2018/12/02 by Jasper Linmans, Linmans, Jasper, Bob van de Velde +3
Computer Science · Social Sciences · #Computation and Language (cs.CL) #FOS: Computer and information sciences #Information Retrieval (cs.IR) #Software Engineering Research #Topic Modeling #Wikis in Education and Collaboration #cs.CL #cs.IR
paper · pdf · doi:10.48550/arxiv.1812.00382
Presented at the 27th ACM International Conference on Information and Knowledge Management (CIKM 2018)
arxiv created 2018/12/02 · openalex publication_date 2018/12/02 · arxiv updated 2018/12/04 · openalex created_date 2022/08/01 · openalex updated_date 2026/07/28
Detecting controversy in general web pages is a daunting task, but increasingly essential to efficiently moderate discussions and effectively filter problematic content. Unfortunately, controversies occur across many topics and domains, with great changes over time. This paper investigates neural classifiers as a more robust methodology for controversy detection in general web pages. Current models have often cast controversy detection on general web pages as Wikipedia linking, or exact lexical matching tasks. The diverse and changing nature of controversies suggest that semantic approaches are better able to detect controversy. We train neural networks that can capture semantic information from texts using weak signal data. By leveraging the semantic properties of word embeddings we robustly improve on existing controversy detection methods. To evaluate model stability over time and to unseen topics, we asses model performance under varying training conditions to test cross-temporal, cross-topic, cross-domain performance and annotator congruence. In doing so, we demonstrate that weak-signal based neural approaches are closer to human estimates of controversy and are more robust to the inherent variability of controversies.