#Hashtagging hate: Using Twitter to track racism online

Chaudhry, Irfan

doi:10.5210/fm.v20i2.5450

Cited by 65 publications

(56 citation statements)

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“…In response to calls for further attention to online bullying of racial minorities (Zych, Ortega-Ruiz, and Del Ray 2015), next we examine tweets during that period that contain a racist slur, n!!ger. We chose that particular offensive term because it is one of the slurs used most frequently to demean individuals on the basis of race (e.g., Chaudhry 2015;Felmlee et al 2018;Wang et al 2014). Although bi!ch and n!…”

Section: Methodsmentioning

confidence: 99%

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

Felmlee

DellaPosta

Rodis

et al. 2020

Socius

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The authors use the timing of a change in Twitter’s rules regarding abusive content to test the effectiveness of organizational policies aimed at stemming online harassment. Institutionalist theories of social control suggest that such interventions can be efficacious if they are perceived as legitimate, whereas theories of psychological reactance suggest that users may instead ratchet up aggressive behavior in response to the sanctioning authority. In a sample of 3.6 million tweets spanning one month before and one month after Twitter’s policy change, the authors find evidence of a modest positive shift in the average sentiment of tweets with slurs targeting women and/or African Americans. The authors further illustrate this trend by tracking the network spread of specific tweets and individual users. Retweeted messages are more negative than those not forwarded. These patterns suggest that organizational “anti-abuse” policies can play a role in stemming hateful speech on social media without inflaming further abuse.

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Section: Methodsmentioning

confidence: 99%

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

Felmlee

DellaPosta

Rodis

et al. 2020

Socius

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“…In the past, racist graffiti, defamation of religious buildings and acts of vandalism were used to measure the amount of racial hostility and hate in society [ 24 , 25 ]. Twitter's posts are today's forum for “writing on the walls” where people openly express their thoughts, opinions and beliefs.…”

Section: Introductionmentioning

confidence: 99%

Online negative sentiment towards Mexicans and Hispanics and impact on mental well-being: A time-series analysis of social media data during the 2016 United States presidential election

Hswen

Qin

Williams

et al. 2020

Heliyon

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Purpose The purpose was to use Twitter to conduct online surveillance of negative sentiment towards Mexicans and Hispanics during the 2016 United States presidential election, and to examine its relationship with mental well-being in this targeted group at the population level. Methods Tweets containing the terms Mexican(s) and Hispanic(s) were collected within a 20-week period of the 2016 United States presidential election (November 9th 2016). Sentiment analysis was used to capture percent negative tweets. A time series lag regression model was used to examine the association between percent count of negative tweets mentioning Mexicans and Hispanics and percent count of worry among Hispanic Gallup poll respondents. Results Of 2,809,641 tweets containing terms Mexican(s) and Hispanic(s), 687,291 tweets were negative. Among 8,314 Hispanic Gallup respondents, a mean of 33.5% responded to be worried on a daily basis. A significant lead time of 1 week was observed, showing that negative tweets mentioning Mexicans and Hispanics appeared to forecast daily worry among Hispanics by 1 week. Conclusion Surveillance of online negative sentiment towards racially vulnerable population groups can be captured using social media. This has potential to identify early warning signals for symptoms of mental well-being among targeted groups at the population level.

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“…Social media provides an arena where race and racism as concepts are debated, (re)constructed, and reified (Chaudhry, 2015(Chaudhry, , 2016. In this study, we consider public statements made on social media in response to two Trump administration announcements: of the travel ban from seven Middle Eastern countries, and of the building of a wall along the Mexico-U.S. border in early 2017 (Amatulli, 2017).…”

Section: Introductionmentioning

confidence: 99%

Analytic racecraft: Race-based averages create illusory group differences in perceptions of racism

Je¹,

El²

2020

Preprint

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Rising nationalism on the one hand and anti-racist protests on the other have called into question norms surrounding what can be considered racist. How do individuals decide whether anti- immigrant statements are “racist”? This project sought to identify determinants of agreement about what kind of speech is “racist”. Across two studies and one replication (N = 1211) from a nationally representative panel, perceivers rated how “racist” they found social media statements about immigration. The statements were drawn following the announcement of immigration policies in the United States. We introduce variance component analyses to quantify the extent to which judgments of racism are shared or idiosyncratic. We calculated the contributions of perceiver demographics and attitudes (e.g., racial category, age, gender, political leaning, attitudes toward hierarchy and race), statement attributes (e.g., aggressiveness or target of the statement), and context cues (e.g., statement popularity) to measure sources of agreement. While traditional aggregate analyses suggested that perceivers’ judgments about what constitutes racism differed on average between racial categories, the variance analyses consistently identified substantial disagreement, even within racial categories and with varied social norm cues (i.e., number of likes). Agreement occurred when perceivers shared political and racial ideology, and when the statements contained aggressive messages. Judgments of racism may not be shared within predicted social cleavages (e.g., racial categories) in this particular case, because anti-immigrant sentiment is highly politicized. Analyzing variance, as compared to only differences in means, brings new insights to the psychological study of racism, group-based beliefs, and anti-prejudice endeavors.

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#Hashtagging hate: Using Twitter to track racism online

Cited by 65 publications

References 0 publications

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

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

Online negative sentiment towards Mexicans and Hispanics and impact on mental well-being: A time-series analysis of social media data during the 2016 United States presidential election

Analytic racecraft: Race-based averages create illusory group differences in perceptions of racism

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