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Academic Journal
Business Analytics

“Unmasking the Backfire Effect: An Exploration of Resistance to Fact-Checking in the Digital Age”

Social media has become a critical battleground in the fight against misinformation, where fact-checks play an essential role in addressing falsehoods. Despite these efforts, growing polarization has led to widespread hostility toward fact-checks, with responses often characterized by toxicity or direct confrontation. We investigate the backfire effect, where fact-checks paradoxically strengthen false beliefs, as manifested through toxic responses on social media. This study explores the drivers of toxic reactions to fact-checks on social media. Using a dataset of fact-checks spanning three months, we analyzed user comments on Twitter (now X) to understand the dynamics of these reactions. Our results reveal that toxic responses are not a universal reaction but a selective one, concentrated on specific triggers. We find that fact-checks with definitive refutations (Pants on Fire or False) attract significantly more negative responses than ambiguous ratings, with True ratings showing minimal impact. Furthermore, toxicity is heavily driven by high-salience topics and the frequency of fact-check tweets. Our findings offer actionable strategies for fact-checkers to mitigate pushback and enhance truth dissemination in a landscape fraught with skepticism and resistance.
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Academic Journal
Business Analytics

“User Opinion Classification in Social Media: A Global Consistency Maximization Approach”

Social media is a major platform for opinion sharing. To better understand and exploit opinions on social media, we aim to classify users with opposite opinions on a topic for decision support. Rather than mining text content, we introduce a link-based classification model named Global Consistency Maximization (GCM) that partitions a social network into two classes of users with opposite opinions. Experiments on a Twitter dataset show that: (1) our global approach achieves higher accuracy than two baseline approaches; and (2) link-based classifiers are more robust to small training samples if selected properly.
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