The main premise of this chapter is that the time is ripe for more extensive research and development of social media tools that filter out intentionally deceptive information such as deceptive memes, rumors and hoaxes, fake news or other fake posts, tweets and fraudulent profiles. Social media users’ awareness of intentional manipulation of online content appears to be relatively low, while the reliance on unverified information (often obtained from strangers) is at an all-time high. I argue there is need for content verification, systematic fact-checking and filtering of social media streams. This literature survey provides a background for understanding current automated deception detection research, rumor debunking, and broader content verification methodologies, suggests a path towards hybrid technologies, and explains why the development and adoption of such tools might still be a significant challenge.
Citation of this paper:
Rubin, V. L. (2017). Deception Detection and Rumor Debunking for Social Media. In Sloan, L. & Quan-Haase, A. (Eds.) (2017) The SAGE Handbook of Social Media Research Methods, London: SAGE. https://uk.sagepub.com/en-gb/eur/the-sage-handbook-of-social-media-research-methods/book245370