Analyzing Social Media Data Misuse and Intellectual Property Rights: A Dual Legal-Empirical Analysis Approach in the Digital Landscape

Social media platforms face significant challenges in combating data misuse and protecting intellectual property (IP) rights. This study investigates the misuse of social media data and its implications for IP protection using the TweepFake dataset, which contains synthetic and authentic tweets. A comprehensive methodology integrating empirical and legal analyses employed. The dataset was preprocessed to remove noise, handle missing data, and extract features such as lexical diversity, sentiment polarity, and temporal patterns. Using a Hybrid LSTM-SVM model, synthetic tweets classified with an impressive accuracy of 98.890/0, along with precision, recall, and F1-Score values of 97.45%, 96.83%, and 97.14%, respectively. Sentiment analysis revealed that synthetic tweets often exhibit extreme sentiment polarities, indicating manipulative intent. Clustering techniques identified suspicious and bot-like behaviors contributing to coordinated misuse campaigns. The legal integration cross-referenced empirical findings with existing statutes and regulations, exposing critical gaps in regulatory frameworks addressing synthetic content and IP violations. This dual approach provides actionable insights for detecting synthetic content, understanding its misuse, and proposing policy reforms. The findings have broad implications for regulators, policymakers, and platform developers in mitigating social media misuse while safeguarding digital IP rights.