Can Social Media Anti-abuse Policies Work? A Quasi-experimental Study of Online Sexist and Racist Slurs

Felmlee, Diane; DellaPosta, Daniel; Rodis, Paulina d. C. Inara; Matthews, Stephen A.

doi:10.1177/2378023120948711

Cited by 8 publications

(9 citation statements)

References 37 publications

(50 reference statements)

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“…Our estimates of the frequency of harassing race and gender material could be relatively conservative, for instance, given that the bulk of our dataset relied on neutral hashtags. Our results also call attention to the necessity for further scholarly work that just now is beginning to consider practices and policies to mitigate serious abuse on these platforms [ 27 , 28 , 32 , 72 ].…”

Section: Discussionmentioning

confidence: 93%

“…Several studies examine the way digital forms of harassment target, and affect, women [ 15 , 24 – 26 ]. These troublesome messages often reinforce traditional feminine stereotypes, such as expectations of physical beauty, sexual “purity,” and a temperament that is pleasant, kind, and soft [ 27 ]. Online content also can aim to degrade women sexually and contain threats of sexual violence [ 26 , 28 ].…”

Section: Introductionmentioning

confidence: 99%

“…Within social media, one version of such competition involves the motivation to get posts noticed by others and to gain followers. Since negative Twitter comments tend to receive more retweets than their positive counterparts [ 27 ], posting derogatory messages can speed the attainment of these attention-seeking goals.…”

Section: Introductionmentioning

confidence: 99%

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Debating stereotypes: Online reactions to the vice-presidential debate of 2020

2023

Self Cite

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The 2020 Vice-Presidential debate afforded the opportunity to examine online reactions toward a woman of color, Kamala Harris, and a white man, Mike Pence, as they vied for the same position. We collected tweets from the Twitter API related to Harris and Pence, mainly using neutral hashtags. We examined keywords for gender and race slurs and conducted a multivariate analysis of tweet sentiment. Gender and racial slurs surface in both Harris and Pence datasets, showcasing the insidious nature of sexist and racist stereotypes that seep into online conversations regarding a high-status job debate. As anticipated, tweets regarding Harris contained a higher proportion of racist and sexist curse words, highlighting greater levels of harassment and “intersectional,” multi-ethnic/gender attacks. Racial insults targeting Blacks or Asians were more negative than those associated with Whites. Unexpectedly, tweets related to Harris were more positive in average sentiment than those regarding Pence. Yet, there were significantly more retweets, and more negativity of retweets, relating to Harris than to Pence, underscoring the relatively widespread broadcasting of derogatory messages about Harris. Overall, we found that harassing messages toward the candidates reinforced traditional race and gender stereotypes and bolstered the status of those who posted negative content by attaining more retweets. Harassers routinely invoked well-worn, stereotypical insults in their attacks, especially when targeting a multiracial woman.

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Section: Discussionmentioning

confidence: 93%

Section: Introductionmentioning

confidence: 99%

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Debating stereotypes: Online reactions to the vice-presidential debate of 2020

2023

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“…Another growing area of research involves leveraging online data for exploring diffusion on a massive scale (e.g., Chen et al, 2021;Felmlee et al, 2020;Rajkumar et al, 2022). Online platforms are also useful in contexts where in-person data are missing or infeasible.…”

Section: Implications Of Diffusion Through Social Network: Homogeniza...mentioning

confidence: 99%

Diffusion of innovations through social networks: Determinants and implications

Gondal

2023

Sociology Compass

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Social scientists have long been interested in the diffusion of innovations—the process by which new ideas, behavior, and practices spread between persons, organizations, and even countries. While innovations can enter a community through various channels, ongoing spread of innovations through a community occurs through the medium of social networks—collections of interpersonal or digital relationships connecting actors to each other. Social networks are important for diffusion because relationships foster communication, trust, and flow of information. Diffusion outcomes are also shaped by the structural properties of social networks such as density, centrality, and strength of ties, as well as properties of the innovation and the actors involved in the process. The purpose of the article is twofold: (1) to take stock of the field and review ongoing debates on the role of social networks in the diffusion of innovations and (2) to summarize the sociological implications of the diffusion of innovations through social networks.

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“…The Twitter networks used in our sample were previously gathered outside of this study (Felmlee et al 2020) via the Twitter API using a keyword search function, based on a period of one week at the end of February 2017. Tweets were collected that contained aggressive, harmful terms (i.e., curse words) that targeted women and minorities.…”

Section: Datamentioning

confidence: 99%

Dyads, triads, and tetrads: a multivariate simulation approach to uncovering network motifs in social graphs

2021

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Motifs represent local subgraphs that are overrepresented in networks. Several disciplines document multiple instances in which motifs appear in graphs and provide insight into the structure and processes of these networks. In the current paper, we focus on social networks and examine the prevalence of dyad, triad, and symmetric tetrad motifs among 24 networks that represent six types of social interactions: friendship, legislative co-sponsorship, Twitter messages, advice seeking, email communication, and terrorist collusion. Given that the correct control distribution for detecting motifs is a matter of continuous debate, we propose a novel approach that compares the local patterns of observed networks to random graphs simulated from exponential random graph models. Our proposed technique can produce conditional distributions that control for multiple, lower-level structural patterns simultaneously. We find evidence for five motifs using our approach, including the reciprocated dyad, three triads, and one symmetric tetrad. Results highlight the importance of mutuality, hierarchy, and clustering across multiple social interactions, and provide evidence of “structural signatures” within different genres of graph. Similarities also emerge between our findings and those in other disciplines, such as the preponderance of transitive triads.

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Can Social Media Anti-abuse Policies Work? A Quasi-experimental Study of Online Sexist and Racist Slurs

Cited by 8 publications

References 37 publications

Debating stereotypes: Online reactions to the vice-presidential debate of 2020

Debating stereotypes: Online reactions to the vice-presidential debate of 2020

Diffusion of innovations through social networks: Determinants and implications

Dyads, triads, and tetrads: a multivariate simulation approach to uncovering network motifs in social graphs

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