Abstract
ABSTRACT Online hate is a global problem, affecting diverse social conflicts around the world. Common techniques to understand online hate focus primarily on measuring the volume of hateful messages and identifying individual perpetrators. However, these approaches fail to capture how online hate draws on wider social narratives to attack its targets, and how online hate is spread and organized in networked groups. Our work posits that assessing online hate through the lens of narratives and networks facilitates a deeper understanding of its dynamics in specific contexts and points to more holistic directions for mitigating its potential manipulation. We demonstrate the utility of our framework across two case studies in the Philippines: (a) the 2020 outbreak of the COVID-19 pandemic, and (b) the 2022 presidential elections. Through a computational pipeline of social cyber-security tools, we characterize the online hate narratives used to attack gendered, political, and racial identities in these events, and the network structures of the online hate groups involved. We additionally quantify manipulation of online hate narratives and networks by bots and trolls. These findings offer insights into the design of effective counter-narratives and building more resilient communities against online hate.
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