Multilingual stance detection in social media political debates. (September 2020)

• Record Type: Journal Article
• Authors: Lai, Mirko; Cignarella, Alessandra Teresa; Hernández Farías, Delia Irazú; Bosco, Cristina; Patti, Viviana; Rosso, Paolo
• Is Part Of: Computer speech & language. Volume 63(2020)
• Publication Date: 2020-09
• Subjects: Stance detection -- Multilingual -- Contextual features -- Political debates -- Twitter; Speech processing systems -- Periodicals; Automatic speech recognition -- Periodicals; Computers -- Periodicals; Linguistics -- Periodicals; Speech-Language Pathology -- Periodicals; Traitement automatique de la parole -- Périodiques; Reconnaissance automatique de la parole -- Périodiques; Automatic speech recognition; Speech processing systems; Electronic journals; Periodicals; 006.454
• DOI: 10.1016/j.csl.2020.101075
• Languages: English
• ISSNs: 0885-2308

Abstract

Highlights: Study of stance in political debates in social media (elections and referendums). Presentation of MultiTACOS: a new multilingual method (English, Spanish, Catalan, French, and Italian). Evaluation of MultiTACOS on two benchmark datasets (SemEval-2016 Task 6 and IberEval 2017 StanceCat task). Study of the impact of new Affective and Contextual features. Creation of a new dataset annotated for stance in French (E-FRA). Creation of a new dataset annotated for stance in Italian (R-ITA). Abstract: Stance Detection is the task of automatically determining whether the author of a text is in favor, against, or neutral towards a given target. In this paper we investigate the portability of tools performing this task across different languages, by analyzing the results achieved by a Stance Detection system (i.e. MultiTACOS) trained and tested in a multilingual setting. First of all, a set of resources on topics related to politics for English, French, Italian, Spanish and Catalan is provided which includes: novel corpora collected for the purpose of this study, and benchmark corpora exploited in Stance Detection tasks and evaluation exercises known in literature. We focus in particular on the novel corpora by describing their development and by comparing them with the benchmarks. Second, MultiTACOS is applied with different sets of features especially designed for Stance Detection, with a specific focus to exploring and combining both features based on the textual content of the tweet (e.g., style and affective load) and features based on contextual information that do not emerge directly from the text. Finally, for better highlighting the contribution of the features that most positively affect system performance in the multilingual setting, a features analysis is provided, together with a qualitative analysis of the misclassified tweets for each of the observed languages, devoted to reflect on the open challenges.