research-article

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

Authors:

Walid MagdyAuthors Info & Claims

Volume 58, Issue 4

https://doi.org/10.1016/j.ipm.2021.102597

Published: 01 July 2021 Publication History

Abstract

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, where each modeled stance detection in different ways. In this paper, we survey the work on stance detection across those communities and present 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. Our survey reports state-of-the-art results on the existing benchmark datasets on stance detection, and discusses the most effective approaches. In addition, we explore the emerging trends and different applications of stance detection on social media, including opinion mining and prediction and recently using it for fake news detection. The study concludes by discussing the gaps in the current existing research and highlights the possible future directions for stance detection on social media.

Highlights

•

We map out the current research terrain on stance detection and synthesize its relation to the existing theoretical orientations.

•

We provide a broader overview of stance detection methods, covering work that has been published in multiple research domains, including NLP, Computational Social Science,and Web science.

•

We survey the recent advances in stance detection methodologies and potential benchmarks by categorizing and summarizing the current methods.

•

We show the different applications of stance detection on social media and identify open key trends and challenges in the domain.

References

[1]

Aker A., Derczynski L., Bontcheva K., Simple open stance classification for rumour analysis, in: Proceedings of the international conference recent advances in natural language processing, INCOMA Ltd., Varna, Bulgaria, 2017, pp. 31–39,.

[2]

Al-Ayyoub M., Rababah A., Jararweh Y., Al-Kabi M.N., Gupta B.B., Studying the controversy in online crowds’ interactions, Applied Soft Computing 66 (2018) 557–563,.

Digital Library

[3]

Aldayel A., Magdy W., Your stance is exposed! analysing possible factors for stance detection on social media, in: Proc. ACM Hum.-Comput. Interact., vol. 3, Association for Computing Machinery, New York, NY, USA, 2019,.

Digital Library

[4]

Aldayel A., Magdy W., Assessing sentiment of the expressed stance on social media, in: Social informatics, Springer International Publishing, Cham, 2019, pp. 277–286.

[5]

Allaway E., McKeown K., Zero-shot stance detection: A dataset and model using generalized topic representations, in: Proceedings of the 2020 conference on empirical methods in natural language processing, Association for Computational Linguistics, Online, 2020, pp. 8913–8931,.

[6]

Allcott H., Gentzkow M., Social media and fake news in the 2016 election, 2017, pp. 211–236.

[7]

Ammar, R., Mucahid, K., Kareem, D., Tamer, E., & Cansin, B. (2021). Embeddings based clustering for target specific stances: The Case of a Polarized Turkey. In 15th international aaai conference on web and social media.

[8]

Anand, P., Walker, M., Abbott, R., Fox Tree, J. E., Bowmani, R., & Minor, M. (2011). Cats rule and dogs drool!: Classifying stance in online debate. In Proceedings of the 2nd workshop on computational approaches to subjectivity and sentiment analysis (pp. 1–9). Association for Computational Linguistics, Portland, Oregon.

[9]

Augenstein I., Rocktäschel T., Vlachos A., Bontcheva K., Stance detection with bidirectional conditional encoding, in: Proceedings of the 2016 conference on empirical methods in natural language processing, Association for Computational Linguistics, Austin, Texas, 2016, pp. 876–885,.

[10]

Augenstein, I., Vlachos, A., & Bontcheva, K. (2016). USFD at SemEval-2016 Task 6: Any-target stance detection on Twitter with autoencoders. In Proceedings of the 10th international workshop on semantic evaluation (pp. 389–393). Association for Computational Linguistics, San Diego, California, https://doi.org/10.18653/v1/S16-1063.

[11]

Baird S., Sibley D., Pan Y., Talos targets disinformation with fake news challenge victory. [online], 2017, https://blog.talosintelligence.com/2017/06/talos-fake-news-challenge.html.

[12]

Banegas D.L., Language online investigating digital texts and practices, Studies in Second Language Acquisition 36 (3) (2014) 601,.

[13]

Bar-Haim R., Bhattacharya I., Dinuzzo F., Saha A., Slonim N., Stance classification of context-dependent claims, in: Proceedings of the 15th conference of the european chapter of the association for computational linguistics, Valencia, Spain, April 3–7, 2017, Association for Computational Linguistics, 2017, pp. 251–261,.

[14]

Barberá P., Jost J.T., Nagler J., Tucker J.A., Bonneau R., Tweeting from left to right: Is online political communication more than an echo chamber?, Psychological Science 26 (10) (2015) 1531–1542,.

[15]

Bassiouney R., Stance-taking, in: The international encyclopedia of language and social interaction, American Cancer Society, 2015, pp. 1–11,.

[16]

Beigman Klebanov B., Beigman E., Diermeier D., Vocabulary choice as an indicator of perspective, in: Proceedings of the ACL 2010 conference short papers, Association for Computational Linguistics, Uppsala, Sweden, 2010, pp. 253–257.

[17]

Belkaroui R., Faiz R., Elkhlifi A., Conversation analysis on social networking sites, in: 2014 tenth international conference on signal-image technology and internet-based systems, IEEE, 2014, pp. 172–178.

[18]

Benamara F., Taboada M., Mathieu Y., Evaluative language beyond bags of words: Linguistic insights and computational applications, Computational Linguistics 43 (1) (2017) 201–264,.

Digital Library

[19]

Benton, A., & Dredze, M. (2018). Using author embeddings to improve tweet stance classification. In Proceedings of the 2018 EMNLP workshop W-NUT: The 4th workshop on noisy user-generated text (pp. 184–194). Association for Computational Linguistics, Brussels, Belgium, https://doi.org/10.18653/v1/W18-6124.

[20]

Bessi A., Petroni F., Del Vicario M., Zollo F., Anagnostopoulos A., Scala A., et al., Homophily and polarization in the age of misinformation, The European Physical Journal Special Topics 225 (10) (2016) 2047–2059.

[21]

Biber D., Finegan E., Adverbial stance types in English, Discourse Processes 11 (1) (1988) 1–34,.

[22]

Borge-Holthoefer J., Magdy W., Darwish K., Weber I., Content and network dynamics behind Egyptian political polarization on Twitter, in: Proceedings of the 18th ACM conference on computer supported cooperative work & social computing, ACM, 2015, pp. 700–711.

[23]

Borges L., Martins B., Calado P., Combining similarity features and deep representation learning for stance detection in the context of checking fake news, Journal of Data and Information Quality (JDIQ) (2019) 1–26,.

Digital Library

[24]

Bucholtz M., Skapoulli E., Barnwell B., Lee J.-e.J., Entextualized humor in the formation of scientist identities among U.S. undergraduates, Anthropology & Education Quarterly 42 (3) (2011) 177–192,.

[25]

Chauhan D.S., Kumar R., Ekbal A., Attention based shared representation for multi-task stance detection and sentiment analysis, in: International conference on neural information processing, Springer, 2019, pp. 661–669,.

[26]

Cignarella A.T., Lai M., Bosco C., Patti V., Paolo R., et al., SardistanceEVALITA2020: Overview of the task on stance detection in Italian tweets, in: EVALITA 2020 seventh evaluation campaign of natural language processing and speech tools for Italian, Ceur, 2020, pp. 1–10.

[27]

Conforti C., Berndt J., Pilehvar M.T., Giannitsarou C., Toxvaerd F., Collier N., Will-they-won’t-they: A very large dataset for stance detection on Twitter, in: Proceedings of the 58th annual meeting of the association for computational linguistics, Association for Computational Linguistics, Online, 2020, pp. 1715–1724,.

[28]

Cramér H., Mathematical methods of statistics (PMS-9), Princeton university press, 1999.

[29]

Darwish K., Magdy W., Rahimi A., Baldwin T., Abokhodair N., Predicting online islamophobic behavior after #Parisattacks, The Journal of Web Science 4 (3) (2018) 34–52,.

[30]

Darwish K., Magdy W., Zanouda T., Improved stance prediction in a user similarity feature space, in: Proceedings of the 2017 IEEE/ACM international conference on advances in social networks analysis and mining 2017, Sydney, Australia, July 31–August 03, 2017, ACM, 2017, pp. 145–148,.

Digital Library

[31]

Darwish K., Magdy W., Zanouda T., Trump vs. Hillary: What went viral during the 2016 US presidential election, in: Social informatics, Springer International Publishing, Cham, 2017, pp. 143–161.

[32]

Darwish, K., Stefanov, P., Aupetit, M., & Nakov, P. (2020). Unsupervised user stance detection on Twitter. In Proceedings of the international AAAI conference on web and social media, vol. 14 (pp. 141–152).

[33]

Demszky D., Garg N., Voigt R., Zou J., Shapiro J., Gentzkow M., et al., Analyzing polarization in social media: Method and application to tweets on 21 mass shootings, in: Proceedings of the 2019 conference of the north american chapter of the association for computational linguistics: Human language technologies, volume 1 (long and short papers), Association for Computational Linguistics, Minneapolis, Minnesota, 2019, pp. 2970–3005,.

[34]

Derczynski, L., Bontcheva, K., Liakata, M., Procter, R., Wong Sak Hoi, G., & Zubiaga, A. (2017). SemEval-2017 Task 8: RumourEval: Determining rumour veracity and support for rumours. In Proceedings of the 11th international workshop on semantic evaluation (pp. 69–76). Association for Computational Linguistics, Vancouver, Canada, https://doi.org/10.18653/v1/S17-2006.

[35]

Derczynski, L., Bontcheva, K., Lukasik, M., Declerck, T., Scharl, A., & Georgiev, G., et al. (2015). PHEME computing veracity the fourth challenge of big social data. In Proceedings of the extended semantic web conference EU project networking session ESCWPN.

[36]

Dey, K., Shrivastava, R., Kaushik, S., & Mathur, V. (2017). Assessing the effects of social familiarity and stance similarity in interaction dynamics. In International workshop on complex networks and their applications (pp. 843–855). Springer.

[37]

Dias, M., & Becker, K. (2016). Inf-Ufrgs-Opinion-Mining at SemEval-2016 Task 6: Automatic generation of a training corpus for unsupervised identification of stance in tweets. In Proceedings of the 10th international workshop on semantic evaluation (pp. 378–383). Association for Computational Linguistics, San Diego, California, https://doi.org/10.18653/v1/S16-1061.

[38]

Dong R., Sun Y., Wang L., Gu Y., Zhong Y., Weakly-guided user stance prediction via joint modeling of content and social interaction, in: Proceedings of the 2017 ACM on conference on information and knowledge management, CIKM 2017, Singapore, November 06–10, 2017, ACM, 2017, pp. 1249–1258,.

Digital Library

[39]

Dori-Hacohen S., Allan J., Automated controversy detection on the web, in: Advances in information retrieval - 37th european conference on IR research, Vienna, Austria, March 29–April 2, 2015. proceedings, in: Lecture Notes in Computer Science, vol. 9022, 2015, pp. 423–434,.

[40]

Du Bois J.W., The stance triangle, Stance taking in discourse: Subjectivity, evaluation, interaction 164 (3) (2007) 139–182.

[41]

Ebner, S., Wang, F., & Van Durme, B. (2019). Bag-of-words transfer: Non-contextual techniques for multi-task learning. In Proceedings of the 2nd workshop on deep learning approaches for low-resource NLP (pp. 40–46). Association for Computational Linguistics, Hong Kong, China, https://doi.org/10.18653/v1/D19-6105.

[42]

Ebrahimi J., Dou D., Lowd D., A joint sentiment-target-stance model for stance classification in tweets, in: Proceedings of COLING 2016, the 26th international conference on computational linguistics: technical papers, The COLING 2016 Organizing Committee, Osaka, Japan, 2016, pp. 2656–2665.

[43]

Elfardy, H., & Diab, M. (2016). CU-GWU perspective at SemEval-2016 Task 6: Ideological stance detection in informal text. In Proceedings of the 10th international workshop on semantic evaluation (pp. 434–439). Association for Computational Linguistics, San Diego, California, https://doi.org/10.18653/v1/S16-1070.

[44]

Ferreira W., Vlachos A., Emergent: a novel data-set for stance classification, in: Proceedings of the 2016 conference of the north american chapter of the association for computational linguistics: Human language technologies, Association for Computational Linguistics, San Diego, California, 2016, pp. 1163–1168,.

[45]

Ferreira W., Vlachos A., Incorporating label dependencies in multilabel stance detection, in: Proceedings of the 2019 conference on empirical methods in natural language processing and the 9th international joint conference on natural language processing, Association for Computational Linguistics, Hong Kong, China, 2019, pp. 6350–6354,.

[46]

Fraisier O., Cabanac G., Pitarch Y., Besancon R., Boughanem M., Stance classification through proximity-based community detection, in: Proceedings of the 29th on hypertext and social media, Association for Computing Machinery, New York, 2018, pp. 220–228,.

Digital Library

[47]

Fuchs C., Social media: A critical introduction, SAGE Publications Sage UK: London, England, 2018.

Digital Library

[48]

Garimella K., De Francisc iMorales G., Gionis A., Mathioudakis M., Mary, mary, quite contrary: Exposing twitter users to contrarian news, in: Proceedings of the 26th international conference on world wide web companion, International World Wide Web Conferences Steering Committee, 2017, pp. 201–205.

[49]

Garimella V.R.K., Weber I., A long-term analysis of polarization on Twitter, in: Proceedings of the eleventh international conference on web and social media, MontrÉAl, QuÉBec, Canada, May 15–18, 2017, AAAI Press, 2017, pp. 528–531.

[50]

Gautam A., Mathur P., Gosangi R., Mahata D., Sawhney R., Shah R.R., # metooma: Multi-aspect annotations of tweets related to the metoo movement, Proceedings of the International AAAI Conference on Web and Social Media (2019) 209–216.

[51]

Ghanem, B., Rosso, P., & Rangel, F. (2018). Stance detection in fake news a combined feature representation. In Proceedings of the first workshop on fact extraction and verification (pp. 66–71). Association for Computational Linguistics, Brussels, Belgium, https://doi.org/10.18653/v1/W18-5510.

[52]

Ghosh S., Singhania P., Singh S., Rudra K., Ghosh S., Stance detection in web and social media: A comparative study, in: Experimental IR meets multilinguality, multimodality, and interaction, Springer International Publishing, Cham, 2019, pp. 75–87.

[53]

Giorgioni, S., Politi, M., Salman, S., Basili, R., & Croce, D. (2020). UNITOR @ Sardistance2020: Combining transformer-based architectures and transfer learning for robust stance detection. In Proceedings of the seventh evaluation campaign of natural language processing and speech tools for Italian. Final workshop, Online Event, December 17th, 2020, CEUR Workshop Proceedings, vol. 2765, CEUR-WS.org.

[54]

Gorrell, G., Kochkina, E., Liakata, M., Aker, A., Zubiaga, A., & Bontcheva, K., et al. (2019). SemEval-2019 Task 7: RumourEval, determining rumour veracity and support for rumours. In Proceedings of the 13th international workshop on semantic evaluation (pp. 845–854). Association for Computational Linguistics, Minneapolis, Minnesota, USA, https://doi.org/10.18653/v1/S19-2147.

[55]

Gottipati S., Qiu M., Yang L., Zhu F., Jiang J., Predicting users political party using ideological stances, in: Social informatics, Springer International Publishing, Cham, 2013, pp. 177–191.

[56]

Graells-Garrido E., Baeza-Yates R., Lalmas M., Every colour you are: Stance prediction and turnaround in controversial issues, in: 12th ACM conference on web science, in: WebSci ’20, Association for Computing Machinery, New York, NY, USA, 2020, pp. 174–183,.

Digital Library

[57]

Grcar M., Cherepnalkoski D., Mozetic I., Kralj Novak P., Stance and influence of Twitter users regarding the Brexit referendum, Computational Social Networks 4 (1) (2017) 6,.

[58]

Gu Y., Chen T., Sun Y., Wang B., Ideology detection for Twitter users via link analysis, in: Social, cultural, and behavioral modeling, Springer International Publishing, 2017, pp. 262–268.

[59]

Gu Y., Sun Y., Jiang N., Wang B., Chen T., Topic-factorized ideal point estimation model for legislative voting network, in: The 20th ACM SIGKDD international conference on knowledge discovery and data mining, New York, NY, USA - August 24–27, 2014, ACM, 2014, pp. 183–192,.

Digital Library

[60]

Hamidian, S., & Diab, M. (2015). Rumor detection and classification for twitter data. In Proceedings of the fifth international conference on social media technologies, communication, and informatics (pp. 71–77).

[61]

Hanawa K., Sasaki A., Okazaki N., Inui K., Stance detection attending external knowledge from wikipedia, Journal of Information Processing 27 (1) (2019) 499–506,.

[62]

Himelboim I., McCreery S., Smith M.A., Birds of a feather tweet together: Integrating network and content analyses to examine cross-ideology exposure on Twitter, J. Comput. Mediat. Commun. 18 (2) (2013) 40–60,.

Digital Library

[63]

Igarashi, Y., Komatsu, H., Kobayashi, S., Okazaki, N., & Inui, K. (2016). Tohoku at SemEval-2016 Task 6: Feature-based model versus convolutional neural network for stance detection. In Proceedings of the 10th international workshop on semantic evaluation (pp. 401–407). Association for Computational Linguistics, San Diego, California, https://doi.org/10.18653/v1/S16-1065.

[64]

Jaffe A., Stance: sociolinguistic perspectives, Oup Usa, 2009,.

[65]

Jang M., Allan J., Explaining controversy on social media via stance summarization, in: The 41st international ACM SIGIR conference on research & development in information retrieval, Ann Arbor, MI, USA, July 08–12, 2018, ACM, 2018, pp. 1221–1224,.

Digital Library

[66]

Joshi, A., Bhattacharyya, P., & Carman, M. (2016). Political issue extraction model: A novel hierarchical topic model that uses tweets by political and non-political authors. In Proceedings of the 7th workshop on computational approaches to subjectivity, sentiment and social media analysis (pp. 82–90). Association for Computational Linguistics, San Diego, California, https://doi.org/10.18653/v1/W16-0415.

[67]

Jurafsky D., Martin J.H., Speech and language processing, Pearson, London, 2008.

[68]

Karamibekr, M., & Ghorbani, A. A. (2012). Sentiment analysis of social issues. In 2012 international conference on social informatics (pp. 215–221), https://doi.org/10.1109/SocialInformatics.2012.49.

[69]

Kaushal, A., Saha, A., & Ganguly, N. (2021). Stance detection is not classification: Increasing the role of target entities for detecting stance. In Proceedings of the 2021 conference of the north american chapter of the association for computational linguistics: human language technologies.

[70]

Kawintiranon, K., & Singh, L. (2021). Knowledge enhanced masked language model for stance detection. In Proceedings of the 2021 conference of the north american chapter of the association for computational linguistics: human language technologies.

[71]

Kochkina, E., Liakata, M., & Augenstein, I. (2017). Turing at SemEval-2017 Task 8: Sequential approach to rumour stance classification with branch-LSTM. In Proceedings of the 11th international workshop on semantic evaluation (pp. 475–480). Association for Computational Linguistics, Vancouver, Canada, https://doi.org/10.18653/v1/S17-2083.

[72]

Krejzl, P., & Steinberger, J. (2016). UWB at SemEval-2016 Task 6: Stance detection. In Proceedings of the 10th international workshop on semantic evaluation (pp. 408–412). Association for Computational Linguistics, San Diego, California, https://doi.org/10.18653/v1/S16-1066.

[73]

Küçük D., Can F., Stance detection: A survey, ACM Computing Surveys 53 (1) (2020),.

Digital Library

[74]

Lahoti P., Garimella K., Gionis A., Joint non-negative matrix factorization for learning ideological leaning on Twitter, in: Proceedings of the eleventh ACM international conference on web search and data mining, WSDM 2018, Marina Del Rey, CA, USA, February 5-9, 2018, ACM, 2018, pp. 351–359,.

Digital Library

[75]

Lai M., Cignarella A.T., Hernández Farías D.I., Bosco C., Patti V., Rosso P., Multilingual stance detection in social media political debates, Computer Speech and Language 63 (2020),.

[76]

Lai M., Farías D.I.H., Patti V., Rosso P., Friends and enemies of clinton and trump: using context for detecting stance in political tweets, in: Mexican international conference on artificial intelligence, Springer, 2016, pp. 155–168.

[77]

Lai M., Patti V., Ruffo G., Rosso P., Stance evolution and Twitter interactions in an Italian political debate, in: Natural language processing and information systems, Springer International Publishing, 2018, pp. 15–27.

[78]

Li Y., Caragea C., Multi-task stance detection with sentiment and stance lexicons, in: Proceedings of the 2019 conference on empirical methods in natural language processing and the 9th international joint conference on natural language processing, Association for Computational Linguistics, Hong Kong, China, 2019, pp. 6299–6305,.

[79]

Li C., Porco A., Goldwasser D., Structured representation learning for online debate stance prediction, in: Proceedings of the 27th international conference on computational linguistics, Association for Computational Linguistics, Santa Fe, New Mexico, USA, 2018, pp. 3728–3739.

[80]

Liebetrau A.M., Measures of association, Sage, 1983.

[81]

Lin W.-H., Wilson T., Wiebe J., Hauptmann A., Which side are you on? Identifying perspectives at the document and sentence levels, in: Proceedings of the tenth conference on computational natural language learning, Association for Computational Linguistics, New York City, 2006, pp. 109–116.

[82]

Liu, C., Li, W., Demarest, B., Chen, Y., Couture, S., & Dakota, D., et al. (2016). IUCL at SemEval-2016 Task 6: An ensemble model for stance detection in Twitter. In Proceedings of the 10th international workshop on semantic evaluation (pp. 394–400). Association for Computational Linguistics, San Diego, California, https://doi.org/10.18653/v1/S16-1064.

[83]

Lynn, V., Giorgi, S., Balasubramanian, N., & Schwartz, H. A. (2019). Tweet classification without the tweet: An empirical examination of user versus document attributes. In Proceedings of the third workshop on natural language processing and computational social science (pp. 18–28). Association for Computational Linguistics, Minneapolis, Minnesota, https://doi.org/10.18653/v1/W19-2103.

[84]

Ma Y., Peng H., Cambria E., Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM, in: Proceedings of the thirty-second AAAI conference on artificial intelligence, (AAAI-18), the 30th innovative applications of artificial intelligence (IAAI-18), and the 8th AAAI symposium on educational advances in artificial intelligence (EAAI-18), New Orleans, Louisiana, USA, February 2-7, 2018, AAAI Press, 2018, pp. 5876–5883.

[85]

Magdy W., Darwish K., Abokhodair N., Rahimi A., Baldwin T., # isisisnotislam or# deportallmuslims?: Predicting unspoken views, in: Proceedings of the 8th ACM conference on web science, ACM, 2016, pp. 95–106.

[86]

McKendrick D., Webb S., Taking a political stance in social work, Critical and Radical Social Work 2 (3) (2014) 357–369,.

[87]

Mohammad S., A practical guide to sentiment annotation: Challenges and solutions, in: Proceedings of the 7th workshop on computational approaches to subjectivity, sentiment and social media analysis, Association for Computational Linguistics, San Diego, California, 2016, pp. 174–179,.

[88]

Mohammad, S., Kiritchenko, S., Sobhani, P., Zhu, X., & Cherry, C. (2016a). SemEval-2016 Task 6: Detecting stance in tweets, In Proceedings of the 10th international workshop on semantic evaluation (pp. 31–41). Association for Computational Linguistics, San Diego, California, https://doi.org/10.18653/v1/S16-1003.

[89]

Mohammad S., Kiritchenko S., Sobhani P., Zhu X., Cherry C., A dataset for detecting stance in tweets, in: Proceedings of the tenth international conference on language resources and evaluation, 2016, pp. 3945–3952.

[90]

Mohammad S.M., Sobhani P., Kiritchenko S., Stance and sentiment in tweets, ACM Transactions on Internet Technology 17 (3) (2017),.

Digital Library

[91]

Mohtarami M., Baly R., Glass J., Nakov P., Màrquez L., Moschitti A., Automatic stance detection using end-to-end memory networks, in: Proceedings of the 2018 conference of the north american chapter of the association for computational linguistics: Human language technologies, volume 1 (long papers), Association for Computational Linguistics, New Orleans, Louisiana, 2018, pp. 767–776,.

[92]

Momchil, H., Arnav, A., Preslav, N., & Isabelle, A. (2020). A survey on stance detection for mis- and disinformation identification. In ArXiv.

[93]

Murakami A., Raymond R., Support or oppose? Classifying positions in online debates from reply activities and opinion expressions, in: Coling 2010: Posters, Coling 2010 Organizing Committee, Beijing, China, 2010, pp. 869–875.

[94]

Nakov, P., Rosenthal, S., Kozareva, Z., Stoyanov, V., Ritter, A., & Wilson, T. (2013). SemEval-2013 Task 2: Sentiment analysis in Twitter. In Second joint conference on lexical and computational semantics (*SEM). Vol. 2. Proceedings of the seventh international workshop on semantic evaluation (pp. 312–320). Association for Computational Linguistics, Atlanta, Georgia, USA.

[95]

Newman N., Mainstream Media and the Distribution of News in the Age of Social Discovery (RISJ Reports), Reuters Institute for the Study of Journalism, University of Oxford, 2011, pp. 1–58.

[96]

Pang B., Lee L., Opinion mining and sentiment analysis, Foundations and Trends in Information Retrieval 2 (1–2) (2008) 1–135,.

Digital Library

[97]

Patwa, P., Aguilar, G., Kar, S., Pandey, S., PYKL, S., & Garrette, D., et al. (2020). SemEval-2020 sentimix Task 9: Overview of sentiment analysis of code-mixed tweets. In Proceedings of the 14th international workshop on semantic evaluation. Association for Computational Linguistics, Barcelona, Spain.

[98]

Pennacchiotti M., Popescu A., Democrats, republicans and starbucks afficionados: user classification in twitter, in: Proceedings of the 17th ACM SIGKDD international conference on knowledge discovery and data mining, San Diego, CA, USA, ACM, 2011, pp. 430–438,.

Digital Library

[99]

Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., & AL-Smadi, M., et al. (2016). SemEval-2016 Task 5: Aspect based sentiment analysis. In Proceedings of the 10th international workshop on semantic evaluation (pp. 19–30). Association for Computational Linguistics, San Diego, California, https://doi.org/10.18653/v1/S16-1002.

[100]

Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., & Manandhar, S. (2014). SemEval-2014 Task 4: Aspect based sentiment analysis. In Proceedings of the 8th international workshop on semantic evaluation (pp. 27–35). Association for Computational Linguistics, Dublin, Ireland, https://doi.org/10.3115/v1/S14-2004.

[101]

Qazvinian V., Rosengren E., Radev D.R., Mei Q., Rumor has it: Identifying misinformation in microblogs, in: Proceedings of the 2011 conference on empirical methods in natural language processing, Association for Computational Linguistics, Edinburgh, Scotland, UK., 2011, pp. 1589–1599.

[102]

Qiu M., Sim Y., Smith N.A., Jiang J., Modeling user arguments, interactions, and attributes for stance prediction in online debate forums, in: Proceedings of the 2015 SIAM international conference on data mining, Vancouver, BC, Canada, April 30–May 2, 2015, SIAM, 2015, pp. 855–863,.

[103]

Quattrociocchi, W., Scala, A., & Sunstein, C. R.

[104]

Rajadesingan A., Liu H., Identifying users with opposing opinions in Twitter debates, in: International conference on social computing, behavioral-cultural modeling, and prediction, Springer, 2014, pp. 153–160.

[105]

Sen I., Flöck F., Wagner C., On the reliability and validity of detecting approval of political actors in tweets, in: Proceedings of the 2020 conference on empirical methods in natural language processing, Association for Computational Linguistics, Online, 2020, pp. 1413–1426,.

[106]

Sen, A., Sinha, M., Mannarswamy, S., & Roy, S. (2018). Stance classification of multi-perspective consumer health information. In Proceedings of the ACM India joint international conference on data science and management of data (pp. 273–281).

[107]

Shu K., Wang S., Liu H., Beyond news contents: The role of social context for fake news detection, in: Proceedings of the twelfth ACM international conference on web search and data mining, Melbourne, VIC, Australia, February 11–15, 2019, ACM, 2019, pp. 312–320,.

Digital Library

[108]

Siddiqua U.A., Chy A.N., Aono M., Stance detection on microblog focusing on syntactic tree representation, in: International conference on data mining and big data, Springer, 2018, pp. 478–490.

[109]

Siddiqua U.A., Chy A.N., Aono M., Tweet stance detection using an attention based neural ensemble model, in: Proceedings of the 2019 conference of the north american chapter of the association for computational linguistics: Human language technologies, volume 1 (long and short papers), Association for Computational Linguistics, Minneapolis, Minnesota, 2019, pp. 1868–1873,.

[110]

Siddiqua U.A., Chy A.N., Aono M., Tweet stance detection using multi-kernel convolution and attentive LSTM variants, IEICE Transactions on Information and Systems E102d (2019) 2493–2503,.

[111]

Simaki V., Paradis C., Kerren A., Stance classification in texts from blogs on the 2016 british referendum, in: Speech and computer, Springer International Publishing, 2017, pp. 700–709.

[112]

Simaki V., Paradis C., Skeppstedt M., Sahlgren M., Kucher K., Kerren A., Annotating speaker stance in discourse: the brexit blog corpus, Corpus Linguistics and Linguistic Theory 16 (2) (2020) 215–248.

[113]

Singh P.K., Singh S.K., Paul S., Sentiment classification of social issues using contextual valence shifters, International Journal of Engineering and Technology 7 (4) (2015) 1443–1452.

[114]

Sobhani P., Inkpen D., Zhu X., A dataset for multi-target stance detection, in: Proceedings of the 15th conference of the European chapter of the association for computational linguistics: Volume 2, short papers, Association for Computational Linguistics, Valencia, Spain, 2017, pp. 551–557.

[115]

Sobhani P., Inkpen D., Zhu X., Exploring deep neural networks for multitarget stance detection, Computational Intelligence 35 (1) (2019) 82–97,.

[116]

Sobhani P., Mohammad S., Kiritchenko S., Detecting stance in tweets and analyzing its interaction with sentiment, in: Proceedings of the fifth joint conference on lexical and computational semantics, Association for Computational Linguistics, Berlin, Germany, 2016, pp. 159–169,.

[117]

Somasundaran S., Wiebe J., Recognizing stances in online debates, in: Proceedings of the joint conference of the 47th annual meeting of the ACL and the 4th international joint conference on natural language processing of the AFNLP, Association for Computational Linguistics, Suntec, Singapore, 2009, pp. 226–234.

[118]

Stefanov P., Darwish K., Atanasov A., Nakov P., Predicting the topical stance and political leaning of media using tweets, in: Proceedings of the 58th annual meeting of the association for computational linguistics, Association for Computational Linguistics, Online, 2020, pp. 527–537,.

[119]

Sun Q., Wang Z., Zhu Q., Zhou G., Stance detection with hierarchical attention network, in: Proceedings of the 27th international conference on computational linguistics, Santa Fe, New Mexico, USA, August 20–26, 2018, Association for Computational Linguistics, 2018, pp. 2399–2409.

[120]

Tanaka L., Communicative stances in Japanese interviews: Gender differences in formal interactions, Language and Communication 29 (4) (2009) 366–382,.

[121]

Taulé, M., Pardo, F. M. R., Martí, M. A., & Rosso, P. (2018). Overview of the task on multimodal stance detection in tweets on catalan #1oct referendum. In Proceedings of the third workshop on evaluation of human language technologies for iberian languages. Co-Located with 34th conference of the spanish society for natural language processing (pp. 149–166). Sevilla, Spain, September 18th, 2018, vol. 2150, CEUR-WS.org.

[122]

Taulé M., Pardo F.M.R., Martí M.A., Rosso P., Overview of the task on multimodal stance detection in tweets on catalan# 1oct referendum., in: IberEvalSEPLN, 2018, pp. 149–166.

[123]

Thonet T., Cabanac G., Boughanem M., Pinel-Sauvagnat K., Users are known by the company they keep: Topic models for viewpoint discovery in social networks, in: Proceedings of the 2017 ACM on conference on information and knowledge management, Singapore, November 06–10, 2017, ACM, 2017, pp. 87–96,.

Digital Library

[124]

Trabelsi A., Zaïane O.R., Unsupervised model for topic viewpoint discovery in online debates leveraging author interactions, in: Proceedings of the twelfth international conference on web and social media, Stanford, California, USA, June 25-28, 2018, AAAI Press, 2018, pp. 425–433.

[125]

Vijayaraghavan, P., Sysoev, I., Vosoughi, S., & Roy, D. (2016). DeepStance at SemEval-2016 Task 6: Detecting stance in tweets using character and word-level CNNs. In Proceedings of the 10th international workshop on semantic evaluation (pp. 413–419). Association for Computational Linguistics, San Diego, California, https://doi.org/10.18653/v1/S16-1067.

[126]

Walker M.A., Anand P., Abbott R., Tree J.E.F., Martell C., King J., That is your evidence?: Classifying stance in online political debate, Decision Support Systems (2012) 719–729.

[127]

Walker M., Tree J.F., Anand P., Abbott R., King J., A corpus for research on deliberation and debate, in: Proceedings of the eighth international conference on language resources and evaluation, European Language Resources Association (ELRA), Istanbul, Turkey, 2012, pp. 812–817.

[128]

Wang R., Zhou D., Jiang M., Si J., Yang Y., A survey on opinion mining: From stance to product aspect, IEEE Access 7 (2019) 41101–41124,.

[129]

Weber I., Garimella V.R.K., Batayneh A., Secular vs. Islamist polarization in Egypt on Twitter, in: Advances in social networks analysis and mining 2013, Niagara, on, Canada–August 25–29, 2013, ACM, 2013, pp. 290–297,.

Digital Library

[130]

Wei P., Lin J., Mao W., Multi-target stance detection via a dynamic memory-augmented network, in: The 41st international ACM SIGIR conference on research & development in information retrieval, SIGIR 2018, Ann Arbor, MI, USA, July 08–12, 2018, ACM, 2018, pp. 1229–1232,.

Digital Library

[131]

Wei P., Mao W., Chen G., A topic-aware reinforced model for weakly supervised stance detection, in: The thirty-third AAAI conference on artificial intelligence, AAAI Press, 2019, pp. 7249–7256,.

Digital Library

[132]

Wei, W., Zhang, X., Liu, X., Chen, W., & Wang, T. (2016). pkudblab at SemEval-2016 Task 6 : A specific convolutional neural network system for effective stance detection. In Proceedings of the 10th international workshop on semantic evaluation (pp. 384–388). Association for Computational Linguistics, San Diego, California, https://doi.org/10.18653/v1/S16-1062.

[133]

Wojatzki, M., & Zesch, T. (2016). ltl.uni-due at SemEval-2016 Task 6: Stance detection in social media using stacked classifiers. In Proceedings of the 10th international workshop on semantic evaluation (pp. 428–433). Association for Computational Linguistics, San Diego, California, https://doi.org/10.18653/v1/S16-1069.

[134]

Xi N., Ma D., Liou M., Steinert-Threlkeld Z.C., Anastasopoulos J., Joo J., Understanding the political ideology of legislators from social media images, in: Proceedings of the international AAAI conference on web and social media, 2020, pp. 726–737.

[135]

Zarrella, G., & Marsh, A. (2016). MITRE at SemEval-2016 Task 6: Transfer learning for stance detection. In Proceedings of the 10th international workshop on semantic evaluation (pp. 458–463). Association for Computational Linguistics, San Diego, California, https://doi.org/10.18653/v1/S16-1074.

[136]

Zhang Q., Liang S., Lipani A., Ren Z., Yilmaz E., From stances’ imbalance to their hierarchicalrepresentation and detection, in: The world wide web conference, WWW 2019, San Francisco, CA, USA, May 13–17, 2019, ACM, 2019, pp. 2323–2332,.

Digital Library

[137]

Zhang B., Yang M., Li X., Ye Y., Xu X., Dai K., Enhancing cross-target stance detection with transferable semantic-emotion knowledge, in: Proceedings of the 58th annual meeting of the association for computational linguistics, Association for Computational Linguistics, Online, 2020, pp. 3188–3197,.

[138]

Zhou Y., Cristea A.I., Shi L., Connecting targets to tweets: Semantic attention-based model for target-specific stance detection, in: International conference on web information systems engineering, Springer, 2017, pp. 18–32.

[139]

Zhu L., He Y., Zhou D., Hierarchical viewpoint discovery from tweets using Bayesian modelling, Expert Systems with Applications 116 (2019) 430–438,.

[140]

Zubiaga A., Kochkina E., Liakata M., Procter R., Lukasik M., Bontcheva K., et al., Discourse-aware rumour stance classification in social media using sequential classifiers, Information Processing & Management (2018) 273–290.

[141]

Zubiaga A., Liakata M., Procter R., Wong Sak Hoi G., Tolmie P., Analysing how people orient to and spread rumours in social media by looking at conversational threads, PLOS ONE 11 (3) (2016) 1–29,.

Cited By

Liu CZhou KZhou L(2024)Infusing external knowledge into user stance detection in social platformsJournal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology10.3233/JIFS-22421746:1(2161-2177)Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.3233/JIFS-224217
Li HYang WWang WWang H(2024)Harmfulness metrics in digital twins of social network rumors detection in cloud computing environmentJournal of Cloud Computing: Advances, Systems and Applications10.1186/s13677-024-00596-x13:1Online publication date: 9-Feb-2024
https://dl.acm.org/doi/10.1186/s13677-024-00596-x
Fawzi MMagdy W(2024)"Pinocchio had a Nose, You have a Network!": On Characterizing Fake News Spreaders on Arabic Social MediaProceedings of the ACM on Human-Computer Interaction10.1145/36537028:CSCW1(1-20)Online publication date: 26-Apr-2024
https://dl.acm.org/doi/10.1145/3653702
Show More Cited By

Index Terms

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

Index terms have been assigned to the content through auto-classification.

Recommendations

Your Stance is Exposed! Analysing Possible Factors for Stance Detection on Social Media

To what extent user's stance towards a given topic could be inferred? Most of the studies on stance detection have focused on analysing user's posts on a given topic to predict the stance. However, the stance in social media can be inferred from a ...
Stance Detection: A Survey

Automatic elicitation of semantic information from natural language texts is an important research problem with many practical application areas. Especially after the recent proliferation of online content through channels such as social media sites, ...
Assessing Sentiment of the Expressed Stance on Social Media
Social Informatics
Abstract
Stance detection is the task of inferring viewpoint towards a given topic or entity either being supportive or opposing. One may express a viewpoint towards a topic by using positive or negative language. This paper examines how the stance is ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image Information Processing and Management: an International Journal

Information Processing and Management: an International Journal Volume 58, Issue 4

Jul 2021

750 pages

ISSN:0306-4573

Issue’s Table of Contents

Copyright © 2021.

Publisher

Pergamon Press, Inc.

United States

Publication History

Published: 01 July 2021

Author Tags

Author Tags

Qualifiers

Research-article

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

33
Total Citations
View Citations
0
Total Downloads

Downloads (Last 12 months)0
Downloads (Last 6 weeks)0

Reflects downloads up to 23 Sep 2024

Other Metrics

View Author Metrics

Citations

Cited By

Liu CZhou KZhou L(2024)Infusing external knowledge into user stance detection in social platformsJournal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology10.3233/JIFS-22421746:1(2161-2177)Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.3233/JIFS-224217
Li HYang WWang WWang H(2024)Harmfulness metrics in digital twins of social network rumors detection in cloud computing environmentJournal of Cloud Computing: Advances, Systems and Applications10.1186/s13677-024-00596-x13:1Online publication date: 9-Feb-2024
https://dl.acm.org/doi/10.1186/s13677-024-00596-x
Fawzi MMagdy W(2024)"Pinocchio had a Nose, You have a Network!": On Characterizing Fake News Spreaders on Arabic Social MediaProceedings of the ACM on Human-Computer Interaction10.1145/36537028:CSCW1(1-20)Online publication date: 26-Apr-2024
https://dl.acm.org/doi/10.1145/3653702
Vrijenhoek SDaniil SSandel JHollink L(2024)Diversity of What? On the Different Conceptualizations of Diversity in Recommender SystemsProceedings of the 2024 ACM Conference on Fairness, Accountability, and Transparency10.1145/3630106.3658926(573-584)Online publication date: 3-Jun-2024
https://dl.acm.org/doi/10.1145/3630106.3658926
Sáenz CBecker K(2024)Understanding stance classification of BERT models: an attention-based frameworkKnowledge and Information Systems10.1007/s10115-023-01962-y66:1(419-451)Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1007/s10115-023-01962-y
Liew XHameed NClos JFischer J(2023)Predicting Stance to Detect Misinformation in Few-shot LearningProceedings of the First International Symposium on Trustworthy Autonomous Systems10.1145/3597512.3599717(1-5)Online publication date: 11-Jul-2023
https://dl.acm.org/doi/10.1145/3597512.3599717
Deng YHuang Y(2023)Twitter stance detection using deep learning model with FastText EmbeddingProceedings of the 2023 2nd Asia Conference on Algorithms, Computing and Machine Learning10.1145/3590003.3590102(534-541)Online publication date: 17-Mar-2023
https://dl.acm.org/doi/10.1145/3590003.3590102
Miyazaki KMurayama TMatsui ANishikawa MUchiba TKwak HAn JHorvát ÁHall WContractor NFröhling LOgnayova KTaneja HWeber IGligori? KMejova Y(2023)Political Honeymoon Effect on Social Media: Characterizing Social Media Reaction to the Changes of Prime Minister in JapanProceedings of the 15th ACM Web Science Conference 202310.1145/3578503.3583594(1-12)Online publication date: 30-Apr-2023
https://dl.acm.org/doi/10.1145/3578503.3583594
Draws TNatesan Ramamurthy KBaldini IDhurandhar APadhi ITimmermans BTintarev N(2023)Explainable Cross-Topic Stance Detection for Search ResultsProceedings of the 2023 Conference on Human Information Interaction and Retrieval10.1145/3576840.3578296(221-235)Online publication date: 19-Mar-2023
https://dl.acm.org/doi/10.1145/3576840.3578296
Liu CZhou SLiu DLi JHuang ZMa X(2023)CoArgue : Fostering Lurkers’ Contribution to Collective Arguments in Community-based QA PlatformsProceedings of the 2023 CHI Conference on Human Factors in Computing Systems10.1145/3544548.3580932(1-17)Online publication date: 19-Apr-2023
https://dl.acm.org/doi/10.1145/3544548.3580932
Show More Cited By

View Options

View options

Get Access

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

Media

Figures

Other

Tables

View Issue’s Table of Contents