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

Fake News Detection in Social Media Using Machine Learning and Deep Learning

Fake news detection in social media is a process of detecting false information that is intentionally created to mislead readers. The spread of fake news may cause social, economic, and political turmoil if their proliferation is not prevented. However, fake news detection using machine learning faces many challenges. Datasets of fake news are usually unstructured and noisy. Fake news often mimics true news. In this study, a data preprocessing method is proposed for mitigating missing values in the datasets to enhance fake news detection accuracy. The experimental results show that Multi- Layer Perceptron (MLP) classifier combined with the proposed data preprocessing method outperforms the state-of-the-art methods. Furthermore, to improve the early detection of rumors in social media, a time-series model is proposed for fake news detection in social media using Twitter data. With the proposed model, computational complexity has been reduced significantly in terms of machine learning models training and testing times while achieving similar results as state-of-the-art in the literature. Besides, the proposed method has a simplified feature extraction process, because only the temporal features of the Twitter data are used. Moreover, deep learning techniques are also applied to fake news detection. Experimental results demonstrate that deep learning methods outperformed traditional machine learning models. Specifically, the ensemble-based deep learning classification model achieved top performance

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