Community-based Stance Detection
Authors: Emanuele Brugnoli, Donald Ruggiero Lo Sardo (Sony Computer Science Laboratories Rome, Centro Studi e Ricerche Enrico Fermi (CREF), Sapienza Università di Roma, 2024)
Topic: Sentiment, Emotion, Irony, Hate

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.

Introduction
Stance detection is a critical task within the domain of natural language processing (NLP). It involves identifying the position or attitude expressed in a piece of text towards a specific topic, claim, or entity AUTHOR. Traditionally, stances are classified into three primary categories: favor, against, and neutral. This classification enables a detailed description of textual data, facilitating a deeper insight into public opinion and discourse dynamics. In recent years, the proliferation of digital communication platforms such as social media, forums, and online news outlets has resulted in an unprecedented volume of user-generated content. This surge underscores the necessity for automated systems capable of efficiently analyzing and interpreting these vast text corpora. Stance detection addresses this need by providing tools that can systematically assess opinions and reactions embedded within texts, thus offering valuable applications across various fields including social media analysis AUTHOR, search engines AUTHOR, and linguistics AUTHOR. According to the last report of World Economic Forum AUTHOR, the increase in societal polarization features among the top three risks for democratic societies. While a macroscopic increase of polarization has been observed, an understanding of the microscopic pathways though which it develops is still an open field of research. Through stance detection it would be possible to reconstruct these pathways down to the individual text-comment pairs. Stance detection, has been explored across various fields with differing definitions and applications. Du Bois introduces the concept of the stance triangle, where stance-taking involves evaluating objects, positioning subjects, and aligning with others in dialogic interactions, emphasizing the sociocognitive aspects and intersubjectivity in discourse AUTHOR. Sayah and Hashemi focus on academic writing, analyzing stance and engagement features like hedges, self-mention, and appeals to shared knowledge to understand communicative styles and interpersonal strategies AUTHOR. Küçük and Can define stance detection as the classification of an author's position towards a target (favor, against, or neutral), highlighting its importance in sentiment analysis, misinformation detection, and argument mining AUTHOR. These diverse approaches underscore the multifaceted nature of stance detection and its applications in enhancing the understanding of social discourse, academic rhetoric, and online content analysis. For a review of the recent developments of the field we refer to Alturayeif et al. AUTHOR and AlDayel et al. AUTHOR. In this work, we propose a novel approach to training stance detection models by leveraging the interactions within highly polarized communities. Our method utilizes tweet/quote pairs from the Italian political debate to construct a robust training set. We operate under the assumption that users who predominantly retweet a particular political profile are likely in agreement with the statements made by that profile. We restricted our analysis to retweet since this form of communication primarily aligns with the endorsement hypothesis AUTHOR. Namely, being a simple re-posting of a tweet, retweeting is commonly thought to express agreement with the claim of the tweet AUTHOR. Further, though retweets might be used with other purposes such as those described by Marsili AUTHOR, the repeated nature of the interaction we observe in our networks reduces the probability that the activity falls outside of the endorsement behavior. Conversely, while quoting a tweet works similarly to retweeting, the function allows users to add their own comments above the tweet. This makes this form of communication controversial regarding the endorsement hypothesis, as agreement or disagreement with the tweet depends on the stance of the added comment. On the other hand, the information social media users see, consume, and share through their news feed heavily depends on the political leaning of their early connections AUTHOR. In other words, while algorithms are highly influential in determining what people see and shaping their on-platform experiences AUTHOR, there is significant ideological segregation in political news exposure AUTHOR. It is therefore reasonable to expect that users who almost exclusively retweet a political entity (party, leader, or both) use quote tweets to express agreement with statements posted by that entity and disagreement with statements posted by political entities ideologically distant from their preferred one. Additionally, the quote interaction perfectly encapsulates the stance triangle described by Du Bois AUTHOR. In order to correctly assess political opposition we construct a retweet network and use the Louvain community detection algorithm AUTHOR to characterize leaders and, through label propagation, the followers that align with their views. Through these community labels we construct a dataset of claim-perspective couples by annotating tweet-quote pairs from profiles that clearly express political alignment as favor and annotating tweet-quote pairs in which the profiles come from different communities as against. Finally, we use a pretrained BERT model for Italian language and fine-tune it to the classification task. This methodology aims to enhance the accuracy of stance detection models by incorporating real-world patterns of agreement and disagreement observed in polarized online environments. Further, it enables an unsupervised training paradigm that can be scaled to very large datasets. In the following sections, we will outline the data gathering approach used for the dataset. Subsequently, we will describe the community detection methods employed to identify leaders and users within the Italian political discourse. We will then discuss the model architecture and its training process. In the results section, we will evaluate the model's performance and present our findings. Finally, the conclusion will address potential future developments, the implications of our work, and its limitations.

Conclusion
This study introduces a novel stance detection model that significantly advances the understanding of alignment and opposition in social discourse. By leveraging social network data from X (formerly Twitter), we developed a robust training technique that utilizes interactions within politically aligned communities. Our approach involved curating a dataset of tweet/quote pairs, where the quotes are derived from users' interactions with leaders and politicians. This dataset facilitated the training of a BERT model, which achieved a state of the art accuracy of approximately 85%. Our findings underscore the efficacy of using social network structures to train NLP models, demonstrating that retweet interactions can serve as reliable indicators of political alignment. This methodology not only enhances the scalability of stance detection but also offers a nuanced understanding of political discourse on social media platforms. By reconstructing and validating politically aligned communities through expert knowledge, our model provides a robust framework for analyzing the alignment of social media statements. The implications of this work extend beyond stance detection, offering potential applications in monitoring political sentiment, identifying misinformation, and understanding public opinion dynamics. Future research could explore the integration of additional social network features and exploring the capacity of the model to generalize to other domains, interaction types and understanding how stance propagates within networks. Additionally, investigating the role of specific linguistic markers like adverbs across different languages and cultures can reveal universal and language-specific determinants of stance. While our model shows promising results, it also relies heavily on the assumption that retweets are mainly a form of endorsement, and that quotes within one's own political community are all in agreement and that outside of one's political community they are all in disagreement. While the high level of polarization observed in these networks support the validity of these assumptions, it also restricts the applicability of the model to domains where polarization is evident and these assumptions are valid. We extend our deepest gratitude to Vittorio Loreto, the director of the Sony Computer Science Laboratories (CSL) and Professor at La Sapienza University of Rome, for his invaluable support and sponsorship of this research. His guidance was pivotal for the successful completion of our study. We also thank the anonymous reviewers for their insightful suggestions, which have greatly contributed to enhancing the quality of this work. \appendix