Bots and online hate during the COVID-19 pandemic: case studies in the United States and the Philippines.
J Comput Soc Sci
; 3(2): 445-468, 2020.
Article
in English
| MEDLINE | ID: covidwho-888325
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.
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Type of study:
Case report
Language:
English
Journal:
J Comput Soc Sci
Year:
2020
Document Type:
Article
Affiliation country:
S42001-020-00087-4
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