pith. sign in

arxiv: 2607.00854 · v1 · pith:OF7QWCXLnew · submitted 2026-07-01 · 💻 cs.SI · cs.CY· cs.HC

A field experiment of social influence and behavioral contagion with bots on Reddit

Hiroki Oda , Kinga Makovi , Taha Yasseri , Milena Tsvetkova This is my paper

Pith reviewed 2026-07-02 03:05 UTC · model grok-4.3

classification 💻 cs.SI cs.CYcs.HC
keywords social influencebehavioral contagionfield experimentbotsRedditawardsonline communitiesuser activity
0
0 comments X

The pith

Awards on Reddit do not boost user activity or impact and can reduce both when issued randomly by bots.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper describes a field experiment that sent symbolic awards to Reddit users from accounts that appeared either human or bot. Each award came with one of four explanations: praise for logical argument, emotional sensitivity, moral integrity, or a random lottery draw. The study tracked how these awards changed the recipients' posting volume, impact, content, and interactions with others. Results showed no rise in activity or influence from any awards, with random bot awards linked to lower activity, yet all awards increased direct replies between users. This pattern suggests online users may resist basic reward signals from artificial agents while remaining open to prompts that encourage conversation.

Core claim

In the field experiment, apparent human and bot accounts issued awards with rationales praising logical argument, emotional sensitivity, moral integrity, or a random lottery. These awards produced no increase in recipients' activity levels or downstream impact on the platform. Awards from bots justified by lottery in fact lowered activity and impact. Across conditions, however, the awards increased direct communication between users. The findings point to resilience against simple behavioral stimuli from artificial agents but underscore the importance of transparent bot labeling for platform governance.

What carries the argument

The field experiment that assigns award givers (apparent human or bot) and rationales (logic, emotion, moral, or lottery) to measure subsequent changes in recipient behavior volume, impact, and communication.

If this is right

  • Simple positive awards from bots or humans do not increase user activity or platform impact on Reddit.
  • Awards justified as random lottery draws from bots can decrease user activity and impact.
  • Awards of any type increase direct communication and replies between users.
  • Users appear more resilient to basic reward manipulation than to schemes that simulate ongoing human conversation.
  • Transparent labeling of automated agents supports ethical platform governance.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • If the pattern holds, platforms that use bot-driven reward systems for engagement may achieve little or no net gain and risk net losses in some cases.
  • Testing the same award design on other platforms could reveal whether the observed resilience is specific to Reddit's culture or more general.
  • More advanced AI interactions that sustain conversation rather than deliver one-time awards might overcome the resistance seen here.

Load-bearing premise

Recipients must interpret the accounts and award rationales exactly as the experiment intended, with any behavior shifts caused by the awards rather than other platform factors.

What would settle it

A replication experiment in which bot-issued lottery awards produce no reduction in recipient activity or impact would falsify the reported negative effect.

Figures

Figures reproduced from arXiv: 2607.00854 by Hiroki Oda, Kinga Makovi, Milena Tsvetkova, Taha Yasseri.

Figure 1
Figure 1. Figure 1: The direct effects of award account and rationale on the award recipient’s A) private messages and B) number and C) combined text length of new contributions made in the 12 hours after receiving the award. Users are more likely to reply to a human account than a bot account and when sent by a human account, the random-lottery rationale elicits fewer replies than the other rationales. Awards do not appear t… view at source ↗
Figure 2
Figure 2. Figure 2: The direct effects of award account and rationale on the A) positive tone B) analytic language, C) emotion-related language, and D) moral language of the contributions the award recipient made in the 12 hours after receiving the award. Awards do not appear to change the content in posts. The only statistically significant effect is that awards by a bot account with a random-lottery rationale decrease the p… view at source ↗
Figure 3
Figure 3. Figure 3: The indirect effects of award account and rationale on the A) total votes and the B) number and C) combined text length of the comments/replies to contribu￾tions the award recipient made in the 12 hours after receiving the award. Awards increase the impact of the award recipient’s subsequent contributions only when they come from human accounts with the moral rationale. The figure shows mean estimates and … view at source ↗
read the original abstract

Recent advances in AI have heightened scholars' and policy makers' concern with social influence and behavioral contagion in online communities. We conduct a field experiment on Reddit to investigate the extent to which online users are susceptible to positive behavioral stimuli from other users and artificial agents. We let apparent human and bot accounts give symbolic awards to users with one of four rationales: praising the recipient's logical argument, emotional sensitivity, or moral integrity, or explaining that the award resulted from a random draw in a lottery. We evaluate how the different rationales for the award affect the recipients' subsequent behavior on the platform in terms of volume, impact, and content, as well as the further behavioral contagion to other users. We find that awards do not increase user activity and downstream impact, and awards from bots with the lottery rationale can in fact reduce them. Nevertheless, awards encourage direct communication between users. These findings highlight the possible resilience of online users to simple behavioral manipulation from platform algorithms and artificial agents, but not necessarily to more sophisticated schemes that simulate human conversation. Transparently labeling automated agents remains essential for ethical and effective platform governance.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The manuscript reports a field experiment on Reddit in which accounts presented as humans or bots delivered symbolic awards to users accompanied by one of four rationales (praising logical argument, emotional sensitivity, moral integrity, or a random lottery draw). The authors track recipients' subsequent posting volume, downstream impact (e.g., upvotes, comments received), content characteristics, and direct communication with other users. They report that awards do not increase activity or impact overall, that lottery awards from bot accounts can reduce these metrics, and that awards of any type increase direct user-to-user communication. The authors interpret the results as evidence of user resilience to simple behavioral stimuli from artificial agents while underscoring the importance of transparent bot labeling.

Significance. If the causal attribution holds, the study supplies rare field-experimental evidence on the boundary conditions of social influence and behavioral contagion in large online platforms. It directly informs debates on platform governance and AI ethics by showing that low-effort award mechanisms may fail to produce the feared manipulation effects, while also documenting a positive spillover into interpersonal communication. The design's use of real platform users and observable behavioral outcomes strengthens external validity relative to lab or survey studies.

major comments (2)
  1. [§3, §4.1] §3 (Experimental Design) and §4.1 (Manipulation and Randomization): The headline differential effect—that lottery-bot awards reduce activity and impact while other conditions do not—requires that recipients correctly perceived the account type (bot vs. human) and attributed the award to the stated rationale rather than platform noise or self-selection. No manipulation check, post-award survey, or validation of perceived account type is reported. Without this, the observed deltas cannot be unambiguously attributed to the experimental factors.
  2. [§4.2, Table 2] §4.2 (Outcome Measurement) and Table 2: The claim that awards 'encourage direct communication' is presented as a robust secondary finding, yet the manuscript does not report whether this increase survives correction for multiple comparisons across the four rationales and two account types, nor whether it is driven by a small number of high-activity users. If the communication effect is the only positive result, its statistical robustness is load-bearing for the overall interpretation.
minor comments (2)
  1. [Abstract, §4.3] The abstract states that 'awards from bots with the lottery rationale can in fact reduce them,' but the corresponding results section should explicitly state the effect size, confidence interval, and sample size for this comparison to allow readers to judge practical significance.
  2. [Figure 1] Figure 1 (or equivalent timeline figure) would benefit from clearer labeling of the pre- and post-award windows and any platform-wide events that occurred during data collection.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for these constructive comments on causal attribution and statistical robustness. We address each point below.

read point-by-point responses

  1. Referee: [§3, §4.1] §3 (Experimental Design) and §4.1 (Manipulation and Randomization): The headline differential effect—that lottery-bot awards reduce activity and impact while other conditions do not—requires that recipients correctly perceived the account type (bot vs. human) and attributed the award to the stated rationale rather than platform noise or self-selection. No manipulation check, post-award survey, or validation of perceived account type is reported. Without this, the observed deltas cannot be unambiguously attributed to the experimental factors.

    Authors: We acknowledge this as a genuine limitation for unambiguous causal attribution. Conducting post-experiment surveys on Reddit would have violated platform terms regarding unsolicited user contact and risked contaminating the naturalistic setting. Account profiles were constructed with explicit bot indicators visible to recipients, and randomization was used to balance conditions. We will add an explicit limitations subsection discussing the lack of direct perception validation and its implications for interpreting the bot-lottery condition. revision: partial

  2. Referee: [§4.2, Table 2] §4.2 (Outcome Measurement) and Table 2: The claim that awards 'encourage direct communication' is presented as a robust secondary finding, yet the manuscript does not report whether this increase survives correction for multiple comparisons across the four rationales and two account types, nor whether it is driven by a small number of high-activity users. If the communication effect is the only positive result, its statistical robustness is load-bearing for the overall interpretation.

    Authors: We agree that these robustness checks are necessary. In revision we will apply Bonferroni (or FDR) correction to the communication-effect tests across the eight conditions and add sensitivity analyses that drop the top 1% and 5% most active users. Updated results and tables will be included in the main text or supplementary materials. revision: yes

Circularity Check

0 steps flagged

No circularity: empirical field experiment with measured outcomes only

full rationale

This is a field experiment reporting measured behavioral changes after awards with different rationales from human vs. bot accounts. No derivations, equations, fitted parameters, or predictions are present; results are direct empirical observations compared across conditions. No self-citation chains or ansatzes support any load-bearing claim. The paper is self-contained against external benchmarks as a standard randomized intervention study.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 0 invented entities

The central claim rests on standard social-science assumptions about causal identification in field experiments and user perception of account authenticity; no free parameters or invented entities are introduced.

axioms (2)
  • domain assumption Recipients perceive the experimental accounts as either human or bot according to the design labels.
    Required for interpreting differential effects between human and bot conditions.
  • domain assumption Changes in posting volume, impact, and communication are attributable to the award and its rationale rather than platform-wide trends or user self-selection.
    Core to causal interpretation of the field experiment.

pith-pipeline@v0.9.1-grok · 5739 in / 1253 out tokens · 42058 ms · 2026-07-02T03:05:41.358011+00:00 · methodology

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Reference graph

Works this paper leans on

61 extracted references · 4 canonical work pages

  1. [1]

    Social AI and the challenges of the human-AI ecosystem, June

    Dino Pedreschi, Luca Pappalardo, Ricardo Baeza-Yates, Albert-Laszlo Barabasi, Frank Dignum, Virginia Dignum, Tina Eliassi-Rad, Fosca Giannotti, Janos Kertesz, Alistair Knott, Yannis Ioannidis, Paul Lukowicz, Andrea Passarella, Alex Sandy Pentland, John Shawe-Taylor, and Alessandro Vespignani. Social AI and the challenges of the human-AI ecosystem, June

  2. [2]

    arXiv:2306.13723 [cs]

    work page arXiv

  3. [3]

    Crandall, Nicholas A

    Iyad Rahwan, Manuel Cebrian, Nick Obradovich, Josh Bongard, Jean-Fran¸ cois Bonnefon, Cyn- thia Breazeal, Jacob W. Crandall, Nicholas A. Christakis, Iain D. Couzin, Matthew O. Jackson, Nicholas R. Jennings, Ece Kamar, Isabel M. Kloumann, Hugo Larochelle, David Lazer, Richard McElreath, Alan Mislove, David C. Parkes, Alex ‘Sandy’ Pentland, Margaret E. Robe...

    2019

  4. [4]

    A new sociology of humans and machines.Nature Human Behaviour, 8(10):1864–1876, October 2024

    Milena Tsvetkova, Taha Yasseri, Niccolo Pescetelli, and Tobias Werner. A new sociology of humans and machines.Nature Human Behaviour, 8(10):1864–1876, October 2024

    2024

  5. [5]

    Normative equivalence in human-AI coop- eration: Behaviour, not identity, drives cooperation in mixed-agent groups, January 2026

    Nico Mutzner, Taha Yasseri, and Heiko Rauhut. Normative equivalence in human-AI coop- eration: Behaviour, not identity, drives cooperation in mixed-agent groups, January 2026. arXiv:2601.20487 [cs]

    work page arXiv 2026

  6. [6]

    Clifford Nass, Jonathan Steuer, and Ellen R. Tauber. Computers are social actors. InPro- ceedings of the SIGCHI Conference on Human Factors in Computing Systems, CHI ’94, pages 72–78, New York, NY, USA, April 1994. Association for Computing Machinery

    1994

  7. [7]

    Machines and Mindlessness: Social Responses to Computers

    Clifford Nass and Youngme Moon. Machines and Mindlessness: Social Responses to Computers. Journal of Social Issues, 56(1):81–103, 2000

    2000

  8. [8]

    We and It: An interdisciplinary review of the experimen- tal evidence on how humans interact with machines.Journal of Behavioral and Experimental Economics, 99:101897, August 2022

    Marina Chugunova and Daniela Sele. We and It: An interdisciplinary review of the experimen- tal evidence on how humans interact with machines.Journal of Behavioral and Experimental Economics, 99:101897, August 2022

    2022

  9. [9]

    Rewards and punishments help humans overcome biases against cooperation partners assumed to be machines.iScience, 28(7), July 2025

    Kinga Makovi, Jean-Fran¸ cois Bonnefon, Mayada Oudah, Anahit Sargsyan, and Talal Rahwan. Rewards and punishments help humans overcome biases against cooperation partners assumed to be machines.iScience, 28(7), July 2025

    2025

  10. [10]

    Strategic interactions between humans and artificial intelligence: Lessons from experiments with computer players.Journal of Economic Psychology, 87:102426, Decem- ber 2021

    Christoph March. Strategic interactions between humans and artificial intelligence: Lessons from experiments with computer players.Journal of Economic Psychology, 87:102426, Decem- ber 2021

    2021

  11. [11]

    Gray, Kurt Gray, and Daniel M

    Heather M. Gray, Kurt Gray, and Daniel M. Wegner. Dimensions of mind perception.Science, 315(5812):619–619, February 2007

    2007

  12. [12]

    Can machines think? Interaction and perspective taking with robots investigated via fMRI.PLOS ONE, 3(7):e2597, July 2008

    S¨ oren Krach, Frank Hegel, Britta Wrede, Gerhard Sagerer, Ferdinand Binkofski, and Tilo Kircher. Can machines think? Interaction and perspective taking with robots investigated via fMRI.PLOS ONE, 3(7):e2597, July 2008. 11

    2008

  13. [13]

    A functional imaging study of cooperation in two-person reciprocal exchange.Proceedings of the National Academy of Sciences, 98(20):11832–11835, September 2001

    Kevin McCabe, Daniel Houser, Lee Ryan, Vernon Smith, and Theodore Trouard. A functional imaging study of cooperation in two-person reciprocal exchange.Proceedings of the National Academy of Sciences, 98(20):11832–11835, September 2001

    2001

  14. [14]

    Jingling Zhang, Jane Conway, and C´ esar A. Hidalgo. Why do people judge humans differently from machines? The role of agency and experience, October 2022. arXiv:2210.10081 [cs]

    work page arXiv 2022

  15. [15]

    Hidalgo, Diana Orghian, Jordi Albo Canals, Filipa De Almeida, and Natalia Martin

    Cesar A. Hidalgo, Diana Orghian, Jordi Albo Canals, Filipa De Almeida, and Natalia Martin. How Humans Judge Machines. MIT Press, February 2021

    2021

  16. [16]

    Rise of the machines: Delegating decisions to autonomous AI.Computers in Human Behavior, 134:107308, September 2022

    Cindy Candrian and Anne Scherer. Rise of the machines: Delegating decisions to autonomous AI.Computers in Human Behavior, 134:107308, September 2022

    2022

  17. [17]

    Algorithm aversion: people erro- neously avoid algorithms after seeing them err.Journal of Experimental Psychology: General, 144(1):114–126, February 2015

    Berkeley J Dietvorst, Joseph P Simmons, and Cade Massey. Algorithm aversion: people erro- neously avoid algorithms after seeing them err.Journal of Experimental Psychology: General, 144(1):114–126, February 2015

    2015

  18. [18]

    Marc T. P. Adam, Timm Teubner, and Henner Gimpel. No rage against the machine: How computer agents mitigate human emotional processes in electronic negotiations.Group Decision and Negotiation, 27(4):543–571, August 2018

    2018

  19. [19]

    Schniter, T

    E. Schniter, T. W. Shields, and D. Sznycer. Trust in humans and robots: Economically similar but emotionally different.Journal of Economic Psychology, 78:102253, June 2020

    2020

  20. [20]

    Bad machines corrupt good morals

    Nils K¨ obis, Jean-Fran¸ cois Bonnefon, and Iyad Rahwan. Bad machines corrupt good morals. Nature Human Behaviour, 5(6):679–685, June 2021

    2021

  21. [21]

    How users reciprocate to computers: an experiment that demon- strates behavior change

    BJ Fogg and Clifford Nass. How users reciprocate to computers: an experiment that demon- strates behavior change. InCHI ’97 Extended Abstracts on Human Factors in Computing Systems, CHI EA ’97, pages 331–332, New York, NY, USA, March 1997. Association for Com- puting Machinery

    1997

  22. [22]

    Spence, and Ashleigh K

    Chad Edwards, Autumn Edwards, Patric R. Spence, and Ashleigh K. Shelton. Is that a bot running the social media feed? Testing the differences in perceptions of communication quality for a human agent and a bot agent on Twitter.Computers in Human Behavior, 33:372–376, April 2014

    2014

  23. [23]

    AI’s assigned gender affects human-AI cooperation.iScience, 28(12), December 2025

    Sepideh Bazazi, Jurgis Karpus, and Taha Yasseri. AI’s assigned gender affects human-AI cooperation.iScience, 28(12), December 2025

    2025

  24. [24]

    Gender bias in perception of human managers extends to AI managers.Computers in Human Behavior Reports, 21:100984, March 2026

    Hao Cui and Taha Yasseri. Gender bias in perception of human managers extends to AI managers.Computers in Human Behavior Reports, 21:100984, March 2026

    2026

  25. [25]

    Norton.Persuasive Robotics: The Influ- ence of Robot Gender on Human Behavior

    Mikey Siegel, Cynthia Lynn Breazeal, and Michael I. Norton.Persuasive Robotics: The Influ- ence of Robot Gender on Human Behavior. Institute of Electrical and Electronics Engineers / Robotics Society of Japan, October 2009. 12

    2009

  26. [26]

    Nicole Salomons, Michael Van Der Linden, Sarah Strohkorb Sebo, and Brian Scassellati. Hu- mans conform to robots: Disambiguating trust, truth, and conformity.Proceedings of the 2018 ACM/IEEE International Conference on Human-Robot Interaction, pages 187–195, February 2018

    2018

  27. [27]

    A minority of one against a majority of robots: Robots cause normative and informational conformity.ACM Transactions on Human-Robot Interaction (THRI), 10(2):1–22, February 2021

    Nicole Salomons, Sarah Strohkorb Sebo, Meiying Qin, and Brian Scassellati. A minority of one against a majority of robots: Robots cause normative and informational conformity.ACM Transactions on Human-Robot Interaction (THRI), 10(2):1–22, February 2021

    2021

  28. [28]

    ChatGPT’s inconsistent moral advice influences users’ judgment.Scientific Reports, 13(1):4569, April 2023

    Sebastian Kr¨ ugel, Andreas Ostermaier, and Matthias Uhl. ChatGPT’s inconsistent moral advice influences users’ judgment.Scientific Reports, 13(1):4569, April 2023

    2023

  29. [29]

    K¨ obis, Rainer Michael Rilke, Marloes Hagens, and Bernd Irlenbusch

    Margarita Leib, Nils C. K¨ obis, Rainer Michael Rilke, Marloes Hagens, and Bernd Irlenbusch. The corruptive force of AI-generated advice, February 2021. arXiv:2102.07536 [cs, econ, q-fin]

    work page arXiv 2021

  30. [30]

    Ming-Cheng Ma and John P. Lalor. An empirical analysis of human-bot interaction on Reddit. InProceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020), pages 101–106, Online, November 2020. Association for Computational Linguistics

    2020

  31. [31]

    Eric Bogert, Aaron Schecter, and Richard T. Watson. Humans rely more on algorithms than social influence as a task becomes more difficult.Scientific Reports, 11(1):8028, April 2021

    2021

  32. [32]

    Logg, Julia A

    Jennifer M. Logg, Julia A. Minson, and Don A. Moore. Algorithm appreciation: People prefer algorithmic to human judgment.Organizational Behavior and Human Decision Processes, 151:90–103, March 2019

    2019

  33. [33]

    Burton, Mari-Klara Stein, and Tina Blegind Jensen

    Jason W. Burton, Mari-Klara Stein, and Tina Blegind Jensen. A systematic review of algorithm aversion in augmented decision making.Journal of Behavioral Decision Making, 33(2):220–239, 2020

    2020

  34. [34]

    Hasan Mahmud, A. K. M. Najmul Islam, Syed Ishtiaque Ahmed, and Kari Smolander. What influences algorithmic decision-making? A systematic literature review on algorithm aversion. Technological Forecasting and Social Change, 175:121390, February 2022

    2022

  35. [35]

    Milena Tsvetkova and Michael W. Macy. The social contagion of generosity.PLoS ONE, 9(2):e87275, February 2014

    2014

  36. [36]

    Pescetelli, D

    N. Pescetelli, D. Barkoczi, and M. Cebrian. Bots influence opinion dynamics without direct human-bot interaction: the mediating role of recommender systems.Applied Network Science, 7(1):1–19, December 2022

    2022

  37. [37]

    Fowler and Nicholas A

    James H. Fowler and Nicholas A. Christakis. Cooperative behavior cascades in human social networks.Proceedings of the National Academy of Sciences, 107(12):5334 –5338, March 2010

    2010

  38. [38]

    Adamic, and Bernardo A

    Jure Leskovec, Lada A. Adamic, and Bernardo A. Huberman. The dynamics of viral marketing. ACM Transactions on the Web, 1(1):5–es, May 2007. 13

    2007

  39. [39]

    Duncan J. Watts. A simple model of global cascades on random networks.Proceedings of the National Academy of Sciences, 99(9):5766–5771, April 2002

    2002

  40. [40]

    Keijzer and Michael M¨ as

    Marijn A. Keijzer and Michael M¨ as. The strength of weak bots.Online Social Networks and Media, 21:100106, January 2021

    2021

  41. [41]

    Bj¨ orn Ross, Laura Pilz, Benjamin Cabrera, Florian Brachten, German Neubaum, and Stefan Stieglitz. Are social bots a real threat? An agent-based model of the spiral of silence to analyse the impact of manipulative actors in social networks.European Journal of Information Systems, 28(4):394–412, July 2019

    2019

  42. [42]

    The ripple effects of vulnerability: The effects of a robot’s vulnerable behavior on trust in human-robot teams

    Sarah Strohkorb Sebo, Margaret Traeger, Malte Jung, and Brian Scassellati. The ripple effects of vulnerability: The effects of a robot’s vulnerable behavior on trust in human-robot teams. InProceedings of the 2018 ACM/IEEE International Conference on Human-Robot Interaction, HRI ’18, pages 178–186, New York, NY, USA, February 2018. Association for Computi...

    2018

  43. [43]

    Traeger, Sarah Strohkorb Sebo, Malte Jung, Brian Scassellati, and Nicholas A

    Margaret L. Traeger, Sarah Strohkorb Sebo, Malte Jung, Brian Scassellati, and Nicholas A. Christakis. Vulnerable robots positively shape human conversational dynamics in a hu- man–robot team.Proceedings of the National Academy of Sciences, 117(12):6370–6375, March 2020

    2020

  44. [44]

    The spread of low-credibility content by social bots.Nature Com- munications, 9(1):4787, November 2018

    Chengcheng Shao, Giovanni Luca Ciampaglia, Onur Varol, Kai-Cheng Yang, Alessandro Flam- mini, and Filippo Menczer. The spread of low-credibility content by social bots.Nature Com- munications, 9(1):4787, November 2018

    2018

  45. [45]

    Influence of augmented humans in online interactions during voting events.PLOS ONE, 14(5):e0214210, May 2019

    Massimo Stella, Marco Cristoforetti, and Manlio De Domenico. Influence of augmented humans in online interactions during voting events.PLOS ONE, 14(5):e0214210, May 2019

    2019

  46. [46]

    The spread of true and false news online.Science, 359(6380):1146–1151, March 2018

    Soroush Vosoughi, Deb Roy, and Sinan Aral. The spread of true and false news online.Science, 359(6380):1146–1151, March 2018

    2018

  47. [47]

    Massanari

    Adrienne L. Massanari. Contested play: The culture and politics of Reddit bots. InSocialbots and Their Friends. Routledge, 2016

    2016

  48. [48]

    Bot detection in Reddit political discus- sion

    Sofia Hurtado, Poushali Ray, and Radu Marculescu. Bot detection in Reddit political discus- sion. InProceedings of the Fourth International Workshop on Social Sensing, SocialSense’19, pages 30–35, New York, NY, USA, April 2019. Association for Computing Machinery

    2019

  49. [49]

    Stuart Geiger

    R. Stuart Geiger. The lives of bots. In Geert Lovink and Nathaniel Tkacz, editors,Critical Point of View: A Wikipedia Reader, pages 78–93. Institute of Network Cultures, Amsterdam, 2011

    2011

  50. [50]

    Bots and cyborgs: Wikipedia’s immune system.Computer, 45(03):79–82, 2012

    Aaron Halfaker and John Riedl. Bots and cyborgs: Wikipedia’s immune system.Computer, 45(03):79–82, 2012

    2012

  51. [51]

    The rise of social bots.Communications of the ACM, 59(7):96–104, June 2016

    Emilio Ferrara, Onur Varol, Clayton Davis, Filippo Menczer, and Alessandro Flammini. The rise of social bots.Communications of the ACM, 59(7):96–104, June 2016. 14

    2016

  52. [52]

    Unpacking the social media bot: A typology to guide research and policy.Policy & Internet, 12(2):225–248, 2020

    Robert Gorwa and Douglas Guilbeault. Unpacking the social media bot: A typology to guide research and policy.Policy & Internet, 12(2):225–248, 2020

    2020

  53. [53]

    Can large language models transform computational social science?Computational Linguistics, 50(1):237–291, March 2024

    Caleb Ziems, William Held, Omar Shaikh, Jiaao Chen, Zhehao Zhang, and Diyi Yang. Can large language models transform computational social science?Computational Linguistics, 50(1):237–291, March 2024

    2024

  54. [54]

    Tausczik and James W

    Yla R. Tausczik and James W. Pennebaker. The psychological meaning of words: LIWC and computerized text analysis methods.Journal of Language and Social Psychology, 29(1):24–54, March 2010

    2010

  55. [55]

    Replication data for: Measuring morality in political attitude expression, Novem- ber 2017

    Patrick Kraft. Replication data for: Measuring morality in political attitude expression, Novem- ber 2017

    2017

  56. [56]

    A simple sequentially rejective multiple test procedure.Scandinavian Journal of Statistics, 6(2):65–70, 1979

    Sture Holm. A simple sequentially rejective multiple test procedure.Scandinavian Journal of Statistics, 6(2):65–70, 1979

    1979

  57. [57]

    How do peer awards motivate creative content? Experimental evidence from Reddit.Management Science, 68(5):3488–3506, 2022

    Gordon Burtch, Qinglai He, Yili Hong, and Dokyun Lee. How do peer awards motivate creative content? Experimental evidence from Reddit.Management Science, 68(5):3488–3506, 2022

    2022

  58. [58]

    Experimental study of informal rewards in peer production.PLoS ONE, 7(3):e34358, March 2012

    Michael Restivo and Arnout van de Rijt. Experimental study of informal rewards in peer production.PLoS ONE, 7(3):e34358, March 2012

    2012

  59. [59]

    Groups reward individual sacrifice: The status solution to the collective action problem.American Sociological Review, 74(1):23–43, February 2009

    Robb Willer. Groups reward individual sacrifice: The status solution to the collective action problem.American Sociological Review, 74(1):23–43, February 2009

    2009

  60. [60]

    ’Unethical’ AI research on Reddit under fire.Science, 388(6747):570–571, May 2025

    Cathleen O’Grady. ’Unethical’ AI research on Reddit under fire.Science, 388(6747):570–571, May 2025

    2025

  61. [61]

    I’m a bot, beep boop. My directive is to give awards to Redditors

    Yifan Yao, Jinhao Duan, Kaidi Xu, Yuanfang Cai, Zhibo Sun, and Yue Zhang. A survey on large language model (LLM) security and privacy: The Good, The Bad, and The Ugly. High-Confidence Computing, 4(2):100211, June 2024. 15 Supplementary Information S1 Data Recording and Tracking We select and treat posts and comments every 30 minutes. Upon intervention and...

    2024