Stance Polarity in Political Debates: a Diachronic Perspective of Network Homophily and Conversations on Twitter

[EN] In the last decade, social media gained a very significant role in public debates, and despite the many intrinsic difficulties of analyzing data streaming from on-line platforms that are poisoned by bots, trolls, and low-quality information, it is undeniable that such data can still be used to test the public opinion and overall mood and to investigate how individuals communicate with each other. With the aim of analyzing the debate in Twitter on the 2016 referendum on the reform of the Italian Constitution, we created an Italian annotated corpus for stance detection for automatically estimating the stance of a relevant number of users. We take into account a diachronic perspective to shed lights on users' opinion dynamics. Furthermore, different types of social network communities, based on friendships, retweets, quotes, and replies were investigated, in order to analyze the communication among users with similar and divergent viewpoints. We observe particular aspects of users' behavior. First, our analysis suggests that users tend to be less explicit in expressing their stances after the outcome of the vote; simultaneously, users who exhibit a high number of cross-stance relations tend to become less polarized or to adopt a more neutral style in the following phase of the debate. Second, despite social media networks are generally aggregated in homogeneous communities, we highlight that the structure of the network can strongly change when different types of social relations are considered. In particular, networks defined by means of reply-to messages exhibit inverse homophily by stance, and users use more often replies for expressing diverging opinions, instead of other forms of communication. Interestingly, we also observe that the political polarization increases forthcoming the election and decreases after the election day.The work of Viviana Patti and Giancarlo Ruffo was partially funded by the Fondazione CRT under research project the Hate Speech and Social Media (2016.0688), and the "Progetto di Ateneo/CSP 2016" under research project "Immigrants, Hate and Prejudice in Social Media" (S1618_L2_BOSC_01). The work of Paolo Rosso was partially funded by the Spanish MICINN under the research project "MISMIS-FAKEnHATE on Misinformation and Miscommunication in social media: FAKE news and HATE speech" (PGC2018-096212-B-C31).Lai, M.; Tambuscio, M.; Patti, V.; Ruffo, G.; Rosso, P. (2019). Stance Polarity in Political Debates: a Diachronic Perspective of Network Homophily and Conversations on Twitter. Data & Knowledge Engineering. 124:1-20. https://doi.org/10.1016/j.datak.2019.101738S12012

Multilingual Stance Detection in Social Media Political Debates

[EN] 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.Cristina Bosco and Viviana Patti are partially supported by Progetto di Ateneo/CSP 2016 (Immigrants, Hate and Prejudice in Social Media, S1618_L2_BOSC_01). The work of Paolo Rosso was partially funded bythe Spanish MICINN under the research project MISMIS-FAKEnHATE on MISinformation and MIScommunication in social media: FAKE news and HATE speech (PGC2018096212-B-C31).Lai, M.; Cignarella, AT.; Hernandez-Farias, DI.; Bosco, C.; Patti, V.; Rosso, P. (2020). Multilingual Stance Detection in Social Media Political Debates. Computer Speech & Language. 63:1-27. https://doi.org/10.1016/j.csl.2020.101075S12763Balahur, A., & Turchi, M. (2014). Comparative experiments using supervised learning and machine translation for multilingual sentiment analysis. Computer Speech & Language, 28(1), 56-75. doi:10.1016/j.csl.2013.03.004Blondel, V. D., Guillaume, J.-L., Lambiotte, R., & Lefebvre, E. (2008). Fast unfolding of communities in large networks. Journal of Statistical Mechanics: Theory and Experiment, 2008(10), P10008. doi:10.1088/1742-5468/2008/10/p10008Boiy, E., & Moens, M.-F. (2008). A machine learning approach to sentiment analysis in multilingual Web texts. Information Retrieval, 12(5), 526-558. doi:10.1007/s10791-008-9070-zDellaPosta, D., Shi, Y., & Macy, M. (2015). Why Do Liberals Drink Lattes? American Journal of Sociology, 120(5), 1473-1511. doi:10.1086/681254Küçük, D., Can, F., 2019. A tweet dataset annotated for named entity recognition and stance detection. arXiv preprint arXiv:1901.04787. Available at: https://arxiv.org.Mohammad, S. M., & Turney, P. D. (2012). CROWDSOURCING A WORD-EMOTION ASSOCIATION LEXICON. Computational Intelligence, 29(3), 436-465. doi:10.1111/j.1467-8640.2012.00460.xMohammad, S. M., Sobhani, P., & Kiritchenko, S. (2017). Stance and Sentiment in Tweets. ACM Transactions on Internet Technology, 17(3), 1-23. doi:10.1145/3003433Raghavan, U. N., Albert, R., & Kumara, S. (2007). Near linear time algorithm to detect community structures in large-scale networks. Physical Review E, 76(3). doi:10.1103/physreve.76.036106Vychegzhanin, S. V., & Kotelnikov, E. V. (2019). Stance Detection Based on Ensembles of Classifiers. Programming and Computer Software, 45(5), 228-240. doi:10.1134/s0361768819050074West, D. M. (1991). Polling effects in election campaigns. Political Behavior, 13(2), 151-163. doi:10.1007/bf00992294Whissell, C. (2009). Using the Revised Dictionary of Affect in Language to Quantify the Emotional Undertones of Samples of Natural Language. Psychological Reports, 105(2), 509-521. doi:10.2466/pr0.105.2.509-521Zappavigna, M. (2015). Searchable talk: the linguistic functions of hashtags. Social Semiotics, 25(3), 274-291. doi:10.1080/10350330.2014.99694

#Brexit: Leave or Remain? The Role of User's Community and Diachronic Evolution on Stance Detection

[EN] Interest has grown around the classification of stance that users assume within online debates in recent years. Stance has been usually addressed by considering users posts in isolation, while social studies highlight that social communities may contribute to influence users¿ opinion. Furthermore, stance should be studied in a diachronic perspective, since it could help to shed light on users¿ opinion shift dynamics that can be recorded during the debate. We analyzed the political discussion in UK about the BREXIT referendum on Twitter, proposing a novel approach and annotation schema for stance detection, with the main aim of investigating the role of features related to social network community and diachronic stance evolution. Classification experiments show that such features provide very useful clues for detecting stance.The work of P. Rosso was partially funded by the Spanish MICINN under the research projects MISMIS-FAKEnHATE on Misinformation and Miscommunication in social media: FAKE news and HATE speech(PGC2018-096212-B-C31) and PROMETEO/2019/121 (DeepPattern) of the Generalitat Valenciana. The work of V. Patti and G. Ruffo was partially funded by Progetto di Ateneo/CSP 2016 Immigrants, Hate and Prejudice in Social Media (S1618 L2 BOSC 01).Lai, M.; Patti, V.; Ruffo, G.; Rosso, P. (2020). #Brexit: Leave or Remain? The Role of User's Community and Diachronic Evolution on Stance Detection. Journal of Intelligent & Fuzzy Systems. 39(2):2341-2352. https://doi.org/10.3233/JIFS-179895S23412352392Blondel, V. D., Guillaume, J.-L., Lambiotte, R., & Lefebvre, E. (2008). Fast unfolding of communities in large networks. Journal of Statistical Mechanics: Theory and Experiment, 2008(10), P10008. doi:10.1088/1742-5468/2008/10/p10008Deitrick, W., & Hu, W. (2013). Mutually Enhancing Community Detection and Sentiment Analysis on Twitter Networks. Journal of Data Analysis and Information Processing, 01(03), 19-29. doi:10.4236/jdaip.2013.13004Duranti A. and Goodwin C. , Rethinking context: Language as an interactive phenomenon, Cambridge University Press, (1992).Evans A. , Stance and identity in Twitter hashtags, Language@ Internet 13(1) (2016).Fortunato, S. (2010). Community detection in graphs. Physics Reports, 486(3-5), 75-174. doi:10.1016/j.physrep.2009.11.002Gelman, A., & King, G. (1993). Why Are American Presidential Election Campaign Polls So Variable When Votes Are So Predictable? British Journal of Political Science, 23(4), 409-451. doi:10.1017/s0007123400006682Gonçalves, B., Perra, N., & Vespignani, A. (2011). Modeling Users’ Activity on Twitter Networks: Validation of Dunbar’s Number. PLoS ONE, 6(8), e22656. doi:10.1371/journal.pone.0022656González, M. C., Hidalgo, C. A., & Barabási, A.-L. (2008). Understanding individual human mobility patterns. Nature, 453(7196), 779-782. doi:10.1038/nature06958Hernández-Castañeda, Á., Calvo, H., & Gambino, O. J. (2018). Impact of polarity in deception detection. Journal of Intelligent & Fuzzy Systems, 35(1), 549-558. doi:10.3233/jifs-169610Lazer, D., Pentland, A., Adamic, L., Aral, S., Barabási, A.-L., Brewer, D., … Van Alstyne, M. (2009). Computational Social Science. Science, 323(5915), 721-723. doi:10.1126/science.1167742Mohammad, S. M., Sobhani, P., & Kiritchenko, S. (2017). Stance and Sentiment in Tweets. ACM Transactions on Internet Technology, 17(3), 1-23. doi:10.1145/3003433Mohammad, S. M., & Turney, P. D. (2012). CROWDSOURCING A WORD-EMOTION ASSOCIATION LEXICON. Computational Intelligence, 29(3), 436-465. doi:10.1111/j.1467-8640.2012.00460.xPang, B., & Lee, L. (2008). Opinion Mining and Sentiment Analysis. Foundations and Trends® in Information Retrieval, 2(1–2), 1-135. doi:10.1561/1500000011Pennebaker J.W. , Francis M.E. and Booth R.J. , Linguistic Inquiry and Word Count: LIWC 2001, Mahway: Lawrence Erlbaum Associates 71 (2001).Sulis, E., Irazú Hernández Farías, D., Rosso, P., Patti, V., & Ruffo, G. (2016). Figurative messages and affect in Twitter: Differences between #irony, #sarcasm and #not. Knowledge-Based Systems, 108, 132-143. doi:10.1016/j.knosys.2016.05.035Theocharis, Y., & Lowe, W. (2015). Does Facebook increase political participation? Evidence from a field experiment. Information, Communication & Society, 19(10), 1465-1486. doi:10.1080/1369118x.2015.1119871Whissell, C. (2009). Using the Revised Dictionary of Affect in Language to Quantify the Emotional Undertones of Samples of Natural Language. Psychological Reports, 105(2), 509-521. doi:10.2466/pr0.105.2.509-52

Capturing stance dynamics in social media: open challenges and research directions

Social media platforms provide a goldmine for mining public opinion on issues of wide societal interest and impact. Opinion mining is a problem that can be operationalised by capturing and aggregating the stance of individual social media posts as supporting, opposing or being neutral towards the issue at hand. While most prior work in stance detection has investigated datasets that cover short periods of time, interest in investigating longitudinal datasets has recently increased. Evolving dynamics in linguistic and behavioural patterns observed in new data require adapting stance detection systems to deal with the changes. In this survey paper, we investigate the intersection between computational linguistics and the temporal evolution of human communication in digital media. We perform a critical review of emerging research considering dynamics, exploring different semantic and pragmatic factors that impact linguistic data in general, and stance in particular. We further discuss current directions in capturing stance dynamics in social media. We discuss the challenges encountered when dealing with stance dynamics, identify open challenges and discuss future directions in three key dimensions: utterance, context and influence

Exploring the vaccine conversation on TikTok in Italy: beyond classic vaccine stances

TikTok, a social media platform for creating and sharing short videos, has seen a surge in popularity during the COVID-19 pandemic. To analyse the Italian vaccine conversation on TikTok, we downloaded a sample of videos with a high play count (Top Videos), identified through an unofficial Application Programming Interface (consistent with TikTok’s Terms of Service), and collected public videos from vaccine sceptic users through snowball sampling (Vaccine Sceptics’ videos). The videos were analysed using qualitative and quantitative methods, in terms of vaccine stance, tone of voice, topic, conformity with TikTok style, and other characteristics. The final datasets consisted of 754 Top Videos (by 510 single users) plus 180 Vaccine Sceptics’ videos (by 29 single users), posted between January 2020 and March 2021. In 40.5% of the Top Videos the stance was promotional, 33.9% were indefinite-ironic, 11.3% were neutral, 9.7% were discouraging, and 3.1% were ambiguous (i.e. expressing an ambivalent stance towards vaccines); 43% of promotional videos were from healthcare professionals. More than 95% of the Vaccine Sceptic videos were discouraging. Multiple correspondence analysis showed that, compared to other stances, promotional videos were more frequently created by healthcare professionals and by females, and their most frequent topic was herd immunity. Discouraging videos were associated with a polemical tone of voice and their topics were conspiracy and freedom of choice. Our analysis shows that Italian vaccine-sceptic users on TikTok are limited in number and vocality, and the large proportion of videos with an indefinite-ironic stance might imply that the incidence of affective polarisation could be lower on TikTok, compared to other social media, in the Italian context. Safety is the most frequent concern of users, and we recorded an interesting presence of healthcare professionals among the creators. TikTok should be considered as a medium for vaccine communication and for vaccine promotion campaigns

Networked partisanship and framing: A socio-semantic network analysis of the Italian debate on migration

The huge amount of data made available by the massive usage of social media has opened up the unprecedented possibility to carry out a data-driven study of political processes. While particular attention has been paid to phenomena like elite and mass polarization during online debates and echo-chambers formation, the interplay between online partisanship and framing practices, jointly sustaining adversarial dynamics, still remains overlooked. With the present paper, we carry out a socio-semantic analysis of the debate about migration policies observed on the Italian Twittersphere, across the period May-November 2019. As regards the social analysis, our methodology allows us to extract relevant information about the political orientation of the communities of users—hereby called partisan communities—without resorting upon any external information. Remarkably, our community detection technique is sensitive enough to clearly highlight the dynamics characterizing the relationship among different political forces. As regards the semantic analysis, our networks of hashtags display a mesoscale structure organized in a core-periphery fashion, across the entire observation period. Taken altogether, our results point at different, yet overlapping, trajectories of conflict played out using migration issues as a backdrop. A first line opposes communities discussing substantively of migration to communities approaching this issue just to fuel hostility against political opponents; within the second line, a mechanism of distancing between partisan communities reflects shifting political alliances within the governmental coalition. Ultimately, our results contribute to shed light on the complexity of the Italian political context characterized by multiple poles of partisan alignment

Is implicit communication quantifiable? A corpus-based analysis of British and Italian political tweets

Twitter is nowadays a powerful means of political propaganda. Its effectiveness can be easily appreciated in the large amounts of messages exchanged by politicians every day. This wealth of data, together with the interactive nature of the social medium, provides an ideal basis for the analysis of a striking feature of political messages, i.e., their implicitness, often achieved using presuppositions, among other strategies. The present work proposes a comparative analysis of British and Italian politicians' use of Twitter by focusing on implicit communication (notably, presuppositions) and the pragmatic functions of tweets. Based on a sample of about 400 tweets, our analysis shows that some of these functions tend to associate either with presuppositional or non-presuppositional communicative devices. Moreover, a critical methodological discussion is offered in order to address the main challenges of quantitative corpus-based pragmatics

Stance detection on social media: State of the art and trends

Stance detection on social media is an emerging opinion mining paradigm for various social and political applications in which sentiment analysis may be sub-optimal. There has been a growing research interest for developing effective methods for stance detection methods varying among multiple communities including natural language processing, web science, and social computing. This paper surveys the work on stance detection within those communities and situates its usage within current opinion mining techniques in social media. It presents an exhaustive review of stance detection techniques on social media, including the task definition, different types of targets in stance detection, features set used, and various machine learning approaches applied. The survey reports state-of-the-art results on the existing benchmark datasets on stance detection, and discusses the most effective approaches. In addition, this study explores the emerging trends and different applications of stance detection on social media. The study concludes by discussing the gaps in the current existing research and highlights the possible future directions for stance detection on social media.Comment: We request withdrawal of this article sincerely. We will re-edit this paper. Please withdraw this article before we finish the new versio