False Information on Web and Social Media: A Survey

TL;DR

This survey comprehensively reviews how false information spreads on the web and social media, examining actors, impact, characteristics, and detection algorithms, and proposes a unified framework for understanding and future research directions.

Contribution

It provides a comprehensive overview of false information proliferation, including a unified framework and future research directions, covering diverse aspects and detection methods.

Findings

False information spreads via various actors and mechanisms.

Detection algorithms utilize feature engineering, graph mining, and information modeling.

Future research needs include improved detection and understanding of false information dynamics.

Abstract

False information can be created and spread easily through the web and social media platforms, resulting in widespread real-world impact. Characterizing how false information proliferates on social platforms and why it succeeds in deceiving readers are critical to develop efficient detection algorithms and tools for early detection. A recent surge of research in this area has aimed to address the key issues using methods based on feature engineering, graph mining, and information modeling. Majority of the research has primarily focused on two broad categories of false information: opinion-based (e.g., fake reviews), and fact-based (e.g., false news and hoaxes). Therefore, in this work, we present a comprehensive survey spanning diverse aspects of false information, namely (i) the actors involved in spreading false information, (ii) rationale behind successfully deceiving readers, (iii) quantifying the impact of false information, (iv) measuring its characteristics across different dimensions, and finally, (iv) algorithms developed to detect false information. In doing so, we create a unified framework to describe these recent methods and highlight a number of important directions for future research.