Fake Content Detection System for Multimodal Signals Over Social Media

Abstract

Fake contents can be easily created and spread through the social media and web-based platforms, resulting into the widespread of real-world impact. Novel digital technologies make it increasingly difficult to distinguish between real and fake media. One of the most recent developments contributing to the problem is the emergence of fake contents in form of images, videos, articles, posts, clickbaits which are hyper-realistic to depict the things that never happened. Coupled with the reach and speed of social media, such fake contents can quickly reach millions of people and have negative impacts on the society. To develop fake content detection tools, characterizing of how fake data proliferates over social platforms and why it succeeds in deceiving readers are critical to develop such efficient tools for early detection. The research work presents the description about the general process of fake content detection using various multimodal signals (articles, URLs, posts, images, videos) over social media platforms. A literature review related to the field of fake content detection for textual and non-textual content is depicted in this research work. The current status of fake contents over social media in form of multimodal signals is classified in two categories (textual and non-textual). The periodical evolution seen in the field of fake content generation and research studies on the basis of publications has been analysed. Further, review protocol is followed and presented, selected sources of publications, retrieved research papers on the basis of inclusion-exclusion criteria. This research approach will help to make the findings available in a systematic way for assisting the researchers working in similar area to select the most appropriate techniques to identify fake content for textual and non-textual datasets.