Vocabulary-based Method for Quantifying Controversy in Social Media

Autor:	de Zarate, Juan Manuel Ortiz, Feuerstein, Esteban
Rok vydání:	2020
Předmět:	Computer Science - Information Retrieval Computer Science - Computation and Language Computer Science - Machine Learning Computer Science - Social and Information Networks
Druh dokumentu:	Working Paper
Popis:	Identifying controversial topics is not only interesting from a social point of view, it also enables the application of methods to avoid the information segregation, creating better discussion contexts and reaching agreements in the best cases. In this paper we develop a systematic method for controversy detection based primarily on the jargon used by the communities in social media. Our method dispenses with the use of domain-specific knowledge, is language-agnostic, efficient and easy to apply. We perform an extensive set of experiments across many languages, regions and contexts, taking controversial and non-controversial topics. We find that our vocabulary-based measure performs better than state of the art measures that are based only on the community graph structure. Moreover, we shows that it is possible to detect polarization through text analysis. Comment: arXiv admin note: text overlap with arXiv:1507.05224 by other authors
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2001.09899 Zobrazit plný text záznamu View this record from Arxiv