Four eyes, two truths: Explaining heterogeneity in perceived severity of digital hate against immigrants

Thomas Kirchmair, Kevin Koban, Jörg Matthes
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Abstract

Drawing on theories related to interpersonal and intergroup behavior, this study investigated effects of personality traits (i.e., empathy and identity insecurity) and attitudes (i.e., anti-migration attitudes and social dominance orientation) on the perceived severity of digital hate against immigrants in Austria. Findings of autoregressive path modeling using two-wave panel data revealed that empathic suffering and egalitarianism positively predicted perceived severity, while anti-migrant attitudes exhibited a negative prediction. In terms of interactions between personality and attitudes, we observed that the prediction of empathic suffering becomes less relevant for egalitarian individuals, which indicates an overwriting process that might be a promising way to counteract socially harmful digital hate perceptions. Implications for research on annotation tasks and hate interventions are discussed.
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四只眼睛,两个真相:解释针对移民的数字仇恨严重程度的异质性
本研究借鉴了与人际和群体间行为相关的理论,调查了人格特质(即移情和身份不安全感)和态度(即反移民态度和社会主导取向)对奥地利数字仇恨移民严重程度的影响。利用两波面板数据进行自回归路径建模的结果显示,移情痛苦和平等主义对感知的严重性有积极的预测作用,而反移民态度则表现出消极的预测作用。在人格与态度之间的交互作用方面,我们观察到,移情痛苦的预测对平等主义个体的相关性较低,这表明了一种覆盖过程,它可能是抵消有害社会的数字仇恨感知的一种有前途的方法。我们还讨论了注释任务和仇恨干预研究的意义。
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Living hated: Everyday experiences of hate speech across online and offline contexts Combatting online hate: Crowd moderation and the public goods problem Anti-immigrant rhetoric of populist radical right leaders on social media platforms “An image hurts more than 1000 words?” Four eyes, two truths: Explaining heterogeneity in perceived severity of digital hate against immigrants
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