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

SocioBot: Twitter for Command and Control of a Botnet

A botnet is a collection of computers controlled by a botmaster, often used for malicious activity. Social network provides an ideal medium for botnets to spread their reach. In this research, we develop and analyze a botnet that uses Twitter for its command and control channel. We use this botnet to perform a distributed denial of service attack on a web server, and we utilize the biological epidemic models to analyze the spread of the botnet using Twitter