Multilingual Multi-Target Stance Recognition in Online Public Consultations

Machine Learning is an interesting tool for stance recognition in a large-scale context, in terms of data size, but also regarding the topics and themes addressed or the languages employed by the participants. Public consultations of citizens using online participatory democracy platforms offer this...

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Bibliographic Details
Published inMathematics (Basel) Vol. 11; no. 9; p. 2161
Main Authors Barriere, Valentin, Balahur, Alexandra
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.05.2023
Subjects
Analysis
Annotations
Attitudes
Classification
Computational linguistics
Datasets
Debates
Democracy
False information
Language
Language processing
Languages
Machine learning
multilingual models
Multilingualism
Natural language interfaces
Natural language processing
online debates
Politics
public consultations
Self-supervised learning
Social networks
stancerecognition
Target recognition
transformers
Europe
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Summary:Machine Learning is an interesting tool for stance recognition in a large-scale context, in terms of data size, but also regarding the topics and themes addressed or the languages employed by the participants. Public consultations of citizens using online participatory democracy platforms offer this kind of setting and are good use cases for automatic stance recognition systems. In this paper, we propose to use three datasets of public consultations, in order to train a model able to classify the stance of a citizen within a text, towards a proposal or a debate question. We studied stance detection in several contexts: using data from an online platform without interactions between users, using multilingual data from online debates that are in one language, and using data from online intra-multilingual debates, which can contain several languages inside the same unique debate discussion. We propose several baselines and methods in order to take advantage of the different available data, by comparing the results of models using out-of-dataset annotations, and binary or ternary annotations from the target dataset. We finally proposed a self-supervised learning method to take advantage of unlabelled data. We annotated both the datasets with ternary stance labels and made them available.
ISSN:2227-7390
2227-7390
DOI:10.3390/math11092161