A Model for Identifying Misinformation in Online Social Networks

A Brief Overview on the Strategiesto Fight Back the Spreadof False Information

“…There are several approaches to tackle the problem of unreliable content on social media. For example, some authors opt by analyzing the patterns of propagation [35,49,60], others by creating supervised systems to classify unreliable content [5,42,59], and others by focusing on the characteristics of the accounts that share this type of content [7,9,17]. In addition, some works also focus on developing techniques for fact-checking claims [12,52] or focus on specific case studies [2,13,26,57].…”

“…Other works address similar events such as Hurricane Sandy [5], the Fukushima Disaster [63] and the Mumbai Blasts in 2011 [25].…”

Quality management architecture for social media data

“…The results indicated that a few straightforward features can be used for detecting usefulness. A model has been trained based on user and tweet characteristics using supervised learning for identifying misinformation in Twitter (accuracy ~77%) [37]. CREDBANK is a corpus of tweets, topics, events, and associated credibility scores comprising more than 1 billion tweets [38].…”

Online Misinformation

“…These are Isis flags and Isis supporters folks but the media has not reported because of political correctness, the demonstration, however, was anti-Isis. 2 Recent news data analysis also showed that fake news spread far more virally than real news. 3 Several social media platforms have recently gone under heavy criticism for becoming a ripe environment for the spread of misinformation, including fake news, mistruths, and hoaxes.…”

“…5 However, while some of these plans are materialising, they are deemed to offer partial solutions to an increasingly complex socio-technical problem. People and current technologies are yet to adapt to the age of misinformation, where incorrect or misleading information is intentionally or unintentionally spread [2].…”