Interregional and intraregional variability of intergroup attitudes predict online hostility

Abstract

To what extent are intergroup attitudes associated with regional differences in online aggression and hostility? We test whether regional attitude biases towards minorities and their local variability (i.e. intraregional polarization) independently predict verbal hostility on social media. We measure online hostility using large US American samples from Twitter and measure regional attitudes using nationwide survey data from Project Implicit. Average regional biases against Black people, White people, and gay people are associated with regional differences in social media hostility, and this effect is confounded with regional racial and ideological opposition. In addition, intraregional variability in interracial attitudes is also positively associated with online hostility. In other words, there is greater online hostility in regions where residents disagree in their interracial attitudes. This effect is present both for the full resident sample and when restricting the sample to White attitude holders. We find that this relationship is also, in part, confounded with regional proportions of ideological and racial groups (attitudes are more heterogeneous in regions with greater ideological and racial diversity). We discuss potential mechanisms underlying these relationships, as well as the dangers of escalating conflict and hostility when individuals with diverging intergroup attitudes interact. © 2020 The Authors. European Journal of Personality published by John Wiley & Sons Ltd on behalf of European Association of Personality Psychology

Adaptation and validation of the Johnson-Lecci scale to assess anti-white bias among black UK minority group members

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Ecological dimensions explain the past but do not predict future changes in trust.

“…Respondents were asked to rate whether different moral issues were justifiable (1 = never justifiable , 10 = always justifiable ): homosexuality, prostitution, abortion, divorce, euthanasia, suicide. Following previous research (Rapp, 2016; Rosenbusch et al, 2020), for each country-wave unit, we calculated the kurtosis of the six items. Following the preregistration, the values of the country-wave units with missing values on more than 50% of the items (more than three items) were coded as missing.…”

“…We split the data into a training (Wave 1–5: 1981 – 2009) and a test set (wave 6: 2010–2014). Training the model on one portion of data (usually, 80%) and testing its predictions using the rest of the data (usually, 20%; also referred to as holdout sample) is a common cross-validation technique (Kuhn & Johnson, 2013; Rosenbusch et al, 2020). We first estimated a baseline model including slopes of time and control variables.…”

Can Political Orientation Explain the Social Structure of Online Aggression? Integrating Social Media and Survey Data

“…The literature presents a broad range of determinants of online aggression: from psychological characteristics such as certain personalities, beliefs, attitudes, emotions, and needs, to situational characteristics such as technical capabilities, online norms, and online experiences, to socio-structural characteristics such as education and gender (Costello and Hawdon 2018;Frischlich et al 2021;Kaakinen et al 2018;Lowry et al 2016;Peterson and Densley 2017;Rosenbusch, Evans, and Zeelenberg 2020;Vargo and Hopp 2017). In particular, social status and political orientation were identified as relevant determinants of online aggression among adults (see Table 2).…”

“…2018; Lowry et al. 2016; Peterson and Densley 2017; Rosenbusch, Evans, and Zeelenberg 2020; Vargo and Hopp 2017). In particular, social status and political orientation were identified as relevant determinants of online aggression among adults (see Table 2).…”