AN EFFECTIVE IDENTIFICATION OF TRUTHFUL INFORMATION USING POLYMERIZATION SENTIMENTAL MODLEL

Abstract

Despite the increasing use of social media platforms for information and news gathering, its immoderate nature will create peoples like keyboard warriors, rumormongers etc. This often leads to the emergence and spreading of rumors, i.e. pieces of information that are unverified at the time of posting. At the same time, the openness of social media platforms provides opportunities to study how users share and discuss rumors, and to explore how natural language processing and data mining techniques may be used to find ways of determining their veracity. In this survey, we introduce the Big Data technology and discuss two types of rumors that circulate on social media; long-standing rumors that circulate for long periods of time, and newlyemerging rumors spawned during fast-paced events such as breaking news, where reports are released piecemeal and often with an unverified status in their early stages. We provide an overview of datasets into social media rumors with the ultimate goal of developing a rumor classification system that consists of four components: rumor detection, rumor tracking, rumor stance classification and rumor veracity classification. We delve into the approaches presented in the scientific literature for the development of each of these four components. We summarize the efforts and achievements so far towards the development of rumor classification systems and conclude with suggestions for avenues for future research in social media mining for detection and resolution of rumors