TrueFluence - Social Media Promotion Video Credibility Analysis

The booming social media and e-commerce have turned the verifying of digital content into a major challenge of modern research. Misleading content, including misinformation, counterfeit reviews, and nongenuine online profiles, endangers not only society but also commerce and public discourse. This paper systematically reviews ten contemporary scholarly articles focusing on the evaluation of credibility and authenticity in different online domains such as influencer marketing, fake news, and review accuracy. We engage in the analysis and comparison of the various methods that these studies describe, categorizing them under two primary concepts: feature engi- neering techniques and the employment of machine learning models. Our review reveals an evident change in the field, where the dependence on single data types has been replaced by more complex methodologies that amalgamate several modalities. In the same vein, we see a shift from conventional machine learning algorithms to sophisticated deep learning frameworks like Transformers and Graph Convolutional Networks. A central revelation from this research is that source and user-related features are gaining importance and are getting as significant as content-based features for strong credibility assessments. We draw a comparative framework from these various approaches, recognizing the contributions, advantages, and disadvantages of each study. Ultimately, this paper underscores the significant challenges that remain, especially the requirement for extensive, standardized datasets with multi-level labels, and it points out the major research areas that will help to develop more effective and widely applicable systems for online content verification.