Hate Speech and Exposure to Low-Credibility News: A Toxic Synergy on Social Media
Hate speakers on social media are exposed to a higher proportion of news from low-credibility sources compared to non-hate speakers, particularly for hate speech targeting Jews and Muslims. This association is driven by exposure to unpopular low-credibility posts from partisan sources aligned with the target population.
Why People Unfollow Social Media Accounts: Insights into the Reasons Behind Social Media Dropoffs
People unfollow social media accounts due to a variety of reasons, including ignorant or tacky content, lack of authenticity, excessive self-promotion, and shifts in the account's perspective or focus.
Analyzing the Spread of Toxicity in Online Conversations: A Reddit Case Study
Toxic comments in online conversations can escalate and perpetuate toxicity, leading to more negativity and hostility on social media platforms.
Leveraging Low and High-level Features for Early Rumor Detection on Twitter
Leveraging neural models to learn hidden representations of individual rumor-related tweets at the very beginning of a rumor, which improves classification performance over time, significantly within the first 10 hours.
Leveraging Weak Learners for Early-stage Rumor Debunking on Twitter
An approach that leverages Convolutional Neural Networks to learn the credibility of individual tweets and aggregates the predictions to obtain the overall event credibility, which is then combined with a time series based rumor classification model for improved early-stage rumor detection performance.
Algorithmic Auditing of Twitter's Friend Recommendation System: Insights into Political Echo Chambers and Misinformation Exposure
Twitter's friend recommendation algorithm results in less politically homogeneous personal networks compared to social endorsement-based network growth, but still structurally resembles echo chambers. Accounts using the recommendation system also have lower potential exposure to false and misleading election-related content.
HyperGraphDis: An Efficient Hypergraph-Based Approach for Detecting Disinformation on Social Media
HyperGraphDis is a novel hypergraph-based method that effectively captures the intricate social structures, user relationships, and semantic/topical nuances to accurately and efficiently detect disinformation on social media platforms like Twitter.
Subjective Social Sorting on Twitter: How Self-Presentation Shapes Perceptions of Political Alignment
Self-presentation afforded by Twitter user profiles can shape perceptions of alignments between non-political and political identifiers, contributing to subjective social sorting.
Ensemble Bot Detection Across Multiple Social Media Platforms: Applications to the 2020 US Elections
An ensemble method is proposed for detecting social media bots across multiple platforms, including Twitter, Reddit, and Instagram, by training specialized classifiers for different user data fields and aggregating their outputs.
Estimating the Prevalence of Toxic Comments on Social Media Platforms Using a Calibrate-Extrapolate Framework
The core message of this article is to introduce a "Calibrate-Extrapolate" framework for efficiently processing and analyzing content to estimate the prevalence of toxic comments on social media platforms, using a pre-trained black box classifier.
© 2024 by Linnk AI