A Systematic Literature Review on Rumor Detection Techniques in Social Media Platforms

Rumor detection is an essential task in the era of social media, where the spread of false information can cause significant harm to an individual, society, and institutions. The purpose of rumor detection is to promote transparency, accuracy, and trustworthiness in information sharing, thereby contributing to the well-being and stability of communities and society as a whole. The main objective of this study is to give a comprehensive review of the past literature on the rumor detection employing, methodologies through manual searches across five databases (IEEE Explore, Springer Link, Science Direct, ACM Digital Library, and Google Scholar), Our systematic review scrutinized these studies to address three formulated research questions, aiming to comprehensively grasp the prevailing trends in utilizing techniques for rumor detection and determine the most effective strategies, including machine learning, deep learning, network analysis, and linguistic analysis for detecting rumors in social media. This study provides an exhaustive list of datasets used for rumor detection and assesses significant studies based on the data sources and methodologies they utilize.