3.9 Article

Do Twitter users change their behavior after exposure to misinformation? An in-depth analysis

Journal

SOCIAL NETWORK ANALYSIS AND MINING
Volume 12, Issue 1, Pages -

Publisher

SPRINGER WIEN
DOI: 10.1007/s13278-022-00992-8

Keywords

Misinformation; Fake news; Twitter; User behavior

Funding

  1. Google Cloud Research Credits program [204357824]
  2. University of Colorado Boulder

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Social media platforms have been exploited to spread misinformation, which has been shown to impact users' behavior and have social implications like polarization. This study focuses on the impact of misinformation on specific user behavior on Twitter. By comparing users' behaviors before and after exposure to misinformation, the study finds statistically significant changes in tweeting frequency, sentiment, word usage, and the ratio of liberal/conservative media URLs shared. The study also examines the characteristics of different user groups and the varying effects of exposure on users' behavior based on follower count.
Social media platforms have been exploited to disseminate misinformation in recent years. The widespread online misinformation has been shown to affect users' beliefs and is connected to social impact such as polarization. In this work, we focus on misinformation's impact on specific user behavior and aim to understand whether general Twitter users changed their behavior after being exposed to misinformation. We compare the before- and after-exposure behaviors of Twitter users to determine whether they changed their tweeting frequency, tweets sentiment, usage of specific types of words, and the ratio of liberal/conservative media URLs they shared. Our results show that users overall exhibited statistically significant changes in behavior across some of these metrics. Through language distance analysis, we show that exposed users were already different from baseline users before the exposure. We also study the characteristics of several specific user groups, which include liberal/conservative leaning groups and multi-exposure groups. Furthermore, we study whether the users' behavior changes after exposure to misinformation tweets vary based on their follower count or the follower count of the tweet authors. Finally, we examine potential bots' behaviors and find they are similar to that of normal users.

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