Social media can be a double-edged sword for modern communications, either a convenient channel exchanging ideas or an unexpected conduit circulating fake news through a large population. Existing studies of fake news focus on efforts on theoretical modelling of propagation or identification methods based on black-box machine learning, neglecting the possibility of identifying fake news using o...

We investigate the influence of fake and traditional, fact-based, news outlets on Twitter during the 2016 US presidential election. Using a comprehensive dataset of 171 million tweets covering the five months preceding election day, we identify 30 million tweets, sent by 2.2 million users, which are classified as spreading fake and extremely biased news, based on a list of news outlets curated ...

Social media for news consumption is becoming popular nowadays. The low cost, easy access and rapid information dissemination of social media bring benefits for people to seek out news timely. However, it also causes the widespread of fake news, i.e., low-quality news pieces that are intentionally fabricated. The fake news brings about several negative effects on individual consumers, news ecos...

The proliferation of misleading information in everyday access media outlets such as social media feeds, news blogs, and online newspapers have made it challenging to identify trustworthy news sources, thus increasing the need for computational tools able to provide insights into the reliability of online content. In this paper, we focus on the automatic identification of fake content in online...

The problem of fake news has gained a lot of attention as it is claimed to have had a significant impact on 2016 US Presidential Elections. Fake news is not a new problem and its spread in social networks is well-studied. Often an underlying assumption in fake news discussion is that it is written to look like real news, fooling the reader who does not check for reliability of the sources or th...

Automatic fake news detection is a challenging problem in deception detection, and it has tremendous real-world political and social impacts. However, statistical approaches to combating fake news has been dramatically limited by the lack of labeled benchmark datasets. In this paper, we present LIAR: a new, publicly available dataset for fake news detection. We collected a decade-long, 12.8K ma...

Our work considers leveraging crowd signals for detecting fake news and is motivated by tools recently introduced by Facebook that enable users to flag fake news. By aggregating users’ flags, our goal is to select a small subset of news every day, send them to an expert (e.g., via a third-party factchecking organization), and stop the spread of news identified as fake by an expert. The main obj...

This research surveys the current state-of-the-art technologies that are instrumental in the adoption and development of fake news detection. “Fake news detection” is defined as the task of categorizing news along a continuum of veracity, with an associated measure of certainty. Veracity is compromised by the occurrence of intentional deceptions. The nature of online news publication has change...

The 2016 US Presidential Election brought considerable attention to the phenomenon of “fake news”: entirely fabricated and often partisan content that is presented as is from a legitimate source. In this talk, I will explore two outstanding questions about fake news: Who falls for it, and what can be done to fight it? It is typically assumed that people believe fake news stories that are consis...

Abstract This article presents a qualitative study of media discourses around fake news, examining 288 news articles from two national elections in Denmark 2019. It explores how construct as security threat and journalists articulate their own role relation to this threat. The draws on discourse theory the concept logics critically map particular meaning ascriptions subject positions come domin...