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Erschienen in: Social Network Analysis and Mining 1/2022

01.12.2022 | Original Article

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

verfasst von: Yichen Wang, Richard Han, Tamara Silbergleit Lehman, Qin Lv, Shivakant Mishra

Erschienen in: Social Network Analysis and Mining | Ausgabe 1/2022

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Abstract

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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Fußnoten
7
Note: Throughout this paper, the comparison of the feature value before and after exposure is shown as Feabefore vs. Feaafter, and P-values are indicated by the stars: ***: P < 0.001, **: P < 0.01, *: P < 0.05.
 
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Metadaten
Titel
Do Twitter users change their behavior after exposure to misinformation? An in-depth analysis
verfasst von
Yichen Wang
Richard Han
Tamara Silbergleit Lehman
Qin Lv
Shivakant Mishra
Publikationsdatum
01.12.2022
Verlag
Springer Vienna
Erschienen in
Social Network Analysis and Mining / Ausgabe 1/2022
Print ISSN: 1869-5450
Elektronische ISSN: 1869-5469
DOI
https://doi.org/10.1007/s13278-022-00992-8

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