False Information on Web and Social Media: A Survey
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.
- Publication:
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arXiv e-prints
- Pub Date:
- April 2018
- DOI:
- 10.48550/arXiv.1804.08559
- arXiv:
- arXiv:1804.08559
- Bibcode:
- 2018arXiv180408559K
- Keywords:
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- Computer Science - Social and Information Networks;
- Computer Science - Computation and Language;
- Computer Science - Computers and Society;
- Computer Science - Digital Libraries
- E-Print:
- To appear in the book titled Social Media Analytics: Advances and Applications, by CRC press, 2018