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Identity and Status: When Counterspeech Increases Hate Speech Reporting and Why

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Abstract

Much has been written about how social media platforms enable the rise of networked activism. However, few studies have examined how these platforms’ low-information environments shape how social movement activists, their opponents, and social media platforms interact. Hate speech reporting is one understudied area where such interactions occur. This article fills this gap by examining to what extent and how the gender and popularity of counterspeech in comment sections influence social media users’ willingness to report hate speech on the #MeToo movement. Based on a survey experiment (n = 1250) conducted in South Korea, we find that YouTube users are more willing to report such sexist hate speech when the counterspeech is delivered by a female rather than a male user. However, when the female user’s counterspeech received many upvotes, this was perceived to signal her enhanced status and decreased the intention to report hate speech, particularly among male users. No parallel patterns were found regarding other attitudes toward hate speech, counterspeech, YouTube, the #MeToo movement, and gender discrimination and hate speech legislation. These findings inform that users report hate speech based on potentially harmful content as well as their complex social interactions with other users and the platform.

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Notes

  1. Each social media platform provides its own guideline for content moderation:

    • Twitter: https://help.twitter.com/en/rules-and-policies/violent-threats-glorification

    • Facebook: https://www.facebook.com/communitystandards/hate_speech

    • YouTube: https://support.google.com/youtube/answer/2801939?hl=en

  2. In our experiment, we use static image-based rather than video-enabled scenarios. Although using a static image makes the experience more artificial, it also makes using YouTube in the context of this survey similar to using other platforms such as Facebook and Twitter. In addition, YouTube video clips and associated comment sections are highly distracting. Using static images helps respondents continue paying attention to the survey.

  3. We ask, “what is the gender of the person who wrote the reply?” to check the manipulation of the replier’s gender. 83.6% of respondents assigned to the female condition report that the author was female, whereas only 24.8% of those assigned to the male condition do. Overall, respondents are less likely to report that the counterspeech author was male (44.4% for the male treatment, 1.4% for the female condition) than female. To check the upvote manipulation, we ask, “Do you agree with the following statement?: This reply received a large number of ‘upvotes’.” (1 = Strongly disagree, 5 = Strongly agree). The mean response among those assigned to the many-upvote condition is 3.892. By contrast, the mean response among those assigned to the few-upvote condition is 3.792. The difference between these two mean responses is statistically significant at the 5% level for the one-tailed t-test (t = 1.75, p = 0.04).

  4. We divided the range of respondent age into four groups: (1) 20-29, (2) 30-39, (3) 40-49, and (4) 50-59. In each experimental group, the four age groups are evenly distributed. Furthermore, each age group within an experimental group has the same number of men and women.

  5. We controlled for respondents’ gender, age, education level, household income, political ideology, party identification, and attitude toward the #MeToo movement. These control variables were measured before respondents were exposed to the treatment except education level and household income.

  6. The question for each variable is as follows:

    1) Attitude toward the platform (i.e., YouTube): “What do you think about YouTube?”

    2) Attitude on user moderation: “YouTube users can keep the comments section safe to everyone.”

    3) Attitude toward the platform’s self-regulation: “YouTube should regulate users’ hate speech by itself.”

    4) Attitude toward the gender discrimination bill: (regarding after the introduction of Belgium gender discrimination law passed in March 2018) “Do you agree that the above bill is also necessary for South Korea?”

    5) Attitude toward the social media regulation bill: (regarding after the introduction of French law making online social media platforms responsible for content moderation passed in 2019) “Do you agree that the above bill is also necessary for South Korea?”

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Acknowledgements

We are grateful to Jiyong Eom, Youngdeok Hwang, Miyeon Jung, Chihong Jeon, Euro Bae, Jay Winston, and participants at the Bright Internet Global Summit (BIGS) for sharing their ideas and encouragement. We also thank the editor and two anonymous reviewers for their valuable feedback on an early draft

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  1. SNF Agora Institute, Johns Hopkins University, Baltimore, MD, USA

    Jae Yeon Kim

  2. College of Business, Korea Advanced Institute of Science and Technology (KAIST), Seoul, South Korea

    Jaeung Sim & Daegon Cho

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Replication data and code are available at https://github.com/jaeyk/status_identity_hate_speech_reporting.

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Kim, J.Y., Sim, J. & Cho, D. Identity and Status: When Counterspeech Increases Hate Speech Reporting and Why. Inf Syst Front 25, 1683–1694 (2023). https://doi.org/10.1007/s10796-021-10229-2

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