@inproceedings{brugnoli-lo-sardo-2024-community,
title = "Community-based Stance Detection",
author = "Brugnoli, Emanuele and
Lo Sardo, Donald Ruggiero",
editor = "Dell'Orletta, Felice and
Lenci, Alessandro and
Montemagni, Simonetta and
Sprugnoli, Rachele",
booktitle = "Proceedings of the 10th Italian Conference on Computational Linguistics (CLiC-it 2024)",
month = dec,
year = "2024",
address = "Pisa, Italy",
publisher = "CEUR Workshop Proceedings",
url = "https://aclanthology.org/2024.clicit-1.13/",
pages = "98--105",
ISBN = "979-12-210-7060-6",
abstract = "Stance detection is a critical task in understanding the alignment or opposition of statements within social discourse. In this study, we present a novel stance detection model that labels claim-perspective pairs as either aligned or opposed. The primary innovation of our work lies in our training technique, which leverages social network data from X (formerly Twitter). Our dataset comprises tweets from opinion leaders, political entities and news outlets, along with their followers' interactions through retweets and quotes. By reconstructing politically aligned communities based on retweet interactions, treated as endorsements, we check these communities against common knowledge representations of the political landscape.Our training dataset consists of tweet/quote pairs where the tweet comes from a political entity and the quote either originates from a follower who exclusively retweets that political entity (treated as aligned) or from a user who exclusively retweets a political entity from an opposing ideological community (treated as opposed). This curated subset is used to train an Italian language model based on the RoBERTa architecture, achieving an accuracy of approximately 85{\%}. We then apply our model to label all tweet/quote pairs in the dataset, analyzing its out-of-sample predictions.This work not only demonstrates the efficacy of our stance detection model but also highlights the utility of social network structures in training robust NLP models. Our approach offers a scalable and accurate method for understanding political discourse and the alignment of social media statements."
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<abstract>Stance detection is a critical task in understanding the alignment or opposition of statements within social discourse. In this study, we present a novel stance detection model that labels claim-perspective pairs as either aligned or opposed. The primary innovation of our work lies in our training technique, which leverages social network data from X (formerly Twitter). Our dataset comprises tweets from opinion leaders, political entities and news outlets, along with their followers’ interactions through retweets and quotes. By reconstructing politically aligned communities based on retweet interactions, treated as endorsements, we check these communities against common knowledge representations of the political landscape.Our training dataset consists of tweet/quote pairs where the tweet comes from a political entity and the quote either originates from a follower who exclusively retweets that political entity (treated as aligned) or from a user who exclusively retweets a political entity from an opposing ideological community (treated as opposed). This curated subset is used to train an Italian language model based on the RoBERTa architecture, achieving an accuracy of approximately 85%. We then apply our model to label all tweet/quote pairs in the dataset, analyzing its out-of-sample predictions.This work not only demonstrates the efficacy of our stance detection model but also highlights the utility of social network structures in training robust NLP models. Our approach offers a scalable and accurate method for understanding political discourse and the alignment of social media statements.</abstract>
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%0 Conference Proceedings
%T Community-based Stance Detection
%A Brugnoli, Emanuele
%A Lo Sardo, Donald Ruggiero
%Y Dell’Orletta, Felice
%Y Lenci, Alessandro
%Y Montemagni, Simonetta
%Y Sprugnoli, Rachele
%S Proceedings of the 10th Italian Conference on Computational Linguistics (CLiC-it 2024)
%D 2024
%8 December
%I CEUR Workshop Proceedings
%C Pisa, Italy
%@ 979-12-210-7060-6
%F brugnoli-lo-sardo-2024-community
%X Stance detection is a critical task in understanding the alignment or opposition of statements within social discourse. In this study, we present a novel stance detection model that labels claim-perspective pairs as either aligned or opposed. The primary innovation of our work lies in our training technique, which leverages social network data from X (formerly Twitter). Our dataset comprises tweets from opinion leaders, political entities and news outlets, along with their followers’ interactions through retweets and quotes. By reconstructing politically aligned communities based on retweet interactions, treated as endorsements, we check these communities against common knowledge representations of the political landscape.Our training dataset consists of tweet/quote pairs where the tweet comes from a political entity and the quote either originates from a follower who exclusively retweets that political entity (treated as aligned) or from a user who exclusively retweets a political entity from an opposing ideological community (treated as opposed). This curated subset is used to train an Italian language model based on the RoBERTa architecture, achieving an accuracy of approximately 85%. We then apply our model to label all tweet/quote pairs in the dataset, analyzing its out-of-sample predictions.This work not only demonstrates the efficacy of our stance detection model but also highlights the utility of social network structures in training robust NLP models. Our approach offers a scalable and accurate method for understanding political discourse and the alignment of social media statements.
%U https://aclanthology.org/2024.clicit-1.13/
%P 98-105
Markdown (Informal)
[Community-based Stance Detection](https://aclanthology.org/2024.clicit-1.13/) (Brugnoli & Lo Sardo, CLiC-it 2024)
ACL
- Emanuele Brugnoli and Donald Ruggiero Lo Sardo. 2024. Community-based Stance Detection. In Proceedings of the 10th Italian Conference on Computational Linguistics (CLiC-it 2024), pages 98–105, Pisa, Italy. CEUR Workshop Proceedings.