We found a match
Your institution may have rights to this item. Sign in to continue.
- Title
Anomaly-based user comments detection in social news websites using troll user comments as normality representation.
- Authors
DE-LA-PEÑA-SORDO, JORGE; PASTOR-LÓPEZ, IKER; UGARTE-PEDRERO, XABIER; SANTOS, IGOR; BRINGAS, PABLO G.
- Abstract
The web has evolved over the years and, now, not only the administrators of a site generate content. Users of a website can express themselves showing their feelings or opinions. This fact has led to negative side effects: sometimes the content generated is inappropriate. Frequently, this content is authored by troll users who deliberately seek controversy. In this article, we propose a new method to detect trolling comments in social news websites. To this end, we extract a combination of statistical, syntactic and opinion features from the user comments. Since this troll phenomenon is quite common in the web, we propose a novel experimental setup for our anomaly detection method: considering troll comments as base model (normal behaviour: ‘normality’). We evaluate our approach with data from ‘Menéame’, a popular Spanish social news site, showing that our method can obtain high rates while minimizing the labelling task.
- Subjects
INFORMATION retrieval; NEWS websites; ANOMALY detection (Computer security); CONTENT filters (Computer science); HUMAN-machine systems; COMPUTER user identification
- Publication
Logic Journal of the IGPL, 2016, Vol 24, Issue 6, p883
- ISSN
1367-0751
- Publication type
Article
- DOI
10.1093/jigpal/jzw043