Demonstration venue · read-only. Every page can be browsed; the buttons that would change it are switched off. Create an account to run TaxoReview on your own data. A simulation from public publication records: the people listed were not invited and did not agree to review.
? Tutorial for this page

Michelle Bobek

dblp:407/9065 · DBLP profile ↗
← Back
1ranked-venue papers
1as first author
1since 2021 · last 2026
0009-0002-4298-5859ORCID · reported

Domains — the database's venue-derived domains; a paper can count in several

Databases, data management, data science and information retrieval · 1 · 1 first-author · 1 since 2021Applied, interdisciplinary, general and emerging computing · 1 · 1 first-author · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Interdisciplinary, comprehensive, and emerging computing
1 paper
Computational social science and digital humanities · 100%
Artificial intelligence
1 paper
Trustworthy machine learning · 100%

Topics — the 2 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Computational social science and digital humanities
misinformation
1.012026
Community Fact-Checks Do Not Break Follower Loyalty · WWW 2026
Computational social science and digital humanities
social media analysis
1.012026
Community Fact-Checks Do Not Break Follower Loyalty · WWW 2026

Methods — techniques the papers use, named apart from their topics

quasi-experimental methods · 2.0time-series analysis · 1.0time series analysis · 1.0
YearPublicationVenuePosition
2026 Community Fact-Checks Do Not Break Follower Loyalty
abstract
Major social media platforms increasingly adopt community-based fact-checking to address misinformation on their platforms. While previous research has largely focused on its effect on engagement (e. g., reposts), an understanding of how fact-checks affect a user's follower base is missing. In this study, we employ quasi-experimental methods to causally assess whether users lose followers after their posts are corrected via community fact-checks. Based on time-series data on follower counts for N = 4391 community fact-checked posts from X, we find that community fact-checks do not lead to meaningful declines in the follower counts of users who post misleading content. This suggests that followers of spreaders of misleading posts tend to remain loyal and do not view community fact-checks as a sufficient reason to disengage. Our findings underscore the need for complementary interventions to more effectively disincentivize the production of misinformation on social media.
Michelle Bobek, Nicolas Pröllochs
WWW1