Rumor detection in twitter an analysis in retrospect

Master of Computer Application

The rapid growth of social media platforms, particularly Twitter, has created a dynamic environment for real-time information sharing. While this has enabled instant communication, it has also facilitated the widespread circulation of rumors and misinformation, leading to serious societal, political, and economic consequences. Rumor detection in Twitter analysis using retrospect focuses on identifying misleading content by examining the context of past events, user credibility, and information propagation patterns. The retrospective approach enhances rumor detection by leveraging historical data, linguistic features, and network behavior to distinguish between factual and false information. Advanced data preprocessing techniques, feature extraction methods, and machine learning-based classification models are employed to analyze tweet datasets and detect potential rumors. By integrating retrospective insights, the system improves detection accuracy, reduces false positives, and strengthens the credibility of information on Twitter. This research contributes to the development of reliable social media monitoring tools, which are essential for combating misinformation and ensuring trustworthy communication in the digital era.

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