Evidence map›Paper›PMID 36407555›Full record

ArticleSocial network analysis and mining2022

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

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

Abstract read
In one paragraph

Article in Social network analysis and mining, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Yichen WangDepartment of Computer Science, University of Colorado Boulder, Boulder, CO 80309 USA.ORCID 0000-0002-1831-9541
Richard HanDepartment of Computer Science, University of Colorado Boulder, Boulder, CO 80309 USA.
Tamara Silbergleit LehmanDepartment of Computer Science, University of Colorado Boulder, Boulder, CO 80309 USA.
Qin LvDepartment of Computer Science, University of Colorado Boulder, Boulder, CO 80309 USA.
Shivakant MishraDepartment of Computer Science, University of Colorado Boulder, Boulder, CO 80309 USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

Fake newsMisinformationTwitterUser behavior

Identifiers

PMID36407555
PMCPMC9647250

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.