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.
AnandP., WalkerM., AbbottR., TreeJ.E.F., BowmaniR. and MinorM., Cats rule and dogs drool!: Classifying stance in online debate. In Proceedings of the 2Nd Workshop on Computational Approaches to Subjectivity and Sentiment Analysis, WASSA ’11, pages 1–9, Stroudsburg, PA, USA, (2011). Association for Computational Linguistics.
2.
BammanD. and SmithN.A., Contextualized sarcasm detection on Twitter, In Ninth International AAAI Conference on Web and Social Media, (2015).
3.
BlondelV.D., GuillaumeJ.-L., LambiotteR. and LefebvreE., Fast unfolding of communities in large networks, Journal of Statistical Mechanics: Theory and Experiment10 (2008), 10008.
4.
BoscoC. and PattiV., Social media analysis for monitoring political sentiment, In R. Alhajj and J. Rokne, editors, Encyclopedia of Social Network Analysis and Mining, Springer, (2017).
5.
GuerraP.H.C., VelosoA., MeiraW.Jr. and AlmeidaV., From bias to opinion: A transferlearning approach to real-time sentiment analysis, In Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD ’11, pages 150–158, New York, NY, USA, 2011. ACM.
6.
CelliF., StepanovE., PoesioM. and RiccardiG., Predicting brexit: Classifying agreement is better than sentiment and pollsters, In Proceedings of the Workshop on Computational Modeling of People’s Opinions, Personality, and Emotions in Social Media (PEOPLES), pages 110–118,Osaka, Japan, December (2016). ACL Anthology.
7.
ConoverM., RatkiewiczJ., FranciscoM., GoncalvesB., MenczerF. and FlamminiA., Political polarization on Twitter, In International AAAI Conference on Web and Social Media, (2011).
8.
DeitrickW. and HuW., Mutually enhancing community detection and sentiment analysis on twitter networks, Journal of Data Analysis and Information Processing1 (2013), 19–29.
9.
DurantiA. and GoodwinC., Rethinking context: Language as an interactive phenomenon, Cambridge University Press, (1992).
10.
ElejaldeE., FerresL. and HerderE., The nature of real and perceived bias in chilean media, In Proceedings of the 28th ACM Conference on Hypertext and Social Media, HT ’17, pages 95–104, New York, NY, USA, 2017. ACM.
11.
EvansA., Stance and identity in Twitter hashtags, Language@ Internet13(1) (2016).
12.
NielsenF.Å., A new ANEW: Evaluation of a word list for sentiment analysis in microblogs. CoRR, abs/1103.2903, (2011).
13.
FortunatoS., Community detection in graphs, Physics Reports486(3) (2010), 75–174.
14.
GelmanA. and KingG., Why are american presidential election campaign polls so variable when votes are so predictable?British Journal of Political Science23(04) (1993), 409–451.
15.
GonçalvesB., PerraN. and VespignaniA., Modeling users’ activity on Twitter networks: Validation of dunbar’s number, PloS One6(8) (2011), e22656.
16.
GonzalezM.C., HidalgoC.A. and BarabasiA.-L., Understanding individual human mobility patterns, Nature453(7196) (2008), 779–782.
17.
Hernández-CastañedaÁ., CalvoH. and GambinoO.J., Impact of polarity in deception detection, Journal of Intelligent & Fuzzy Systems35(1) (2018), 549–558.
18.
HowardP.N. and KollanyiB., Bots, #strongerin, and #brexit: Computational propaganda during the uk-eu referendum, ArXiv e-prints, (2016).
19.
HuM. and LiuB., Mining and summarizing customer reviews, In Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD ’04, pages 168–177, New York, NY, USA, 2004. ACM.
20.
LaiM., CignarellaA.T. and FariasD.I.H., iTACOS at Iber Eva l2017: Detecting Stance in Catalan and Spanish Tweets. In Notebook Papers of 2nd SEPLN Workshop on Evaluation of Human Language Technologies for Iberian Languages (IBEREVAL), CEUR Workshop Proceedings. CEUR-WS.org, 2017, Murcia, Spain, september (2017).
21.
LaiM., FaríasD.I.H., PattiV. and RossoP., Friends and enemies of Clinton and Trump: Using context for detecting stance in political tweets, In Grigori Sidorov and Oscar Herrera-Alcántara, editors, Advances in Computational Intelligence: 15th Mexican International Conference on Artificial Intelligence, MICAI 2016, Cancún, Mexico, October 23–28, 2016, Proceedings, Part I, pages 155–168. Springer International Publishing, Cham, (2017).
22.
LaiM., PattiV., RuffoG. and RossoP., Stance evolution and Twitter interactions in an Italian political debate, In M. Silberztein, F. Atigui, E. Kornyshova, E. Métais and F. Meziane, editors, Natural Language Processing and Information Systems, NLDB, pages 15–27, Cham, Switzerland, June (2018). Springer International Publishing.
23.
LaiM., TambuscioM., PattiV., RuffoG. and RossoP., Extracting graph topological information and users’ opinion, In G.J.F. Jones, S. Lawless, J. Gonzalo, L. Kelly, L. Goeuriot, T. Mandl, L. Cappellato and N. Ferro, editors, Experimental IR Meets Multilinguality, Multimodality, and Interaction: 8th International Conference of the CLEF Association, CLEF 2017, Dublin, Ireland, September 11–14, 2017, Proceedings, pages 112–118. Springer International Publishing, Cham, (2017).
24.
LaiM., TambuscioM., PattiV., RuffoG. and RossoP., Stance polarity in political debates: A diachronic perspective of network homophily and conversations on Twitter, Data & Knowledge Engineering, (2019).
25.
LazarsfeldP.F. and MertonR.K., Friendship as a social process: A substantive and methodological analysis. In M. Berger, T. Abel and C. Page, editors, Freedom and Control in Modern Society, pages 18–66. Van Nostrand, New York, (1954).
26.
LazerD., (Sandy) PentlandA., AdamicL., AralS., BarabasiA.L., BrewerD., ChristakisN., ContractorN., FowlerJ., GutmannM., JebaraT., KingG., MacyM., RoyD. and Van AlstyneM., Life in the network: the coming age of computational social science, Science323(5915) (2009), 721–723. 19197046[pmid].
27.
LinW.-H., WilsonT., WiebeJ. and HauptmannA., Which side are you on?: Identifying perspectives at the document and sentence levels, In Proceedings of the Tenth Conference on Computational Natural Language Learning, CoNLL-X ’06, pages 109–116, Stroudsburg, PA, USA, (2006). Association for Computational Linguistics.
28.
McPhersonM., Smith-LovinL. and CookJ.M., Birds of a feather: Homophily in social networks, Annual Review of Sociology, (2001), pp. 415–444.
29.
MessinaE., FersiniE. and Zammit-LuciaJ., All Atwitter About Brexit: Lessons for the Election Campaigns, (2017).
30.
MohammadS., KiritchenkoS., SobhaniP., ZhuX. and CherryC., Semeval-2016 task 6: Detecting stance in tweets, In Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016), pages 31–41, San Diego, California, June (2016). Association for Computational Linguistics.
31.
MohammadS.M., KiritchenkoS. and ZhuX., Nrc-canada: Building the state-of-the-art in sentiment analysis of tweets, CoRR, abs/1308.6242, (2013).
32.
MohammadS.M., SobhaniP. and KiritchenkoS., Stance and sentiment in tweets, ACM Trans Internet Technol17(3) (2017), 26:1–26:23.
33.
MohammadS.M. and TurneyP.D., Crowdsourcing a word–emotion association lexicon, Computational Intelligence29(3) (2013), 436–465.
34.
NielsenF.Å., A new ANEW: evaluation of a word list for sentiment analysis in microblogs, In Proceedings of the ESWC 2011 Workshop on ’Making Sense of Microposts’: Big things come in small packages, volume 718 of CEUR Workshop Proceedings, pages 93–98, Heraklion, Crete, Greece, (2011). CEUR-WS.org.
35.
NissimM. and PattiV., Semantic aspects in sentiment analysis, In F.E. Pozzi, Federico, E. Messina and B. Liu, editors, Sentiment Analysis in Social Networks, chapter 3, pages 31–48. Elsevier, (2017).
36.
PangB. and LeeL., Opinion mining and sentiment analysis, Found Trends Inf Retr2(1-2) (2008), 1–135.
37.
PennebakerJ.W., FrancisM.E. and BoothR.J., Linguistic Inquiry and Word Count: LIWC 2001, Mahway: Lawrence Erlbaum Associates71 (2001).
38.
PennebakerJ.W., FrancisM.E. and BoothR.J., Linguistic inquiry and word count: LIWC 2001, Lawrence Erlbaum Associates, Mahwah,NJ, (2001).
39.
SobhaniP., MohammadS. and KiritchenkoS., Detecting stance in tweets and analyzing its interaction with sentiment, In Proceedings of the Fifth Joint Conference on Lexical and Computational Semantics, pages 159–169, Berlin, Germany, August (2016). Association for Computational Linguistics.
40.
SulisE., FaríasD.I.H., RossoP., PattiV. and RuffoG., Figurative messages and affect in Twitter: Differences between #irony, #sarcasm and #not, Knowledge-Based Systems108 (2016), 132–143.
41.
TauléM., MartíM.A., PardoF.M.R., RossoP., BoscoC. and PattiV., Overview of the task of Stance and Gender Detection in Tweets on Catalan Independence at IBEREVAL 2017. In Notebook Papers of 2nd SEPLN Workshop on Evaluation of Human Language Technologies for Iberian Languages (IBEREVAL 2017), volume 1881 of CEUR Workshop Proceedings, pages 157–177, Murcia, Spain, September (2017). CEUR-WS.org.
42.
TheocharisY. and LoweW., Does Facebook increase political participation? Evidence from a field experiment, Information, Communication & Society19(10) (2016), 1465–1486.
43.
WallaceB.C., ChoeD.K. and CharniakE., Sparse, contextually informed models for irony detection: Exploiting user communities, entities and sentiment. In Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing, pages 1035–1044. ACL, (2015).
44.
WengL., KarsaiM., PerraN., MenczerF. and FlamminiA., Attention on weak ties in social and communication networks, arXiv preprint arXiv:1505.02399, (2015).
45.
WhissellC., Using the Revised Dictionary of Affect in Language to Quantify the Emotional Undertones of Samples of Natural Language, Psychological Reports105(2) (2009), 509–521.
46.
WilsonT., WiebeJ. and HoffmannP., Recognizing contextual polarity in phrase-level sentiment analysis, In Proceedings of the Conference on Human Language Technology and Empirical Methods in Natural Language Processing, HLT ’05, pages 347–354, Stroudsburg, PA, USA, (2005). Association for Computational Linguistics.
47.
XuK., LiJ. and LiaoS.S., Sentiment community detection in social networks. In Proceedings of the 2011 iConference, iConference ’11, pages 804–805, New York, NY, USA, (2011). ACM.
48.
ZhangZ. and LanM., Ecnu at semeval 2016 task 6: Relevant or not? supportive or not? a two-step learning system for automatic detecting stance in tweets, In Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016), pages 463–469. (2016).
