Tweetment Effects on the Tweeted: Experimentally Reducing Racist Harassment
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These conditions make status comparisons particularly likely and painful.
“These conditions make status comparisons particularly likely and painful (Brady et al., 2020; Munger, 2017).”
From Social Drivers and Algorithmic Mechanisms on Digital Media. (2024) · cites this paper as Munger, 2017
Empathic and humanizing prompts have been shown to reduce affective polarization and racist harassment.
“Empathic and humanizing prompts have been shown to reduce affective polarization (Saveski et al., 2022) and racist harassment (Hangartner et al., 2021; Munger, 2017).”
From Social Drivers and Algorithmic Mechanisms on Digital Media. (2024) · cites this paper as Munger, 2017
Identity- and empathy-based bystander interventions on social media can effectively reduce incivility, hate speech, and misinformation.
“For instance, identity-and empathy-based bystander interventions on social media can effectively reduce incivility (Munger 2020), hate speech (Hangartner et al. 2021, Munger 2017, Siegel & Badaan 2020), and misinformation (Pretus et al. 2022).”
From Social Media and Morality. (2024) · cites this paper as Munger 2017
Motivating bystanders to denounce hostility or harassment on social media is a promising strategy.
“One promising strategy is motivating bystanders to denounce hostility or harassment on social media (Munger 2017).”
From Social Media and Morality. (2024) · cites this paper as Munger 2017
Cites
A meta-analytic test of intergroup contact theory.
Pettigrew, T. F., & Tropp, L. R. (2006) · Journal of personality and social psychology · doi:10.1037/0022-3514.90.5.751 · Cited by 8,964 · cited 2× · see citing works
“A comprehensive review finds only mild support for the contact hypothesis (Pettigrew and Tropp 2006), and others note”
BackgroundComputer-Mediated Communication: Impersonal, Interpersonal, and Hyperpersonal Interaction
WALTHER, J. B. (1996) · Communication Research · doi:10.1177/009365096023001001 · Cited by 4,798 · see citing works
Social psychological aspects of computer-mediated communication
Kiesler, S., Siegel, J., & McGuire, T. W. (1984) · American Psychologist · doi:10.1037/0003-066x.39.10.1123 · Cited by 2,630 · see citing works
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Pennycook, G., & Rand, D. G. (2021) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2021.02.007 · Cited by 1,360 · see citing works
Shifting attention to accuracy can reduce misinformation online
Pennycook, G., Epstein, Z., Mosleh, M., Arechar, A. A., Eckles, D., & Rand, D. G. (2021) · Nature · doi:10.1038/s41586-021-03344-2 · Cited by 1,155 · see citing works
Prejudice Reduction: Progress and Challenges
Paluck, E. L., Porat, R., Clark, C. S., & Green, D. P. (2021) · Annual Review of Psychology · doi:10.1146/annurev-psych-071620-030619 · Cited by 686 · see citing works
Brady, W. J., Crockett, M. J., & Van Bavel, J. J. (2020) · Perspectives on Psychological Science · doi:10.1177/1745691620917336 · Cited by 417 · see citing works
Social Media, Echo Chambers, and Political Polarization
Barberá, P. (2020) · Social Media and Democracy · doi:10.1017/9781108890960.004 · Cited by 373 · see citing works
Locally noisy autonomous agents improve global human coordination in network experiments
Shirado, H., & Christakis, N. A. (2017) · Nature · doi:10.1038/nature22332 · Cited by 288 · see citing works
Unpacking the Social Media Bot: A Typology to Guide Research and Policy
Gorwa, R., & Guilbeault, D. (2020) · Policy & Internet · doi:10.1002/poi3.184 · Cited by 270 · see citing works
Social Drivers and Algorithmic Mechanisms on Digital Media.
Metzler, H., & Garcia, D. (2024) · Perspectives on Psychological Science · doi:10.1177/17456916231185057 · Cited by 204 · see citing works
“These conditions make status comparisons particularly likely and painful (Brady et al., 2020; Munger, 2017).”
BackgroundEmpathy-based counterspeech can reduce racist hate speech in a social media field experiment
Hangartner, D., Gennaro, G., Alasiri, S., Bahrich, N., Bornhoft, A., Boucher, J., Demirci, B. B., Derksen, L., Hall, A., Jochum, M., et al. (2021) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2116310118 · Cited by 144 · see citing works
Vulnerable robots positively shape human conversational dynamics in a human–robot team
Traeger, M. L., Strohkorb Sebo, S., Jung, M., Scassellati, B., & Christakis, N. A. (2020) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1910402117 · Cited by 140 · see citing works
Argyle, L. P., Bail, C. A., Busby, E. C., Gubler, J. R., Howe, T., Rytting, C., Sorensen, T., & Wingate, D. (2023) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2311627120 · Cited by 144 · see citing works
Me, My Echo Chamber, and I: Introspection on Social Media Polarization
Gillani, N., Yuan, A., Saveski, M., Vosoughi, S., & Roy, D. (2018) · Proceedings of the 2018 World Wide Web Conference on World Wide Web - WWW '18 · doi:10.1145/3178876.3186130 · Cited by 117 · see citing works
Van Bavel, J. J., Robertson, C. E., del Rosario, K., Rasmussen, J., & Rathje, S. (2024) · Annual Review of Psychology · doi:10.1146/annurev-psych-022123-110258 · Cited by 121 · see citing works
Mosleh, M., Martel, C., Eckles, D., & Rand, D. (2021) · Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3411764.3445642 · Cited by 106 · see citing works
Online disinhibition: conceptualization, measurement, and implications for online deviant behavior
Cheung, C. M. K., Wong, R. Y. M., & Chan, T. K. H. (2020) · Industrial Management & Data Systems · doi:10.1108/imds-08-2020-0509 · Cited by 89 · see citing works
SoK: Content Moderation in Social Media, from Guidelines to Enforcement, and Research to Practice
Singhal, M., Ling, C., Paudel, P., Thota, P., Kumarswamy, N., Stringhini, G., & Nilizadeh, S. (2023) · 2023 IEEE 8th European Symposium on Security and Privacy (EuroS&P) · doi:10.1109/eurosp57164.2023.00056 · Cited by 57 · see citing works
Tucker, J. A. (2023) · Handbook of Computational Social Science for Policy · doi:10.1007/978-3-031-16624-2_20 · Cited by 3 · see citing works
Me, My Echo Chamber, and I: Introspection on Social Media Polarization
Gillani, N., Yuan, A., Saveski, M., Vosoughi, S., & Roy, D. (2018) · arXiv · doi:10.48550/arxiv.1803.01731 · Cited by 33 · see citing works
- Authors
- Munger, K.
- Affiliations
- New York University (US)
- Corresponding author
- Munger, Kevin
- ORCID
- Munger, Kevin
- Venue
- Political Behavior
- Published
- 2016-11-11
- Volume
- 39
- Issue
- 3
- Pages
- 629-649
- ISSN
- 0190-9320 (ISSN-L)
- 1573-6687
- OpenAlex
- W2555691318
- Cited by
- 395
- References
- 3
- Citations by year
- 2026: 26 · 2025: 38 · 2024: 51 · 2023: 42 · 2022: 41 · 2021: 39 · 2020: 100 · 2019: 22 · 2018: 24 · 2017: 10 · 2016: 2
- FWCI
- 55.27
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Political Behavior (publishedVersion)
- Topics
- Social Sciences › Sociology and Political Science › Social and Intergroup Psychology
- Physical Sciences › Computer Science › Artificial Intelligence › Hate Speech and Cyberbullying Detection
- Social Sciences › Sociology and Political Science › Names, Identity, and Discrimination Research
- Keywords
- Twitter bots, ethnocentrism, prejudice reduction, follower influence, white men, naturalistic experiment
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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