Political Framing: US COVID19 Blame Game

Chereen Shurafa*, Kareem Darwish, Wajdi Zaghouani

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

15 Citations (Scopus)

Abstract

Through the use of Twitter, framing has become a prominent presidential campaign tool for politically active users. Framing is used to influence thoughts by evoking a particular perspective on an event. In this paper, we show that the COVID19 pandemic rather than being viewed as a public health issue, political rhetoric surrounding it is mostly shaped through a blame frame (blame Trump, China, or conspiracies) and a support frame (support candidates) backing the agenda of Republican and Democratic users in the lead up to the 2020 presidential campaign. We elucidate the divergences between supporters of both parties on Twitter via the use of frames. Additionally, we show how framing is used to positively or negatively reinforce users’ thoughts. We look at how Twitter can efficiently be used to identify frames for topics through a reproducible pipeline.

Original languageEnglish
Title of host publicationSocial Informatics - 12th International Conference, SocInfo 2020, Proceedings
EditorsSamin Aref, Kalina Bontcheva, Marco Braghieri, Frank Dignum, Fosca Giannotti, Francesco Grisolia, Dino Pedreschi
PublisherSpringer Science and Business Media Deutschland GmbH
Pages333-351
Number of pages19
ISBN (Print)9783030609740
DOIs
Publication statusPublished - 2020
Event12th International Conference on Social Informatics, SocInfo 2020 - Pisa, Italy
Duration: 6 Oct 20209 Oct 2020

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12467 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference12th International Conference on Social Informatics, SocInfo 2020
Country/TerritoryItaly
CityPisa
Period6/10/209/10/20

Keywords

  • COVID19
  • Framing theory
  • Social media analysis

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