Skip to main content

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.

Identity effects in social media

Abstract

Identity cues appear ubiquitously alongside content in social media today. Some also suggest universal identification, with names and other cues, as a useful deterrent to harmful behaviours online. Unfortunately, we know little about the effects of identity cues on opinions and online behaviours. Here we used a large-scale longitudinal field experiment to estimate the extent to which identity cues affect how people form opinions about and interact with content online. We randomly assigned content produced on a social news aggregation website to ‘identified’ and ‘anonymous’ conditions to estimate the causal effect of identity cues on how viewers vote and reply to content. The effects of identity cues were significant and heterogeneous, accounting for between 28% and 61% of the variation in voting associated with commenters’ production, reputation and reciprocity. Our results also showed that identity cues cause people to vote on content faster (consistent with heuristic processing) and to vote according to content producers’ reputations, production history and reciprocal votes with content viewers. These results provide evidence that rich-get-richer dynamics and inequality in social content evaluation are mediated by identity cues. They also provide insights into the evolution of status in online communities. From a practical perspective, we show via simulation that social platforms may improve content quality by including votes on anonymized content as a ranking signal.

This is a preview of subscription content, access via your institution

Access options

Buy article

Get time limited or full article access on ReadCube.

$32.00

All prices are NET prices.

Fig. 1: Associations between values of moderator covariates and the effect of identity on interactions.
Fig. 2: Distribution of voting duration and estimated identity effect.
Fig. 3: Algorithmic ranking simulations.
Fig. 4: Average treatment effect of identity for each month of the experiment.
Fig. 5: Causal diagram indicating the interference we test for and the type we rule out by assumption.
Fig. 6: Estimates of spillover effects of the anonymous treatment.

Data availability

An anonymized version of the data supporting this study is retained indefinitely for reproducibility. The data can be accessed from the authors by signing a non-disclosure agreement available at the following GitHub repository: https://github.com/seanjtaylor/identify_effects_in_social_media. The NDA requires that researchers provide their affiliation and attest that they will only use the data for reproduction and that no attempt will be made to re-engineer the identities of users or the platform.

Code availability

The code supporting this study is available at the following GitHub repository: https://github.com/seanjtaylor/identify_effects_in_social_media.

References

  1. Burns, W. Is it time to require identity verification for everyone using social media? Forbes https://www.forbes.com/sites/willburns/2018/02/22/is-it-time-to-require-identity-verification-for-everyone-using-social-media/?sh=74308aec8683 (2018).

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

    Article  Google Scholar 

  3. Lorenz, J., Rauhut, H., Schweitzer, F. & Helbing, D. How social influence can undermine the wisdom of crowd effect. Proc. Natl Acad. Sci. USA 108, 9020–9025 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  5. Chaiken, S. Heuristic versus systematic information processing and the use of source versus message cues in persuasion. J. Pers. Soc. Psychol. 39, 752–766 (1980).

    Article  Google Scholar 

  6. Chaiken, S. in Social Influence: The Ontario Symposium Vol. 5 (eds Zanna, M. P. et al.) 3–39 (Lawrence Erlbaum Associates, 1987).

  7. Hass, R. G. in Cognitive Responses in Persuasion Vol. 2 (eds Petty, R. E. et al.) Ch. 7 (Lawrence Erlbaum Associates, 1981); https://doi.org/10.4324/9781315803012

  8. Walther, J. B. Relational aspects of computer-mediated communication: experimental observations over time. Organ. Sci. 6, 186–203 (1995).

    Article  Google Scholar 

  9. Walther, J. B. Computer-mediated communication: impersonal, interpersonal, and hyperpersonal interaction. Commun. Res. 23, 3–43 (1996).

    Article  Google Scholar 

  10. Resnick, P., Kuwabara, K., Zeckhauser, R. & Friedman, E. Reputation systems. Commun. ACM 43, 45–48 (2000).

    Article  Google Scholar 

  11. Pavlou, P. A. & Gefen, D. Building effective online marketplaces with institution-based trust. Inf. Syst. Res. 15, 37–59 (2004).

    Article  Google Scholar 

  12. Moon, J. Y. & Sproull, L. S. The role of feedback in managing the Internet-based volunteer work force. Inf. Syst. Res. 19, 494–515 (2008).

    Article  Google Scholar 

  13. Wetzer, I. M., Zeelenberg, M. & Pieters, R. “Never eat in that restaurant, I did!”: exploring why people engage in negative word-of-mouth communication. Psychol. Mark. 24, 661–680 (2007).

    Article  Google Scholar 

  14. Wood, W. Attitude change: persuasion and social influence. Annu. Rev. Psychol. 51, 539–570 (2000).

    Article  CAS  PubMed  Google Scholar 

  15. Cialdini, R. B. & Trost, M. R. Social Influence: Social Norms, Conformity and Compliance. The handbook of social psychology, McGraw-Hill, 151–192 (1998).

  16. Chaiken, S., Wood, W. & Eagly, A. H. Principles of Persuasion. Social psychology: Handbook of basic principles. Guilford, 702–742 (1996).

  17. Chen, S., Shechter, D. & Chaiken, S. Getting at the truth or getting along: accuracy- versus impression-motivated heuristic and systematic processing. J. Pers. Soc. Psychol. 71, 262–275 (1996).

    Article  Google Scholar 

  18. Lundgren, S. R. & Prislin, R. Motivated cognitive processing and attitude change. Pers. Soc. Psychol. Bull. 24, 715–726 (1998).

    Article  Google Scholar 

  19. Petty, R. E. & Wegener, D. T. Matching versus mismatching attitude functions: implications for scrutiny of persuasive messages. Pers. Soc. Psychol. Bull. 24, 227–240 (1998).

    Article  Google Scholar 

  20. Tajfel, H. Social psychology of intergroup relations. Annu. Rev. Psychol. 33, 1–39 (1982).

    Article  Google Scholar 

  21. Turner, J. C. Social Influence (Thomson Brooks/Cole, 1991).

  22. Flache, A. Models of social influence: towards the next frontiers. J. Artif. Soc. Soc. Simul. https://doi.org/10.18564/jasss.3521 (2017).

  23. Aral, S. & Walker, D. Creating social contagion through viral product design: a randomized trial of peer influence in networks. Manag. Sci. 57, 1623–1639 (2011).

    Article  Google Scholar 

  24. Aral, S. & Walker, D. Identifying influential and susceptible members of social networks. Science 337, 337–341 (2012).

    Article  CAS  PubMed  Google Scholar 

  25. Bakshy, E., Eckles, D., Yan, R. & Rosenn, I. Social influence in social advertising: evidence from field experiments. In Proc. 13th ACM Conference on Electronic Commerce 146–161 (ACM, 2012); https://doi.org/10.1145/2229012.2229027

  26. Aral, S. & Walker, D. Tie strength, embeddedness, and social influence: a large-scale networked experiment. Manag. Sci. 60, 1352–1370 (2014).

    Article  Google Scholar 

  27. Tucker, C. Social Advertising: How Advertising that Explicitly Promotes Social Influence Can Backfire. SSRN https://doi.org/10.2139/ssrn.1975897 (2016).

  28. Bakshy, E., Rosenn, I., Marlow, C. & Adamic, L. The role of social networks in information diffusion. In Proc. 21st International Conference on World Wide Web 519–528 (ACM, 2012).

  29. Bapna, R. & Umyarov, A. Do your online friends make you pay? A randomized field experiment on peer influence in online social networks. Manag. Sci. 61, 1902–1920 (2015).

    Article  Google Scholar 

  30. Luc, J. G. Y. et al. Does tweeting improve citations? One-year results from the TSSMN prospective randomized trial. Ann. Thorac. Surg. 111, 296–300 (2021).

    Article  PubMed  Google Scholar 

  31. Forman, C., Ghose, A. & Wiesenfeld, B. Examining the relationship between reviews and sales: the role of reviewer identity disclosure in electronic markets. Inf. Syst. Res. 19, 291–313 (2008).

    Article  Google Scholar 

  32. Ma, M. & Agarwal, R. Through a glass darkly: information technology design, identity verification, and knowledge contribution in online communities. Inf. Syst. Res. 18, 42–67 (2007).

    Article  Google Scholar 

  33. Shalizi, C. R. & Thomas, A. C. Homophily and contagion are generically confounded in observational social network studies. Sociol. Methods Res. 40, 211–239 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  34. Toubia, O. & Stephen, A. T. Intrinsic vs. image-related utility in social media: why do people contribute content to twitter? Mark. Sci. 32, 368–392 (2013).

    Article  Google Scholar 

  35. Taylor, S. J., Bakshy, E. & Aral, S. Selection effects in online sharing: consequences for peer adoption. In ACM Conference on Electronic Commerce 821–836 (ACM, 2013); https://doi.org/10.1145/2492002.2482604

  36. Bertrand, M. & Mullainathan, S. Are Emily and Greg more employable than Lakisha and Jamal? A field experiment on labor market discrimination. Am. Econ. Rev. 94, 991–1013 (2004).

    Article  Google Scholar 

  37. Edelman, B., Luca, M. & Svirsky, D. Racial discrimination in the sharing economy: evidence from a field experiment. Am. Econ. J. Appl. Econ. 9, 1–22 (2017).

    Article  Google Scholar 

  38. Hu, N., Zhang, J. & Pavlou, P. A. Overcoming the J-shaped distribution of product reviews. Commun. ACM 52, 144–147 (2009).

    Article  Google Scholar 

  39. Kahneman, D., Sibony, O. & Sunstein, C. R. Noise: A Flaw in Human Judgment (Little, Brown, 2021).

  40. Bourdieu, P. in: Handbook for Theory and Research for the Sociology of Education (ed. Richardson, J.). Greenwood Press, 241–258 (1986).

  41. Throsby, D. Cultural capital. J. Cult. Econ. 23, 3–12 (1999).

    Article  Google Scholar 

  42. Putnam, R. The prosperous community: social capital and public life. The American Prospect https://prospect.org/infrastructure/prosperous-community-social-capital-public-life (1993).

  43. Lin, C.-S. & Chen, Y.-F. Examining social tagging behaviour and the construction of an online folksonomy from the perspectives of cultural capital and social capital. J. Inf. Sci. 38, 540–557 (2012).

    Article  Google Scholar 

  44. Simon, H.A. On a class of skew distribution functions. Biometrika 42, 425–440 (1955).

    Article  Google Scholar 

  45. Merton, R. K. The Matthew effect in science. Science 159, 56–63 (1968).

    Article  CAS  PubMed  Google Scholar 

  46. Barabási, A.-L. & Albert, R. Emergence of scaling in random networks. Science 286, 509–512 (1999).

    Article  PubMed  Google Scholar 

  47. Salganik, M. J., Dodds, P. S. & Watts, D. J. Experimental study of inequality and unpredictability in an artificial cultural market. Science 311, 854–856 (2006).

    Article  CAS  PubMed  Google Scholar 

  48. Van de Rijt, A. Self-correcting dynamics in social influence processes. Am. J. Sociol. 124, 1468–1495 (2019).

    Article  Google Scholar 

  49. Berry, G. & Taylor, S. J. Discussion quality diffuses in the digital public square. In Proc. 26th International Conference on World Wide Web 1371–1380 (ACM, 2017); https://doi.org/10.1145/3038912.3052666

  50. Taylor, S. J. & Eckles, D. in Complex Spreading Phenomena in Social Systems (eds Lehmann, S. & Ahn, Y. Y.) 289–322 (Springer, 2018); https://doi.org/10.1007/978-3-319-77332-2_16

  51. Sun, T. & Taylor, S. J. Displaying things in common to encourage friendship formation: a large randomized field experiment. Quant. Mark. Econ. 18, 237–271 (2020).

    Article  Google Scholar 

Download references

Acknowledgements

We thank members of the MIT Initiative on the Digital Economy for valuable feedback. L.M. acknowledges support from the Israel Science Foundation (Grant 2566/21) and the David Goldman Data-Driven Innovation Research Centre for supporting this research. The authors received no specific funding for this work.

Author information

Authors and Affiliations

Authors

Contributions

S.J.T., L.M. and S.A. performed the research design and data analysis. S.J.T., M.K. and S.A. did the writing.

Corresponding author

Correspondence to Sinan Aral.

Ethics declarations

Competing interests

The authors declare no competing interests.

Peer review

Peer review information

Nature Human Behaviour thanks Johan Ugander, Marijn Keijzer and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Supplementary Information

Supplementary Sections A.1–A.7, Figs. B3–B7 and Tables C3–C5.

Reporting Summary

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Taylor, S.J., Muchnik, L., Kumar, M. et al. Identity effects in social media. Nat Hum Behav (2022). https://doi.org/10.1038/s41562-022-01459-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1038/s41562-022-01459-8

Search

Quick links

Nature Briefing

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

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing