Current State of the Art to Detect Fake News in Social Media: Global Trendings and Next Challenges

• Type: Article in International Conference Proceedings Book
• Date: 2018

Authors

Figueira, A (Author) FCUP; Guimarães, N (Author) Other; Torgo, L (Author) FCUP

Conference proceedings International

Title: Proceedings of the 14th International Conference on Web Information Systems and Technologies, WEBIST 2018, Seville, Spain, September 18-20, 2018.

Pages: 332-339

14th International Conference on Web Information Systems and Technologies, WEBIST 2018

18 September 2018 through 20 September 2018

Indexing

Scopus - 10 Citations

Other information

Authenticus ID: P-00P-MHA

DOI: 10.5220/0007188503320339

Abstract (EN): Nowadays, false news can be created and disseminated easily through the many social media platforms, resulting in a widespread real-world impact. Modeling and characterizing how false information proliferates on social platforms and why it succeeds in deceiving readers are critical to develop efficient algorithms and tools for their early detection. A recent surge of researching in this area has aimed to address the key issues using methods based on machine learning, deep learning, feature engineering, graph mining, image and video analysis, together with newly created data sets and web services to identify deceiving content. Majority of the research has been targeting fake reviews, biased messages, and against-facts information (false news and hoaxes). In this work, we present a survey on the state of the art concerning types of fake news and the solutions that are being proposed. We focus our survey on content analysis, network propagation, fact-checking and fake news analysis and emerging detection systems. We also discuss the rationale behind successfully deceiving readers. Finally, we highlight important challenges that these solutions bring. Copyright

Language: English

Type (Professor's evaluation): Scientific