Who Do We Trust? AI, Algorithms, and Emotions in Shaping News Credibility on Social Media

Abstract

This study aims to analyze how AI-generated news summary points, algorithmic recommendations, and emotional dissemination interact to shape news credibility in the social media environment. Nowadays, social media has become the main channel for obtaining news. Its emergence has made news more rapid, and the public has also become participants in news dissemination, breaking the pattern of celebrity dominance in the traditional media era.Meanwhile, social media has also restructured the entire news ecosystem. Personalized recommendations have turned news into tailormade cognitive filters, and algorithms are eroding news objectivity in a covert way. However, most of the existing research focuses on exploring the sole factor of algorithms, and research on news credibility under the interaction of multiple influences remains limited. This study will adopt content analysis and a literature review to examine the formation of news credibility through case studies.The findings indicate that algorithmic recommendations can amplify information bias, emotional content can weaken the public’s rational trust in news, and news summaries generated by artificial intelligence are often not timely enough, which may bring the risk of false information. Therefore, this study helps clarify the public’s judgment of news information and assists social platforms in designing more transparent and trustworthy mechanisms for news distribution.