Detection and Resolution of Rumours in Social Media

Abstract

Despite the increasing use of social media platforms for information and news gathering, its unmoderated nature often leads to the emergence and spread of rumours, i.e., items 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 rumours, and to explore how to automatically assess their veracity, using natural language processing and data mining techniques. In this article, we introduce and discuss two types of rumours that circulate on social media: long-standing rumours that circulate for long periods of time, and newly emerging rumours 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 research into social media rumours with the ultimate goal of developing a rumour classification system that consists of four components: rumour detection, rumour tracking, rumour stance classification, and rumour veracity classification. We delve into the approaches presented in the scientific literature for the development of each of these four components. We summarise the efforts and achievements so far toward the development of rumour classification systems and conclude with suggestions for avenues for future research in social media mining for the detection and resolution of rumours.

Exploiting Context for Rumour Detection in Social Media

“…Despite increasing interest in rumours in social media [23,26,39,28,31,40], there has been very little work in automatic rumour detection [36]. Much of what work that has been done on rumour detection [24,10,11] has been limited to finding rumours known a priori.…”

“…There is also work on veracity classification both in the context of rumours and beyond [4,14,15,19,33,18,12]. Work on stance and veracity classification can be seen as complementary to our objectives; one could use the set of rumours detected by a rumour detection system as input to a classifier that determines stance of tweets in those rumours and/or veracity of those rumours [36].…”

“…A rumour detection system can in fact be the first component of a system that deals with rumours [36]: (1) rumour detection; (2) rumour tracking; (3) rumour stance classification, and (4) rumour veracity classification. Our rumour detection experiments on five datasets, each associated with a breaking news story, show that a classifier that sequentially exploits context from earlier tweets achieves significant improvements over non-sequential classifiers.…”

Detection and visualization of misleading content on Twitter

“…Typically, rumours do not directly correspond to a single piece of text or a social media post, but rather to a collection of items that disseminate it. Zubiaga et al [43] present a survey of approaches for rumor detection, including veracity classification and the collection and annotation of rumor-focused datasets from social media. Finally, a related problem is automated fact checking, which pertains to the classification of sentences into non-factual, unimportant factual and check-worthy factual statements [12].…”

Verifying information with multimedia content on twitter

“…classifying an individual content item as being fake or real. This is in contrast to the related problem of rumor detection [41], which considers that a piece of false information is spreading across social networks. Although rumor detection is a highly relevant research problem and several methodological aspects are common with those arising in our problem setting, the following discussion is mostly focusing on single post verification approaches.…”