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.
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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.
The code supporting this study is available at the following GitHub repository: https://github.com/seanjtaylor/identify_effects_in_social_media.
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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.
The authors declare no competing interests.
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Taylor, S.J., Muchnik, L., Kumar, M. et al. Identity effects in social media. Nat Hum Behav 7, 27–37 (2023). https://doi.org/10.1038/s41562-022-01459-8