Bots and misinformation spread on social media: A mixed scoping review with implications for COVID-19 (Preprint)

Autor: McKenzie Himelein-Wachowiak, Salvatore Giorgi, Amanda Devoto, Muhammad Rahman, Lyle Ungar, H. Andrew Schwartz, David H. Epstein, Lorenzo Leggio, Brenda Curtis
Rok vydání: 2021
DOI: 10.2196/preprints.26933
Popis: UNSTRUCTURED As of December 2020, the SARS-CoV-2 virus has been responsible for over 78 million cases of COVID-19 worldwide, resulting in over 1.7 million deaths. In the United States in particular, protective measures against the COVID-19 pandemic have been hampered by political polarization and discrepancies among federal, state, and local policies. As a result, a huge amount of information surrounding COVID-19, some of it contradictory or blatantly false, has proliferated on social media. In this mixed scoping review, we survey the role of automated accounts, or “bots,” in spreading misinformation during past epidemics, natural disasters, and politically polarizing events through the lens of the COVID-19 pandemic. We also review strategies used by bots to spread (mis)information and machine learning methods for detecting bot activity. We conclude by conducting and presenting a secondary analysis of known bots, finding that up to 66% of bots are discussing COVID-19. The proliferation of COVID-19 (mis)information by bots, coupled with human susceptibility to believing and sharing misinformation, may well impact the course of the pandemic.
Databáze: OpenAIRE