Bots and online hate during the COVID-19 pandemic: case studies in the United States and the Philippines
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Bots and online hate during the COVID‑19 pandemic: case studies in the United States and the Philippines Joshua Uyheng1 · Kathleen M. Carley1 Received: 23 July 2020 / Accepted: 3 October 2020 © Springer Nature Singapore Pte Ltd. 2020
Abstract Online hate speech represents a serious problem exacerbated by the ongoing COVID-19 pandemic. Although often anchored in real-world social divisions, hate speech in cyberspace may also be fueled inorganically by inauthentic actors like social bots. This work presents and employs a methodological pipeline for assessing the links between hate speech and bot-driven activity through the lens of social cybersecurity. Using a combination of machine learning and network science tools, we empirically characterize Twitter conversations about the pandemic in the United States and the Philippines. Our integrated analysis reveals idiosyncratic relationships between bots and hate speech across datasets, highlighting different network dynamics of racially charged toxicity in the US and political conflicts in the Philippines. Most crucially, we discover that bot activity is linked to higher hate in both countries, especially in communities which are denser and more isolated from others. We discuss several insights for probing issues of online hate speech and coordinated disinformation, especially through a global approach to computational social science. Keywords Hate speech · Social cybersecurity · Bots · Information maneuvers · COVID-19
Introduction In the time of COVID-19, nations all over the world face not just a major public health crisis, but also a crisis of social relations [66, 82]. Especially in settings of entrenched inequalities and political polarization, the pandemic has exposed and exacerbated conflicts between social groups [31, 51]. In this work, we investigate how such dynamics play out in cyberspace. We specifically examine the * Joshua Uyheng [email protected] Kathleen M. Carley [email protected] 1
CASOS Center, Institute for Software Research, Carnegie Mellon University, Pittsburgh, USA
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Vol.:(0123456789)
Journal of Computational Social Science
phenomenon of hate speech on social media, especially in relation to online disinformation [10, 20, 59]. In the context of a global pandemic, we ask to what extent the spread of online hate speech may be linked to bot-driven activities. We also probe what ends such information maneuvers may be instrumentalized toward, with consequences which extend beyond the digital sphere [5, 17, 70, 86]. This work bears several implications for understanding online hate speech in the context of the pandemic and beyond. We pivot from extant technical approaches of classifying hate speech [7, 35, 53], to theory-informed frameworks for characterizing it in the context of large-scale social interactions and potential information maneuvers [44, 80, 84]. We also reflect on the value of taking a global approach to computational social science, especially in the context of international issues like COVID-19, with its univer
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