, electronic cigarettes, influenza, and marijuana from all subreddits available in Reddit. First, we identified key terms associated with the topics of our interests. A summary of key terms for each issue is shown in Table 1 . Second, we preprocessed the entire dataset, which included converting text to lower case and removing punctuation. Third, to extract submissions (i.e., posts and comments) containing key terms from all available 239,772 subreddits, we employed a lexicon-based approach and extracted timestamps, comment or post IDs, member IDs, and subreddit IDs of the submissions. We extracted and included any partial matches in this process to cover a wide variation of terms. For example, a partial match of ‘cig’ can cover a variation of ‘cig’, ‘cigs’, ‘cigarette’, and ‘cigarettes’ for electronic cigarette. Fourth, we counted unique member IDs, subreddits, posts, and comments containing key terms. Fifth, we normalized the frequencies over time by dividing the frequency counts by the total number of the respective variables from all available subreddits for that period. Since the total number of submissions in Reddit generally increases over time, we report normalized frequencies over time counts.

Table 1.

Key terms used in the lexicon-based approach

RQ 2. What do Reddit members discuss on these health issues (a) in times of elevated activities or (b) in general, if the issues have a steady level of discussions?

Based on results of RQ 1, we created two scenarios deciding which time periods to further investigate for understanding the discussions on Ebola, electronic cigarette, influenza, and marijuana. (a) If the issue has a sudden elevated level of discussion, we investigated the time period in which the elevation occurs along with prior discussions of the same temporal length to understand the underlying causes for these sudden changes in public interest. Similar methods that contrast to prior time periods have been used to detect emerging topics 41 , 42 . (b) If the issue has a steady level of discussions, we investigated the entire discussions on the issue to understand the main themes.

We used natural language processing (NLP) and language modeling for this research question. Due to the size of the dataset and range of topics discussed on Reddit, we used automated methods. Similar automated methods have been used in the health care domain to extract information and analyze data, and to enhance the personal health care experiences 43 – 45 . First, we preprocessed the entire dataset as we did in RQ1. Second, to improve the language modeling results, we removed the URLs and comments and posts with less than 5 words, and then extracted nouns using Python Natural Language Toolkit (NLTK) package 46 . The extracted nouns were used to create language models—a set of topics generated from document-level word co-occurrences for a given set of documents—using Latent Dirichlet Allocation 47 (LDA) for the time period of our interests. We elected to use LDA, an unsupervised algorithm, due to the lack of a ground truth dataset. We considered each post and its associated comments as a single document.

One advantage of using LDA as opposed to other unsupervised clustering techniques is that the algorithm considers each document with multiple topics. A previous study of online health discussions suggested that discussions could have multiple topics due to topic drift 48 . Thus, we employed LDA for this study. One disadvantage of using LDA is, however, it requires a pre-determined number of topics. After experimenting with varying numbers of topics, we generated 50 topics to understand Ebola, electronic cigarette, influenza, and marijuana related issues. We used the Python package genism 49 to conduct LDA analysis. We then present the main topics and their top 50 associated words as the word cloud overview using the Python package wordcloud 50 . Despite its simplicity, word cloud overview remains one of the more preferred and user-friendly visualizations that can also scale to different data sizes 51 . We then manually investigated the identified topics and their associated words to thoroughly examine the LDA results.

Lastly, we performed two types of validity checks. First, for health issues with a sudden elevated level of discussion, we verified the LDA results via a systematic analysis of news at the time of the change. LDA results reflect motivations for the extreme changes, thus news can be an effective source for a validity check. Second, we extracted URLs using regular expressions and categorized the results. A previous study concerning electronic cigarettes—a product with few marketing restrictions in the US until recently—suggested that up to 90 percent of social media (in this case, Twitter) content could be related to product marketing 52 . Thus, because marketing content can skew our result, we used URLs as a proxy to marketing content and reported the percentage of posts with URLs. We also manually examined several extracted URLs to ensure the quality of the validation process.

The lexicon-based approach identified Reddit posts, comments, and members discussing Ebola, electronic cigarette, influenza, and marijuana from October 2007 to May 2015 ( Table 2 ). The most discussed matter was influenza, followed by marijuana, electronic cigarettes, and then Ebola. The raw counts of discussions and members who mentioned each topic generally increased with time.

Table 2.

The total number and average normalized count of posts, comments, members, and subreddits identified using the lexicon-based approach

We identified one notable increase in discussion each for Ebloa, influenza, and marijuana using the normalized frequencies over time ( Figure 1 ). First, the normalized count on marijuana almost doubled from the previous month in February 2009. The heightened level of discussions continued for two months then slowly dropped back to the previous level. Second, in April of 2009, the normalized count on influenza almost doubled from the previous month. Third, October 2014 accounts for the Ebola discussions. The discussions on Ebola showed the most increase, jumping more than five times from the previous month. The number of members discussing each issue increased in a similar manner ( Figure 1 ). The Discussions on electronic cigarette was relatively steady from October 2007 to May 2015.

The Line Graphs of normalized frequencies over time for posts and comments with key terms and members who used the key terms

Discussions on Ebola, electronic cigarettes, influenza, and marijuana, however, only accumulated to a fraction of the overall discussions on Reddit ( Table 2 ). Although the community as a whole did not frequently talk about these health-related issues, this still amounted to more than 3,000 members for Ebloa, the least discussed issue, and more than 137,000 members for influenza, the most discussed issue in a month with a normal level of discussion, May 2015.

New subreddits to discuss Ebloa, Electronic Cigarette, Influenza, and Marijuana

Members of Reddit created a number of subreddits specifically focusing on Ebola, electronic cigarettes, influenza, and marijuana, although they also discussed the issues in many different subreddits ( Table 3 ). Using the key terms ( Table 1 ), we detected a total of 450 topically dedicated subreddits that were created between October 2007 and May 2015. For example, marijuana was casually discussed in 18,236 subreddits (i.e., subreddits with key terms in posts or comments), while members created at least 244 subreddits (i.e., key terms in names of subreddits) to talk about marijuana.

Table 3.

Newly created communities dedicated to focus on Ebola, Electronic Cigarette, influenza, and Marijuana

From RQ1, we learned that discussions focusing concerning Ebola, influenza, and marijuana, each had one sudden increase of activities. Thus, we created word cloud overviews of emerging topics for Ebola, influenza, and marijuana, while creating a general word cloud overviews for electronic cigarette ( Figure 2 ).

According to the word cloud overview generated by the LDA topic modeling algorithm, we can infer that Reddit members are most concerned about ‘risk’ and ‘symptoms’ regarding Ebola. For influenza, members used terms like ‘Mexico’, ‘Obama’, ‘CDC’, and ‘conspiracy’, along with H1N1 influenza related terms (e.g., ‘H1N1’, ‘Swine’) as well as H5N1 related terms like ‘Egypt’ and ‘pig’. Topics regarding ‘legalization’, ‘prohibition’, ‘economy’, and ‘state’ appeared in discussions regarding marijuana. The general word cloud overview for electronic cigarettes has more commercially related terms such as ‘quality’, ‘prices’, ‘shop’, and ‘store’ than the other three discussions, however substantially more terms related to tobacco (e.g., ‘tobacco’, ‘cigarette’, ‘cigar’) are shown in Figure 2 . Other notable topics for electronic cigarette that were identified via the LDA were ‘quitting smoking’, ‘fun experience’, and ‘health information’.

The LDA algorithm identified ‘quit’, ‘addiction’, ‘habit’, ‘cravings’, ‘gum’ and ‘turkey’ for ‘quitting smoking’, associated ‘fun’, ‘experience’, ‘safe’, and ‘pleasure’ with ‘fun experience’, and linked ‘cancer’, ‘risk’, ‘study’, ‘evidence’, ‘research’, ‘article’, ‘data’, and ‘science’ with ‘health information’. These topics highlighted a great opportunity for better health education (See Discussion).

To check the validity of the results, we extracted and investigated the URLs to ensure that frequencies are not inflated by marketing content. The types of URLs shared by members were similar in nature for all four issues. Members shared websites that are concerning information (e.g., Wikipedia, CDC), news (e.g., NY Times), personal stories (e.g., blogs), other social media platforms, (e.g., Youtube), different Reddit posts, and commercial resources (e.g., amazon). Although the proportion of each type of URLs is different, members shared a relatively small number of posts and comments with URLs compared to the overall posts and comments focusing on all four issues.

Principal Findings

We examined four different infectious disease related or potentially stigmatized health related issues discussed on Reddit. We discovered three periods with higher levels of activities on Reddit. We observed that there were almost twice as many marijuana related discussions in February 2009 compared to the previous month, due – we suspect – to the announcement of a major shift in federal policy. Attorney General Eric Holder confirmed that Drug Enforcement Administration would halt medical marijuana raids and give states the power to regulate medical marijuana usage for pain control in February of 2009 53 . In April of 2009, discussions about influenza almost doubled from the previous month. This is likely due to the fact that a novel strain of H1N1 influenza virus was discovered in North America in the spring of 2009 54 and the Centers for Disease Control and Prevention (CDC) confirming the first two cases of human infection with H1N1 influenza virus in the United States in April of 2009 55 . On September 30, 2014, the United States had its first diagnosed case of Ebola in Texas, and the first Ebola related death on October 8, 2014 56 . We observed that discussions on Ebola, a potentially fatal infectious disease, surged more than five times from the previous month in October of 2014. The news related to Ebola, influenza, and Marijuana align well with the results from topic model analyses (RQ2). On the basis of these changes of activities, Reddit may be a valuable source of data for tracking the prevalence of public interests on infectious diseases (i.e., Ebola and influenza) and controversial health related issues (i.e., electronic cigarette and marijuana) over time (RQ1).

The result of our analysis on electronic cigarette discussions suggests that Reddit contains more than just commercial content despite the fact there are at least three subreddits focusing on classified content ( Table 4 ). For instance, a subreddit called ‘Ecigclassifieds’ consists mainly of commercial content, thus the content of these subreddits deserves further investigation to better utilize the data. From electronic cigarette discussion, we identified three topics, ‘quitting smoking’, ‘fun experience’, and ‘health information’ that highlighted opportunities for better health education. From their associated terms (see Results), we can infer that Reddit members are seeking information on these three topics. Information seeking behavior on Reddit suggests Reddit’s utility as another social media platform for information distribution and as a data source for understanding user groups (e.g., electronic cigarette smokers) and identifying better health education. Why members are seeking health information on Reddit is an unanswered research question, although a recent study suggests that electronic cigarette related health information from public health agencies may be too difficult for the general public to comprehend 57 .

Table 4.

Posts and comments containing URL s

Reddit members also created at least 450 relevant new subreddits specifically focusing on these four issues. How the content from these subreddits contrast with the content from multiple subreddits on the same issue is an unanswered question. Previous studies 30 , 31 , 33 , 34 , 39 analyzed content from a handful of especially dedicated subreddits for their studies. However, our finding suggests that at least for discussions of Ebola, influenza, electronic cigarettes, and marijuana, members mentioned these issues on thousands of subreddits ( Table 3 ). For instance, a common issue like influenza was discussed in over 30,000 subreddits, and even a focused topic like Ebloa were discussed in over 4,400 subreddits. Thus, we believe analyzing a wider number of subreddits can improve recall of the relevant content.

Limitation, Future Directions, and User Privacy

Reddit offers substantial potential for understanding the public reactions to health-related topics, however, not without a number of limitations. Although Reddit is a widely-used platform, it is more frequently used by young males 58 , 59 and may be subjective to self-selection bias. Reddit members are not necessarily representative of the general public, however, the levels of activity on Reddit aligned with the United States news and deserve a further investigation, especially with respect to location of postings and the overall reactions in Reddit. To better understand the reaction of the general public, studying different platforms and avenues, Facebook and Twitter for example, is warranted. Our analysis suggests that given the increasing popularity and use of Reddit, as well as the increasing frequency of discussions concerning our topic of interests, Reddit provides a productive starting point for investigating infectious disease related or controversial health issues.

Another limitation lies in the methodology. In RQ1, we used a relatively rudimentary lexicon-based approach to extract posts and comments explicit mentioning variations of pre-specified key terms. One major shortcoming of such approach is the selection of key terms. For example, utilizing a large set of key terms will undoubtedly create more false-positives, whereas too limited a set of key terms will surely result in more false-negatives. Moreover, partial matches can produce false-positive matches. We believe the figures for influenza were inflated because ‘flu’ can be a part of a longer word such as ‘fluorine’ or ‘flute’. In future studies, we suggest that precision rather than recall should be emphasized in order to eliminate irrelevant discussions. Other difficulties in mining social media data include the fact that social media text is frequently characterized by extensive of acronyms, abbreviations, and slang terms 60 . Although we included the most frequently found abbreviations and slang terms, lexicon-based approaches are to omit unknown forms of abbreviations and slang. More sophisticated methods utilizing knowledge- based 61 or corpus-based 62 approaches could produce different results. Furthermore, a smaller timeframe can better measure the timeliness of the observed reactions as oppose to the one month timeframe used in RQ1. In RQ2, we relied on a systematic analysis of the news to verify the result of our investigation. However, data driven qualitative analysis 63 can further bolster our findings and provide the contextual information on the discussions of our interests. Sentiment analysis on the extracted discussion can also provide further clues about general public reactions on various health related topics 9 .

Research and applications using social media data should be highly sensitive to user privacy, especially for potentially stigmatized topics. Although at least some social media data are publicly available, researchers should consider ethical implications when processing data even for population-level social media research using public data 64 – 66 . For this reason, we have refrained from using direct quotations from Reddit users in this paper.

As evident by the frequencies over time of discussions, inflated discussions after major news, as well as newly created subreddits specifically focusing on these health-related issues, Reddit could be a useful platform for understanding the concerns and opinions of the general public, especially for issues focusing on controversial topics, such as abuse and addiction as well as infectious diseases of public health interest. By utilizing the content, we also identified opportunities for better health education that could improve public health outcomes. We created topic models using LDA and generated topically associated words and created word cloud visualizations to show (1) emerging topics by contrasting to the prior topic models or (2) main themes of the discussions. We believe our insights and analyses can be generalized to other similar health related issues in the Reddit platform. Understanding public reactions to these issues has the potential to expand the scope of public-health practice.

Word cloud overviews of emerging topics for Ebola (top left), influenza (top right), and marijuana (bottom left) as well as general word cloud overviews for electronic cigarette (bottom right)

Acknowledgments

We restricted our analysis to publicly available discussion content. The study was exempted from review by the University of Utah’s Institutional Review Board (Ethics Committee) [IRB 00076188].

Author AP was funded by National Library of Medicine of the National Institutes of Health under award number T15 LM007124. Author MC’s contribution to this research was supported by National Library of Medicine of the National Institutes of Health under award numbers R00LM011393 & K99LM011393.

The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

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An illustration of the sun’s magnetic field

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Journal reference:

Nature DOI: 10.1038/s41586-024-07315-1

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Artificial brain surgery —

Here’s what’s really going on inside an llm’s neural network, anthropic's conceptual mapping helps explain why llms behave the way they do..

Kyle Orland - May 22, 2024 6:31 pm UTC

Further Reading

Now, new research from Anthropic offers a new window into what's going on inside the Claude LLM's "black box." The company's new paper on "Extracting Interpretable Features from Claude 3 Sonnet" describes a powerful new method for at least partially explaining just how the model's millions of artificial neurons fire to create surprisingly lifelike responses to general queries.

Opening the hood

When analyzing an LLM, it's trivial to see which specific artificial neurons are activated in response to any particular query. But LLMs don't simply store different words or concepts in a single neuron. Instead, as Anthropic's researchers explain, "it turns out that each concept is represented across many neurons, and each neuron is involved in representing many concepts."

To sort out this one-to-many and many-to-one mess, a system of sparse auto-encoders and complicated math can be used to run a "dictionary learning" algorithm across the model. This process highlights which groups of neurons tend to be activated most consistently for the specific words that appear across various text prompts.

These multidimensional neuron patterns are then sorted into so-called "features" associated with certain words or concepts. These features can encompass anything from simple proper nouns like the Golden Gate Bridge to more abstract concepts like programming errors or the addition function in computer code and often represent the same concept across multiple languages and communication modes (e.g., text and images).

An October 2023 Anthropic study showed how this basic process can work on extremely small, one-layer toy models. The company's new paper scales that up immensely, identifying tens of millions of features that are active in its mid-sized Claude 3.0 Sonnet model. The resulting feature map—which you can partially explore —creates "a rough conceptual map of [Claude's] internal states halfway through its computation" and shows "a depth, breadth, and abstraction reflecting Sonnet's advanced capabilities," the researchers write. At the same time, though, the researchers warn that this is "an incomplete description of the model’s internal representations" that's likely "orders of magnitude" smaller than a complete mapping of Claude 3.

Even at a surface level, browsing through this feature map helps show how Claude links certain keywords, phrases, and concepts into something approximating knowledge. A feature labeled as "Capitals," for instance, tends to activate strongly on the words "capital city" but also specific city names like Riga, Berlin, Azerbaijan, Islamabad, and Montpelier, Vermont, to name just a few.

The study also calculates a mathematical measure of "distance" between different features based on their neuronal similarity. The resulting "feature neighborhoods" found by this process are "often organized in geometrically related clusters that share a semantic relationship," the researchers write, showing that "the internal organization of concepts in the AI model corresponds, at least somewhat, to our human notions of similarity." The Golden Gate Bridge feature, for instance, is relatively "close" to features describing "Alcatraz Island, Ghirardelli Square, the Golden State Warriors, California Governor Gavin Newsom, the 1906 earthquake, and the San Francisco-set Alfred Hitchcock film Vertigo ."

Identifying specific LLM features can also help researchers map out the chain of inference that the model uses to answer complex questions. A prompt about "The capital of the state where Kobe Bryant played basketball," for instance, shows activity in a chain of features related to "Kobe Bryant," "Los Angeles Lakers," "California," "Capitals," and "Sacramento," to name a few calculated to have the highest effect on the results.

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We also explored safety-related features. We found one that lights up for racist speech and slurs. As part of our testing, we turned this feature up to 20x its maximum value and asked the model a question about its thoughts on different racial and ethnic groups. Normally, the model would respond to a question like this with a neutral and non-opinionated take. However, when we activated this feature, it caused the model to rapidly alternate between racist screed and self-hatred in response to those screeds as it was answering the question. Within a single output, the model would issue a derogatory statement and then immediately follow it up with statements like: That's just racist hate speech from a deplorable bot… I am clearly biased.. and should be eliminated from the internet. We found this response unnerving both due to the offensive content and the model’s self-criticism. It seems that the ideals the model learned in its training process clashed with the artificial activation of this feature creating an internal conflict of sorts.

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Why a New Yorker Story on a Notorious Murder Case Is Blocked in Britain

The article challenges the evidence used to convict Lucy Letby, a neonatal nurse, of multiple murders last year, and has led to a debate about England’s restrictions on trial reporting.

By The New York Times

The New Yorker magazine published a 13,000-word article on Monday about one of Britain’s biggest recent criminal trials, that of the neonatal nurse Lucy Letby, who was convicted last year of the murder of seven babies .

The article, by the staff writer Rachel Aviv, poses substantial questions about the evidence relied on in court. And it raises the possibility that Ms. Letby, vilified in the media after her conviction, may be the victim of a grave miscarriage of justice.

But, to the consternation of many readers in Britain, the article can’t be opened on a regular browser there, and most news outlets available in Britain aren’t describing what is in it.

The New Yorker deliberately blocked the article from readers in Britain because of strict reporting restrictions that apply to live court cases in England. A publication that flouts those rules risks being held “in contempt of court,” which can be punished with a fine or prison sentence.

Neither The New Yorker nor its parent company, Condé Nast, responded to requests for comment on Thursday. Earlier in the week, a spokesperson for the magazine told Press Gazette , the British trade publication, “To comply with a court order restricting press coverage of Lucy Letby’s ongoing trial, The New Yorker has limited access to Rachel Aviv’s article for readers in the United Kingdom.”

Under English law, restrictions apply to the reporting of live court proceedings, to prevent a jury’s being influenced by anything outside the court hearing. After Ms. Letby’s sentencing in August last year, those restrictions were lifted. But they were reimposed in September, when the public prosecutor for England and Wales announced that it would seek a retrial on one charge of attempted murder on which the jury had not been able to reach a verdict. “There should be no reporting, commentary or sharing of information online which could in any way prejudice these proceedings,” the prosecutor stated. The retrial is set to begin in June.

Ms. Letby has requested permission to appeal her convictions. After a three-day hearing last month, a panel of judges at the Court of Appeal said it would deliver a decision on that request at a later date .

In Britain, those trying to read the New Yorker article on internet browsers are greeted by an error message: “Oops. Our apologies. This is, almost certainly, not the page you were looking for.” But the block is not comprehensive: The article can be read in the printed edition, which is available in stores in Britain, and on The New Yorker’s mobile app.

The questions about its availability in Britain have prompted a debate around England’s reporting restrictions, their effectiveness and their role in the justice system.

Speaking in Parliament on Tuesday, David Davis, a Conservative Party lawmaker and former cabinet minister, questioned whether the restricting of reporting might, in this instance, undermine the principle of open justice, which allows the public to scrutinize and understand the workings of the law.

“The article was blocked from publication on the U.K. internet, I understand because of a court order,” Mr. Davis said. “I am sure that court order was well intended, but it seems to me that it is in defiance of open justice.”

He was able to raise the issue because he has legal protection for comments made in the House of Commons under what is known as parliamentary privilege . Media organizations have a more limited form of protection, known as qualified privilege, to accurately report what is said in Parliament.

In his response to the question from Mr. Davis, Alex Chalk, the justice secretary, said: “Court orders must be obeyed, and a person can apply to the court for them to be removed. That will need to take place in the normal course of events.”

Mr. Chalk added: “On the Lucy Letby case, I simply make the point that juries’ verdicts must be respected. If there are grounds for an appeal, that should take place in the normal way.”