False News On Social Media: A Data-Driven Survey

In the past few years, the research community has dedicated growing interest to the issue of false news circulating on social networks. The widespread attention on detecting and characterizing false news has been motivated by considerable backlashes of this threat against the real world. As a matter of fact, social media platforms exhibit peculiar characteristics, with respect to traditional news outlets, which have been particularly favorable to the proliferation of deceptive information. They also present unique challenges for all kind of potential interventions on the subject. As this issue becomes of global concern, it is also gaining more attention in academia. The aim of this survey is to offer a comprehensive study on the recent advances in terms of detection, characterization and mitigation of false news that propagate on social media, as well as the challenges and the open questions that await future research on the field. We use a data-driven approach, focusing on a classification of the features that are used in each study to characterize false information and on the datasets used for instructing classification methods. At the end of the survey, we highlight emerging approaches that look most promising for addressing false news

A Survey on Various Methods to Detect Rumors on Social Media

Internet-based life stages have been utilized for data and newsgathering, and they are entirely significant in numerous applications. In any case, they likewise lead to the spreading of gossipy tidbits, Rumors, and phony news. Numerous endeavors have been taken to recognize and expose rumors via social networking media through dissecting their substance and social setting utilizing ML (Machine Learning) strategies. This paper gives an outline of the ongoing investigations in the rumor detection. The errand for rumor detection means to distinguish and characterize gossip either as obvious (genuine), bogus (nonfactual), or uncertain. This can hugely profit society by forestalling the spreading of such mistaken and off base data proactively. This paper is an introduction to rumor recognition via social networking media which presents the essential wording and kinds of bits of rumor and the nonexclusive procedure of rumor detection. A cutting edge portraying the utilization of directed ML algorithms for rumor detection via Social networking media is introduced. Keywords: Rumor Detection, Rumor Classification, Misinformation, News Events, Social Media, Machine Learning DOI: 10.7176/CEIS/11-4-01 Publication date:June 30th 202

Combating Fake News on Social Media: A Framework, Review, and Future Opportunities

Social media platforms facilitate the sharing of a vast magnitude of information in split seconds among users. However, some false information is also widely spread, generally referred to as “fake news”. This can have major negative impacts on individuals and societies. Unfortunately, people are often not able to correctly identify fake news from truth. Therefore, there is an urgent need to find effective mechanisms to fight fake news on social media. To this end, this paper adapts the Straub Model of Security Action Cycle to the context of combating fake news on social media. It uses the adapted framework to classify the vast literature on fake news to action cycle phases (i.e., deterrence, prevention, detection, and mitigation/remedy). Based on a systematic and inter-disciplinary review of the relevant literature, we analyze the status and challenges in each stage of combating fake news, followed by introducing future research directions. These efforts allow the development of a holistic view of the research frontier on fighting fake news online. We conclude that this is a multidisciplinary issue; and as such, a collaborative effort from different fields is needed to effectively address this problem