Abstract

The year 2020 will be remembered for two events of global significance: the COVID-19 pandemic and 2020 U.S. presidential election. In this chapter, we summarize recent studies using large public Twitter datasets on these issues. We have three primary objectives. First, we delineate epistemological and practical considerations when combining the traditions of computational research and social science research. A sensible balance should be struck when the stakes are high between advancing social theory and concrete, timely reporting of ongoing events. We additionally comment on the computational challenges of gleaning insight from large amounts of social media data. Second, we characterize the role of social bots in social media manipulation around the discourse on the COVID-19 pandemic and 2020 U.S. presidential election. Third, we compare results from 2020 to prior years to note that, although bot …

Metadata

publication

Handbook of Computational Social Science, Volume 1: Theory, Case Studies and …, 2021

year

2021

publication date

2021/11/10

authors

HCH Chang, E Chen, M Zhang, G Muric, E Ferrara

link

https://www.taylorfrancis.com/chapters/edit/10.4324/9781003024583-21/social-bots-social-media-manipulation-2020-ho-chun-herbert-chang-emily-chen-meiqing-zhang-goran-muric-emilio-ferrara

resource_link

https://arxiv.org/pdf/2102.08436

book

Handbook of Computational Social Science, Volume 1: Theory, Case Studies and Ethics

pages

185

publisher

Routledge