Spam detection encompasses automated and manual techniques for identifying unwanted content online, including email spam, review spam, comment spam, and coordinated inauthentic behavior. In the context of misinformation research, spam detection overlaps significantly with efforts to identify deliberately misleading content—fake reviews, coordinated disinformation campaigns, and bot-generated posts. Effective detection balances precision (avoiding false positives that block legitimate content) with recall (catching actual spam).
Key papers
- 2011-ott-deceptive-opinion-spam — Foundational work on detecting deceptive opinion spam in product reviews; introduces benchmark dataset and shows machine learning approaches significantly outperform human judges at distinguishing truthful from deceptive opinions.
Related topics
- opinion-spam (fraudulent reviews specifically)
- fake-reviews (generation and detection of inauthentic reviews)
- social-spambots (coordinated bot-driven spam campaigns)