research papers about reddit

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

• View all journals
• Explore content
• About the journal
• Publish with us
• Sign up for alerts
• 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.

You can also search for this author in PubMed   Google Scholar

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.

Access options

Access Nature and 54 other Nature Portfolio journals

Get Nature+, our best-value online-access subscription

24,99 € / 30 days

cancel any time

Subscribe to this journal

Receive 51 print issues and online access

185,98 € per year

only 3,65 € per issue

Rent or buy this article

Prices vary by article type

Prices may be subject to local taxes which are calculated during checkout

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).

Article   Google Scholar  

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

Download references

Related Articles

• Information technology

How I fled bombed Aleppo to continue my career in science

Career Feature 08 MAY 24

Illuminating ‘the ugly side of science’: fresh incentives for reporting negative results

Hunger on campus: why US PhD students are fighting over food

Career Feature 03 MAY 24

My PI yelled at me and I’m devastated. What do I do?

Career Feature 02 MAY 24

The dream of electronic newspapers becomes a reality — in 1974

News & Views 07 MAY 24

A global timekeeping problem postponed by global warming

Article 27 MAR 24

AI image generators often give racist and sexist results: can they be fixed?

News Feature 19 MAR 24

Research Associate - Neural Development Disorders

Houston, Texas (US)

Baylor College of Medicine (BCM)

Staff Scientist - Mitochondria and Surgery

Recruitment of talent positions at shengjing hospital of china medical university.

Call for top experts and scholars in the field of science and technology.

Shenyang, Liaoning, China

Shengjing Hospital of China Medical University

Faculty Positions at SUSTech School of Medicine

SUSTech School of Medicine offers equal opportunities and welcome applicants from the world with all ethnic backgrounds.

Shenzhen, Guangdong, China

Southern University of Science and Technology, School of Medicine

Manager, Histology Laboratory - Pathology

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Quick links

• Explore articles by subject
• Guide to authors
• Editorial policies

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))

Included in the following conference series:

• Dynamics on and of Complex Networks

2852 Accesses

40 Citations

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.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

• Available as PDF
• Read on any device
• Instant download
• Own it forever
• Available as EPUB and PDF
• Compact, lightweight edition
• Dispatched in 3 to 5 business days
• Free shipping worldwide - see info
• Durable hardcover edition

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

https://en.wikipedia.org/wiki/Reddit .

https://en.wikipedia.org/wiki/Slashdot .

https://en.wikipedia.org/wiki/Hacker_News .

https://en.wikipedia.org/wiki/Digg .

https://praw.readthedocs.io/en/latest .

The authors used karmadecay.com —the reverse image search tool specifically designed for Reddit.

Aragón, P., Gómez, V., Kaltenbrunner, A.: Visualization tool for collective awareness in a platform of citizen proposals. In: Proceedings of the International AAAI Conference on Weblogs and Social Media, pp. 756–757 (2016)

Google Scholar  

Aragón, P., Gómez, V., García, D., Kaltenbrunner, A.: Generative models of online discussion threads: state of the art and research challenges. J. Internet Serv. Appl. 8 (1), 15 (2017)

Article   Google Scholar  

Aragón, P., Gómez, V., Kaltenbrunner, A.: To thread or not to thread: the impact of conversation threading on online discussion. In: International AAAI Conference on Web and Social Media (2017)

Backstrom, L., Boldi, P., Rosa, M., Ugander, J., Vigna, S.: Four degrees of separation. In: Proceedings of the 4th Annual ACM Web Science Conference, pp. 33–42. ACM, New York (2012)

Bandari, R., Asur, S., Huberman, B.A.: The pulse of news in social media: forecasting popularity. In: Proceedings of the Sixth International AAAI Conference on Weblogs and Social Media (ICWSM), vol. 12, pp. 26–33 (2012)

Bishop, J.: The effect of de-individuation of the internet troller on criminal procedure implementation: an interview with a hater. Int. J. Cyber Criminol. 7 (1), 28–48 (2013)

Chandrasekharan, E., Pavalanathan, U., Srinivasan, A., Glynn, A., Eisenstein, J., Gilbert, E.: You can’t stay here: the efficacy of Reddit’s 2015 ban examined through hate speech. Proc. ACM Hum.-Comput. Interact. 1 , 31 (2017)

Chandrasekharan, E., Samory, M., Jhaver, S., Charvat, H., Bruckman, A., Lampe, C., Eisenstein, J., Gilbert, E.: The internet’s hidden rules: an empirical study of Reddit norm violations at micro, meso, and macro scales. Proc. ACM Hum.-Comput. Interact. 2 , 32:1–32:25 (2018). http://doi.acm.org/10.1145/3274301

Cohen, R., Havlin, S.: Scale-free networks are ultrasmall. Phys. Rev. Lett. 90 (5), 058701 (2003)

Article   ADS   Google Scholar  

Das, S., Lavoie, A.: The effects of feedback on human behavior in social media: an inverse reinforcement learning model. In: Proceedings of the 2014 International Conference on Autonomous Agents and Multi-Agent Systems, pp. 653–660. International Foundation for Autonomous Agents and Multiagent Systems (2014)

Derczynski, L., Rowe, M.: Tracking the diffusion of named entities. (2017, preprint). arXiv:1712.08349

Dommers, S., Van Der Hofstad, R., Hooghiemstra, G.: Diameters in preferential attachment models. J. Stat. Phys. 139 (1), 72–107 (2010)

Article   ADS   MathSciNet   Google Scholar  

Fang, H., Cheng, H., Ostendorf, M.: Learning latent local conversation modes for predicting comment endorsement in online discussions. In: Proceedings of The Fourth International Workshop on Natural Language Processing for Social Media, pp. 55–64 (2016)

Gaffney, D., Matias, J.N.: Caveat emptor, computational social science: large-scale missing data in a widely-published Reddit corpus. (2018, preprint). arXiv:1803.05046

Gilbert, E.: Widespread underprovision on Reddit. In: Proceedings of the 2013 Conference on Computer Supported Cooperative Work, pp. 803–808. ACM, New York (2013)

Glenski, M., Weninger, T.: Predicting user-interactions on Reddit. In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 609–612. ACM, New York (2017)

Glenski, M., Pennycuff, C., Weninger, T.: Consumers and curators: browsing and voting patterns on Reddit. IEEE Trans. Comput. Soc. Syst. 4 (4), 196–206 (2017)

Gómez, V., Kaltenbrunner, A., López, V.: Statistical analysis of the social network and discussion threads in Slashdot. In: Proceedings of the 17th International Conference on World Wide Web, pp. 645–654. ACM, New York (2008)

Gómez, V., Kappen, H.J., Kaltenbrunner, A.: Modeling the structure and evolution of discussion cascades. In: Proceedings of the 22Nd ACM Conference on Hypertext and Hypermedia, pp. 181–190 (2011)

Gómez, V., Kappen, H.J., Litvak, N., Kaltenbrunner, A.: A likelihood-based framework for the analysis of discussion threads. World Wide Web 16 (5–6), 645–675 (2013)

Gonzalez-Bailon, S., Kaltenbrunner, A., Banchs, R.E.: The structure of political discussion networks: a model for the analysis of online deliberation. J. Inf. Technol. 25 (2), 230–243 (2010). https://doi.org/10.1057/jit.2010.2

Halfaker, A., Keyes, O., Kluver, D., Thebault-Spieker, J., Nguyen, T., Shores, K., Uduwage, A., Warncke-Wang, M.: User session identification based on strong regularities in inter-activity time. In: Proceedings of the 24th International Conference on World Wide Web, pp. 410–418. International World Wide Web Conferences Steering Committee, Geneva (2015)

Hamilton, W.L., Zhang, J., Danescu-Niculescu-Mizil, C., Jurafsky, D., Leskovec, J.: Loyalty in online communities. In: Proceedings of the International AAAI Conference on Weblogs and Social Media, vol. 2017, p. 540. NIH Public Access (2017)

Hanson, W.A., Putler, D.S.: Hits and misses: herd behavior and online product popularity. Mark. Lett. 7 (4), 297–305 (1996)

Hessel, J., Tan, C., Lee, L.: Science, askscience, and badscience: on the coexistence of highly related communities. In: The Tenth International Conference on Web and Social Media (ICWSM), pp. 171–180 (2016)

Hessel, J., Lee, L., Mimno, D.: Cats and captions vs. creators and the clock: comparing multimodal content to context in predicting relative popularity. In: Proceedings of the 26th International Conference on World Wide Web, pp. 927–936. International World Wide Web Conferences Steering Committee, Geneva (2017)

Hirsch, J.E.: An index to quantify an individual’s scientific research output. Proceedings of the National Academy of Sciences 102 (46), 16569–16572 (2005)

Horne, B.D., Adali, S.: The impact of crowds on news engagement: a Reddit case study. (2017, preprint). arXiv:1703.10570

Horne, B.D., Adali, S., Sikdar, S.: Identifying the social signals that drive online discussions: a case study of Reddit communities. In: 26th International Conference on Computer Communication and Networks (ICCCN), pp. 1–9 (2017). https://doi.org/10.1109/ICCCN.2017.8038388

Jaech, A., Zayats, V., Fang, H., Ostendorf, M., Hajishirzi, H.: Talking to the crowd: what do people react to in online discussions? In: Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, pp. 2026–2031 (2015)

Kaltenbrunner, A., Gomez, V., Lopez, V.: Description and prediction of Slashdot activity. In: Latin American Web Conference 2007 (LA-WEB 2007), pp. 57–66. IEEE, Piscataway (2007)

Karsai, M., Kivelä, M., Pan, R.K., Kaski, K., Kertész, J., Barabási, A.L., Saramäki, J.: Small but slow world: how network topology and burstiness slow down spreading. Phys. Rev. E 83 (2), 025102 (2011)

Kumar, S., Hamilton, W.L., Leskovec, J., Jurafsky, D.: Community interaction and conflict on the web. In: Proceedings of the 2018 World Wide Web Conference on World Wide Web, pp. 933–943. International World Wide Web Conferences Steering Committee, Geneva (2018)

Lakkaraju, H., McAuley, J.J., Leskovec, J.: What’s in a name? Understanding the interplay between titles, content, and communities in social media. In: International AAAI Conference on Web and Social Media (ICWSM), vol. 1, no. 2, 3 (2013)

Lambiotte, R., Kosinski, M.: Tracking the digital footprints of personality. Proc. IEEE 102 (12), 1934–1939 (2014)

Lee, J.G., Moon, S., Salamatian, K.: Modeling and predicting the popularity of online contents with cox proportional hazard regression model. Neurocomputing 76 (1), 134–145 (2012)

Lumbreras, A., Jouve, B., Velcin, J., Guégan, M.: Role detection in online forums based on growth models for trees. Soc. Netw. Anal. Min. 7 (1), 49 (2017)

Marckert, J.F., Mokkadem, A., et al.: The depth first processes of Galton–Watson trees converge to the same Brownian excursion. Ann. Probab. 31 (3), 1655–1678 (2003)

Article   MathSciNet   Google Scholar  

Medvedev, A.N., Delvenne, J.C., Lambiotte, R.: Modelling structure and predicting dynamics of discussion threads in online boards. J. Complex Netw. 7 , 67–82 (2018). https://doi.org/10.1093/comnet/cny010

Mishne, G., Glance, N.: Leave a reply: an analysis of weblog comments. In: Proceedings of 3rd Annual Workshop on the Weblogging Ecosystem at the 15th International World Wide Web Conference (2006)

Mojica, L.G.: Modeling trolling in social media conversations. (2016, preprint). arXiv:1612.05310

Morstatter, F., Pfeffer, J., Liu, H., Carley, K.M.: Is the sample good enough? comparing data from Twitter’s streaming API with Twitter’s Firehose. In: International AAAI Conference on Web and Social Media (ICWSM) (2013)

Moyer, D., Carson, S.L., Dye, T.K., Carson, R.T., Goldbaum, D.: Determining the influence of Reddit posts on Wikipedia pageviews. In: Proceedings of the Ninth International AAAI Conference on Web and Social Media (2015)

Muchnik, L., Aral, S., Taylor, S.J.: Social influence bias: a randomized experiment. Science 341 (6146), 647–651 (2013)

Newell, E., Jurgens, D., Saleem, H.M., Vala, H., Sassine, J., Armstrong, C., Ruths, D.: User migration in online social networks: a case study on Reddit during a period of community unrest. In: International AAAI Conference on Web and Social Media (ICWSM), pp. 279–288 (2016)

Nishi, R., Takaguchi, T., Oka, K., Maehara, T., Toyoda, M., Kawarabayashi, K.I., Masuda, N.: Reply trees in twitter: data analysis and branching process models. Soc. Netw. Anal. Min. 6 (1), 1–13 (2016)

Saleem, H.M., Ruths, D.: The aftermath of disbanding an online hateful community (2018). Preprint. arXiv:1804.07354

Salganik, M.J., Watts, D.J.: Leading the herd astray: an experimental study of self-fulfilling prophecies in an artificial cultural market. Soc. Psychol. Quart. 71 (4), 338–355 (2008)

Sinatra, R., Lambiotte, R.: Topical issue-quantifying success. Adv. Complex Syst. 21 , 3–4 (2018)

Singer, P., Flöck, F., Meinhart, C., Zeitfogel, E., Strohmaier, M.: Evolution of Reddit: from the front page of the internet to a self-referential community? In: Proceedings of the 23rd International Conference on World Wide Web, pp. 517–522. ACM, New York (2014)

Singer, P., Ferrara, E., Kooti, F., Strohmaier, M., Lerman, K.: Evidence of online performance deterioration in user sessions on Reddit. PloS One 11 (8), e0161636 (2016)

Stoddard, G.: Popularity dynamics and intrinsic quality in Reddit and hacker news. In: International AAAI Conference on Web and Social Media (ICWSM), pp. 416–425 (2015)

Stuck_In_the_Matrix: Dataset is available on the following webpage. https://files.pushshift.io/reddit/ (Query: 2017-06-01)

Stuck_In_the_Matrix: I have every publicly available Reddit comment for research. approx. 1.7 billion comments @ 250 gb compressed. any interest in this? https://redd.it/3bxlg7 (Query: 2017-07-14)

Stuck_In_the_Matrix: Update for the Reddit corpus. https://redd.it/8aen5g (Query: 2018-09-27)

Szabo, G., Huberman, B.A.: Predicting the popularity of online content. Commun. ACM 53 (8), 80–88 (2010)

Tan, C.: Tracing community genealogy: how new communities emerge from the old. (2018, preprint). arXiv:1804.01990

Tan, C., Lee, L.: All who wander: on the prevalence and characteristics of multi-community engagement. In: Proceedings of the 24th International Conference on World Wide Web, WWW ’15, pp. 1056–1066. International World Wide Web Conferences Steering Committee, Republic and Canton of Geneva (2015)

Tsagkias, M., Weerkamp, W., De Rijke, M.: Predicting the volume of comments on online news stories. In: Proceedings of the 18th ACM Conference on Information and Knowledge Management, pp. 1765–1768. ACM, New York (2009)

Wakefield, J.: Are you scared yet? Meet Norman, the psychopathic AI. BBC News https://www.bbc.com/news/technology-44040008

Wang, C., Ye, M., Huberman, B.A.: From user comments to on-line conversations. In: Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD’12, pp. 244–252 (2012)

Zannettou, S., Caulfield, T., Blackburn, J., De Cristofaro, E., Sirivianos, M., Stringhini, G., Suarez-Tangil, G.: On the origins of memes by means of fringe web communities (2018). Preprint. arXiv:1805.12512

Zayats, V., Ostendorf, M.: Conversation modeling on Reddit using a graph-structured LSTM. Trans. Assoc. Comput. Linguist. 6 , 121–132 (2018)

Zhang, J., Hamilton, W.L., Danescu-Niculescu-Mizil, C., Jurafsky, D., Leskovec, J.: Community identity and user engagement in a multi-community landscape. In: Proceedings of the International AAAI Conference on Weblogs and Social Media, vol. 2017, p. 377. NIH Public Access (2017)

Zhao, Q., Erdogdu, M.A., He, H.Y., Rajaraman, A., Leskovec, J.: Seismic: A self-exciting point process model for predicting tweet popularity. In: Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1513–1522 (2015)

Download references

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.

Author information

Authors and affiliations.

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

You can also search for this author in PubMed   Google Scholar

Corresponding author

Correspondence to Jean-Charles Delvenne .

Editor information

Editors and affiliations.

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

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this paper

Cite this paper.

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

Download citation

DOI : https://doi.org/10.1007/978-3-030-14683-2_9

Published : 14 May 2019

Publisher Name : Springer, Cham

Print ISBN : 978-3-030-14682-5

Online ISBN : 978-3-030-14683-2

eBook Packages : Physics and Astronomy Physics and Astronomy (R0)

Share this paper

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Publish with us

Policies and ethics

• Find a journal
• Track your research

• Getting Published
• Open Research
• Communicating Research
• Life in Research
• For Editors
• For Peer Reviewers
• Research Integrity

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.

Penny Freedman is a Marketing Manager on the Author Experience & Services team based in the New York office. She works closely on sharing insight and guidance on the benefits and services available to our editors, reviewers, and authors.

• social media
• research communication
• Tools & Services
• Account Development
• Sales and account contacts
• Professional
• Press office
• Locations & Contact

We are a world leading research, educational and professional publisher. Visit our main website for more information.

• © 2024 Springer Nature
• General terms and conditions
• Your US State Privacy Rights
• Your Privacy Choices / Manage Cookies
• Accessibility
• Legal notice
• Help us to improve this site, send feedback.

Using Reddit for research

Psychology Phd Student Hannah Bowers has kindly shared her experience of using Reddit for research.

Using reddit meant users could comment on my recruitment post, and I could see positive and negative reactions to my research, both of which gave me a really great insight into why people were or weren’t taking part.

Overall my experience with reddit has been great, and I would recommend it to all researchers (so long as you have a thick skin).

Share this:

• Click to share on Facebook (Opens in new window)
• Click to share on Twitter (Opens in new window)
• Click to share on Pinterest (Opens in new window)
• Click to print (Opens in new window)
• Click to email a link to a friend (Opens in new window)
• Click to share on Reddit (Opens in new window)
• Click to share on Tumblr (Opens in new window)
• Click to share on LinkedIn (Opens in new window)

Leave a Reply Cancel reply

Main Navigation

• Contact NeurIPS
• Code of Ethics
• Code of Conduct
• Create Profile
• Journal To Conference Track
• Diversity & Inclusion
• Proceedings
• Future Meetings
• Exhibitor Information
• Privacy Policy

NeurIPS 2024

Conference Dates: (In person) 9 December - 15 December, 2024

Homepage: https://neurips.cc/Conferences/2024/

Call For Papers 

Author notification: Sep 25, 2024

Camera-ready, poster, and video submission: Oct 30, 2024 AOE

Submit at: https://openreview.net/group?id=NeurIPS.cc/2024/Conference  

The site will start accepting submissions on Apr 22, 2024 

Subscribe to these and other dates on the 2024 dates page .

The Thirty-Eighth Annual Conference on Neural Information Processing Systems (NeurIPS 2024) is an interdisciplinary conference that brings together researchers in machine learning, neuroscience, statistics, optimization, computer vision, natural language processing, life sciences, natural sciences, social sciences, and other adjacent fields. We invite submissions presenting new and original research on topics including but not limited to the following:

• Applications (e.g., vision, language, speech and audio, Creative AI)
• Deep learning (e.g., architectures, generative models, optimization for deep networks, foundation models, LLMs)
• Evaluation (e.g., methodology, meta studies, replicability and validity, human-in-the-loop)
• General machine learning (supervised, unsupervised, online, active, etc.)
• Infrastructure (e.g., libraries, improved implementation and scalability, distributed solutions)
• Machine learning for sciences (e.g. climate, health, life sciences, physics, social sciences)
• Neuroscience and cognitive science (e.g., neural coding, brain-computer interfaces)
• Optimization (e.g., convex and non-convex, stochastic, robust)
• Probabilistic methods (e.g., variational inference, causal inference, Gaussian processes)
• Reinforcement learning (e.g., decision and control, planning, hierarchical RL, robotics)
• Social and economic aspects of machine learning (e.g., fairness, interpretability, human-AI interaction, privacy, safety, strategic behavior)
• Theory (e.g., control theory, learning theory, algorithmic game theory)

Machine learning is a rapidly evolving field, and so we welcome interdisciplinary submissions that do not fit neatly into existing categories.

Authors are asked to confirm that their submissions accord with the NeurIPS code of conduct .

Formatting instructions:   All submissions must be in PDF format, and in a single PDF file include, in this order:

• The submitted paper
• Technical appendices that support the paper with additional proofs, derivations, or results 
• The NeurIPS paper checklist  

Other supplementary materials such as data and code can be uploaded as a ZIP file

The main text of a submitted paper is limited to nine content pages , including all figures and tables. Additional pages containing references don’t count as content pages. If your submission is accepted, you will be allowed an additional content page for the camera-ready version.

The main text and references may be followed by technical appendices, for which there is no page limit.

The maximum file size for a full submission, which includes technical appendices, is 50MB.

Authors are encouraged to submit a separate ZIP file that contains further supplementary material like data or source code, when applicable.

You must format your submission using the NeurIPS 2024 LaTeX style file which includes a “preprint” option for non-anonymous preprints posted online. Submissions that violate the NeurIPS style (e.g., by decreasing margins or font sizes) or page limits may be rejected without further review. Papers may be rejected without consideration of their merits if they fail to meet the submission requirements, as described in this document. 

Paper checklist: In order to improve the rigor and transparency of research submitted to and published at NeurIPS, authors are required to complete a paper checklist . The paper checklist is intended to help authors reflect on a wide variety of issues relating to responsible machine learning research, including reproducibility, transparency, research ethics, and societal impact. The checklist forms part of the paper submission, but does not count towards the page limit.

Supplementary material: While all technical appendices should be included as part of the main paper submission PDF, authors may submit up to 100MB of supplementary material, such as data, or source code in a ZIP format. Supplementary material should be material created by the authors that directly supports the submission content. Like submissions, supplementary material must be anonymized. Looking at supplementary material is at the discretion of the reviewers.

We encourage authors to upload their code and data as part of their supplementary material in order to help reviewers assess the quality of the work. Check the policy as well as code submission guidelines and templates for further details.

Use of Large Language Models (LLMs): We welcome authors to use any tool that is suitable for preparing high-quality papers and research. However, we ask authors to keep in mind two important criteria. First, we expect papers to fully describe their methodology, and any tool that is important to that methodology, including the use of LLMs, should be described also. For example, authors should mention tools (including LLMs) that were used for data processing or filtering, visualization, facilitating or running experiments, and proving theorems. It may also be advisable to describe the use of LLMs in implementing the method (if this corresponds to an important, original, or non-standard component of the approach). Second, authors are responsible for the entire content of the paper, including all text and figures, so while authors are welcome to use any tool they wish for writing the paper, they must ensure that all text is correct and original.

Double-blind reviewing:   All submissions must be anonymized and may not contain any identifying information that may violate the double-blind reviewing policy.  This policy applies to any supplementary or linked material as well, including code.  If you are including links to any external material, it is your responsibility to guarantee anonymous browsing.  Please do not include acknowledgements at submission time. If you need to cite one of your own papers, you should do so with adequate anonymization to preserve double-blind reviewing.  For instance, write “In the previous work of Smith et al. [1]…” rather than “In our previous work [1]...”). If you need to cite one of your own papers that is in submission to NeurIPS and not available as a non-anonymous preprint, then include a copy of the cited anonymized submission in the supplementary material and write “Anonymous et al. [1] concurrently show...”). Any papers found to be violating this policy will be rejected.

OpenReview: We are using OpenReview to manage submissions. The reviews and author responses will not be public initially (but may be made public later, see below). As in previous years, submissions under review will be visible only to their assigned program committee. We will not be soliciting comments from the general public during the reviewing process. Anyone who plans to submit a paper as an author or a co-author will need to create (or update) their OpenReview profile by the full paper submission deadline. Your OpenReview profile can be edited by logging in and clicking on your name in https://openreview.net/ . This takes you to a URL "https://openreview.net/profile?id=~[Firstname]_[Lastname][n]" where the last part is your profile name, e.g., ~Wei_Zhang1. The OpenReview profiles must be up to date, with all publications by the authors, and their current affiliations. The easiest way to import publications is through DBLP but it is not required, see FAQ . Submissions without updated OpenReview profiles will be desk rejected. The information entered in the profile is critical for ensuring that conflicts of interest and reviewer matching are handled properly. Because of the rapid growth of NeurIPS, we request that all authors help with reviewing papers, if asked to do so. We need everyone’s help in maintaining the high scientific quality of NeurIPS.  

Please be aware that OpenReview has a moderation policy for newly created profiles: New profiles created without an institutional email will go through a moderation process that can take up to two weeks. New profiles created with an institutional email will be activated automatically.

Venue home page: https://openreview.net/group?id=NeurIPS.cc/2024/Conference

If you have any questions, please refer to the FAQ: https://openreview.net/faq

Ethics review: Reviewers and ACs may flag submissions for ethics review . Flagged submissions will be sent to an ethics review committee for comments. Comments from ethics reviewers will be considered by the primary reviewers and AC as part of their deliberation. They will also be visible to authors, who will have an opportunity to respond.  Ethics reviewers do not have the authority to reject papers, but in extreme cases papers may be rejected by the program chairs on ethical grounds, regardless of scientific quality or contribution.  

Preprints: The existence of non-anonymous preprints (on arXiv or other online repositories, personal websites, social media) will not result in rejection. If you choose to use the NeurIPS style for the preprint version, you must use the “preprint” option rather than the “final” option. Reviewers will be instructed not to actively look for such preprints, but encountering them will not constitute a conflict of interest. Authors may submit anonymized work to NeurIPS that is already available as a preprint (e.g., on arXiv) without citing it. Note that public versions of the submission should not say "Under review at NeurIPS" or similar.

Dual submissions: Submissions that are substantially similar to papers that the authors have previously published or submitted in parallel to other peer-reviewed venues with proceedings or journals may not be submitted to NeurIPS. Papers previously presented at workshops are permitted, so long as they did not appear in a conference proceedings (e.g., CVPRW proceedings), a journal or a book.  NeurIPS coordinates with other conferences to identify dual submissions.  The NeurIPS policy on dual submissions applies for the entire duration of the reviewing process.  Slicing contributions too thinly is discouraged.  The reviewing process will treat any other submission by an overlapping set of authors as prior work. If publishing one would render the other too incremental, both may be rejected.

Anti-collusion: NeurIPS does not tolerate any collusion whereby authors secretly cooperate with reviewers, ACs or SACs to obtain favorable reviews. 

Author responses:   Authors will have one week to view and respond to initial reviews. Author responses may not contain any identifying information that may violate the double-blind reviewing policy. Authors may not submit revisions of their paper or supplemental material, but may post their responses as a discussion in OpenReview. This is to reduce the burden on authors to have to revise their paper in a rush during the short rebuttal period.

After the initial response period, authors will be able to respond to any further reviewer/AC questions and comments by posting on the submission’s forum page. The program chairs reserve the right to solicit additional reviews after the initial author response period.  These reviews will become visible to the authors as they are added to OpenReview, and authors will have a chance to respond to them.

After the notification deadline, accepted and opted-in rejected papers will be made public and open for non-anonymous public commenting. Their anonymous reviews, meta-reviews, author responses and reviewer responses will also be made public. Authors of rejected papers will have two weeks after the notification deadline to opt in to make their deanonymized rejected papers public in OpenReview.  These papers are not counted as NeurIPS publications and will be shown as rejected in OpenReview.

Publication of accepted submissions:   Reviews, meta-reviews, and any discussion with the authors will be made public for accepted papers (but reviewer, area chair, and senior area chair identities will remain anonymous). Camera-ready papers will be due in advance of the conference. All camera-ready papers must include a funding disclosure . We strongly encourage accompanying code and data to be submitted with accepted papers when appropriate, as per the code submission policy . Authors will be allowed to make minor changes for a short period of time after the conference.

Contemporaneous Work: For the purpose of the reviewing process, papers that appeared online within two months of a submission will generally be considered "contemporaneous" in the sense that the submission will not be rejected on the basis of the comparison to contemporaneous work. Authors are still expected to cite and discuss contemporaneous work and perform empirical comparisons to the degree feasible. Any paper that influenced the submission is considered prior work and must be cited and discussed as such. Submissions that are very similar to contemporaneous work will undergo additional scrutiny to prevent cases of plagiarism and missing credit to prior work.

Plagiarism is prohibited by the NeurIPS Code of Conduct .

Other Tracks: Similarly to earlier years, we will host multiple tracks, such as datasets, competitions, tutorials as well as workshops, in addition to the main track for which this call for papers is intended. See the conference homepage for updates and calls for participation in these tracks. 

Experiments: As in past years, the program chairs will be measuring the quality and effectiveness of the review process via randomized controlled experiments. All experiments are independently reviewed and approved by an Institutional Review Board (IRB).

Financial Aid: Each paper may designate up to one (1) NeurIPS.cc account email address of a corresponding student author who confirms that they would need the support to attend the conference, and agrees to volunteer if they get selected. To be considered for Financial the student will also need to fill out the Financial Aid application when it becomes available.

Reshoring, Automation, and Labor Markets Under Trade Uncertainty

Hamid Firooz

Download PDF (864 KB)

2024-16 | May 8, 2024

We study the implications of trade uncertainty for reshoring, automation, and U.S. labor markets. Rising trade uncertainty creates incentive for firms to reduce exposures to foreign suppliers by moving production and distribution processes to domestic producers. However, we argue that reshoring does not necessarily bring jobs back to the home country or boost domestic wages, especially when firms have access to labor-substituting technologies such as automation. Automation improves labor productivity and facilitates reshoring, but it can also displace jobs. Furthermore, automation poses a threat that weakens the bargaining power of low-skilled workers in wage negotiations, depressing their wages and raising the skill premium and wage inequality. The model predictions are in line with industry-level empirical evidence.

We use cookies to provide you with the best possible experience on our website

Visit our cookies policy for more information about cookies and privacy. Manage your cookie settings here … Read more

On May 2018, we published revised versions of the OMRON Privacy Policy and Cookie Policy. Please read these updated terms by clicking here . Your use of our products and services is subject to these revised terms

• Global - English
• Global - Deutsch
• Global - Español
• Global - Français
• Asia Pacific - English
• Brasil - Português
• Japan - 日本語
• Korea - 한국어

• Global News

OMRON SINIC X Six Research Papers Accepted for ICRA 2024, Top-Tier Conference on Robotics

• May 10, 2024

OMRON SINIC X Corporation (Headquarters: Bunkyo-ku, Tokyo; President and CEO: Masaki Suwa; hereinafter referred to as "OSX") is pleased to announce that six of its research papers have been accepted for publication at the 2024 IEEE International Conference on Robots and Automation (ICRA 2024).

ICRA is one of the largest and most influential international conferences on robotics and automation. A total of 1,765 papers (about 45%) were accepted out of 3,937 submissions in 2024. The conference will be held in Yokohama, Japan, from May 13 to 17, 2024. Our papers present research robots that understand work intentions based on linguistic and scene observations, move flexibly like humans, or autonomously cooperate with other robots. For details, please confirm the link of each paper accepted for ICRA 2024.

■Paper accepted for ICRA 2024 *All presentation times are in local time *Affiliation at the time of submission

1. Vision-Language Interpreter for Robot Task Planning

2. An Electromagnetism-Inspired Method for Estimating In-Grasp Torque from Visuotactile Sensors

3. SliceIt! - A Dual Simulator Framework for Learning Robot Food Slicing

4. When to Replan? an Adaptive Replanning Strategy for Autonomous Navigation Using Deep Reinforcement Learning

5. Symmetry-aware Reinforcement Learning for Robotic Assembly under Partial Observability with a Soft Wrist

6. Benchmarking Actor-Critic Deep Reinforcement Learning Algorithms for Robotics Control with Action Constraints

For a list of OSX's publications, please visit https://www.omron.com/global/en/technology/publications/?affiliation=%5B%22OSX%22%5D

About OMRON SINIC X Corporation OMRON SINIC X Corporation is a strategic subsidiary seeking to realize the "near future designs" that OMRON forecasts. It is comprised of researchers with cutting-edge knowledge and experience across a wide range of technological domains, including AI, Robotics, IoT, and sensing. With the aim of addressing social issues, OSX integrates innovative technologies with business models and strategies in technology and IP to create near-future designs. Additionally, the company accelerates the creation of these designs through collaborative research with universities and external research institutions. For more details, please refer to https://www.omron.com/sinicx/en/

Adobe Acrobat Reader is free software that lets you view and print Adobe Portable Document Format (PDF) files.

Help | Advanced Search

Computer Science > Machine Learning

Title: kan: kolmogorov-arnold networks.

Abstract: Inspired by the Kolmogorov-Arnold representation theorem, we propose Kolmogorov-Arnold Networks (KANs) as promising alternatives to Multi-Layer Perceptrons (MLPs). While MLPs have fixed activation functions on nodes ("neurons"), KANs have learnable activation functions on edges ("weights"). KANs have no linear weights at all -- every weight parameter is replaced by a univariate function parametrized as a spline. We show that this seemingly simple change makes KANs outperform MLPs in terms of accuracy and interpretability. For accuracy, much smaller KANs can achieve comparable or better accuracy than much larger MLPs in data fitting and PDE solving. Theoretically and empirically, KANs possess faster neural scaling laws than MLPs. For interpretability, KANs can be intuitively visualized and can easily interact with human users. Through two examples in mathematics and physics, KANs are shown to be useful collaborators helping scientists (re)discover mathematical and physical laws. In summary, KANs are promising alternatives for MLPs, opening opportunities for further improving today's deep learning models which rely heavily on MLPs.

Submission history

Access paper:.

• Other Formats

References & Citations

• Google Scholar
• Semantic Scholar

BibTeX formatted citation

Bibliographic and Citation Tools

Code, data and media associated with this article, recommenders and search tools.

• Institution

arXivLabs: experimental projects with community collaborators

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.

Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.

Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs .

• Zomato Introduces Customised Photo Cakes Ahead Of Mother's Day Celebration
• Woman In Italy Falls To Death After Slipping Out Of Zipline Safety Harness
• Tamil Nadu Board Announces Class 10 Result, 91.55% Students Qualify Exam
• US Cop Enters Wrong Apartment, Shoots Dead Black Air Force Officer
• X Users Can Now Post Full-Length Movies And Earn Money
• Change Font Size A A
• Change Language हिंदी | Hindi বাঙালি | Bengali தமிழ் | Tamil
• Focus on Story
• Dark Theme Light Theme

NEET UG 2024: Exam Body Denies Paper Leak Allegations, Calls Them Baseless

Neet ug 2024: except for an incident in rajasthan, where some students forcibly took away question papers before the exam's conclusion, the nta clarified that all other circulated photos of papers have no relation to the actual exam paper..

NEET UG 2024: The National Testing Agency (NTA) has refuted the claims of a paper leak in the National Eligibility-cum-Entrance Test (Undergraduate) (NEET UG) 2024 held on Sunday, terming them as "completely baseless and without any merit".

"It has been ascertained from NTA's security protocols and Standard Operating Procedures that the social media posts pointing towards any paper leak are completely baseless and without any substance," the NTA said in an official release.

The exam body stated that except for an incident in Rajasthan's Sawai Madhopur where some students forcibly took away the question papers before the conclusion of the examination, all other photos of papers circulating on social media have no relation to the actual paper.

The NTA mentioned instances of malpractice and impersonation cases, stating that stringent actions were taken against impersonators and candidates.

Many people have been arrested in various cities across the country for allegedly appearing in NEET UG 2024 in place of original candidates.

"NTA also conducts post-exam data analysis to detect cases of Unfair Means (UFM). Action on UFM cases is taken as per extant rules, which include cancellation of candidature and debarring from future examinations," the exam body stated.

This year, over 24 lakh students, the highest ever, registered for NEET UG 2024. The examinees include over 10 lakh male students and over 13 lakh female students.

The NTA attributed the increase in the number of students appearing in the exam to the option of several smaller cities as centers, especially in the Northeast, apart from Puducherry, Jammu and Kashmir, and other states.

The NTA urges candidates, their parents, teachers, and all concerned not to pay heed to any rumors and to concentrate on forthcoming examinations.

The NEET (UG) 2024 was conducted across 4,750 centres in 571 cities (including 14 cities abroad) on May 5.

• Notifications
• Web Stories
• TV Schedule
• Big Bonus REGISTER NOW
• मध्य प्रदेश