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Analyzing QAnon on Twitter in Context of US Elections 2020: Analysis of User Messages and Profiles Using VADER and BERT Topic modeling

Published: 09 June 2021 Publication History

Abstract

In this paper we analyze Twitter users and their tweets mentioning “QAnon” in context of US Presidential Elections of 2020. We collect over 12 million tweets for 46 consecutive days starting from August 1st - September 15th 2020 containing the keywords “Trump”, “Biden” or “Election2020”. We identify users mentioning “QAnon” in their messages and perform sentiment analysis using VADER to evaluate their position towards Trump and Biden. Along with this we create word cloud and perform topic modeling using BERT on user profile descriptions. Some of our key findings contradict the popular notion that people discussing QAnon on social media are mostly located in Republican dominated states of the US. We also discover that an over whelming majority of QAnon tweeters are Donald Trump supporters, are conservative and nationalist having terms like “MAGA”, “God”, “Patriot” and “WWG1WGA” in their Twitter profile descriptions.

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cover image ACM Other conferences
dg.o '21: Proceedings of the 22nd Annual International Conference on Digital Government Research
June 2021
600 pages
ISBN:9781450384926
DOI:10.1145/3463677
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 09 June 2021

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Author Tags

  1. BERT
  2. Biden
  3. QAnon
  4. Trump
  5. Twitter
  6. US elections 2020
  7. sentiment analysis
  8. topic modeling

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  • (2024)Graph-Based Interpretability for Fake News Detection through Topic- and Propagation-Aware VisualizationComputation10.3390/computation1204008212:4(82)Online publication date: 15-Apr-2024
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