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2016 | OriginalPaper | Buchkapitel

Contrasting Public Opinion Dynamics and Emotional Response During Crisis

verfasst von : Svitlana Volkova, Ilia Chetviorkin, Dustin Arendt, Benjamin Van Durme

Erschienen in: Social Informatics

Verlag: Springer International Publishing

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Abstract

We propose an approach for contrasting spatiotemporal dynamics of public opinions expressed toward targeted entities, also known as stance detection task, in Russia and Ukraine during crisis. Our analysis relies on a novel corpus constructed from posts on the VKontakte social network, centered on local public opinion of the ongoing Russian-Ukrainian crisis, along with newly annotated resources for predicting expressions of fine-grained emotions including joy, sadness, disgust, anger, surprise and fear. Akin to prior work on sentiment analysis we align traditional public opinion polls with aggregated automatic predictions of sentiments for contrastive geo-locations. We report interesting observations on emotional response and stance variations across geo-locations. Some of our findings contradict stereotypical misconceptions imposed by media, for example, we found posts from Ukraine that do not support Euromaidan but support Putin, and posts from Russia that are against Putin but in favor USA. Furthermore, we are the first to demonstrate contrastive stance variations over time across geo-locations using storyline visualization (Storyline visualization is available at http://​www.​cs.​jhu.​edu/​~svitlana/​) technique.

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Vorheriges Kapitel Inferring Population Preferences via Mixtures of Spatial Voting Models
Nächstes Kapitel Social Politics: Agenda Setting and Political Communication on Social Media
Fußnoten
1
Anonymized VK corpus is available upon request at http://​www.​cs.​jhu.​edu/​~svitlana/​.
 
2
Pre-trained models for emotion prediction and data annotated with 6 Ekman’s emotions in Russian and Ukrainian can be found at http://​www.​cs.​jhu.​edu/​~svitlana/​.
 
3
Social Network Analysis Reveals Full Scale of Kremlin’s Twitter Bot: https://​globalvoices.​org/​2015/​04/​02/​analyzing-kremlin-twitter-bots/​.
 
5
Ukraine conflict: Inside Russia’s ’Kremlin troll army’: http://​www.​bbc.​com/​news/​world-europe-31962644.
 
9
We prefer Ekman’s emotion classification over others e.g., Plutchik’s, because we would like to compare the performance of our predictive models to other systems.
 
10
Morphological analyzer for Russian: https://​pypi.​python.​org/​pypi/​pymorphy2.
 
Literatur
1.
Alves, A.L.F., de Souza Baptista, C., Firmino, A.A., de Oliveira, M.G., de Paiva, A.C.: A spatial, temporal sentiment analysis approach applied to Twitter microtexts. J. Inf. Data Manag. 6(2), 118 (2016)
2.
Ashktorab, Z., Brown, C., Nandi, M., Culotta, A., Tweedr: mining Twitter to inform disaster response. In: Proceedings of the International Conference on Information Systems for Crisis Response and Management (SCRAM) (2014)
3.
Bastos, M. T., Mercea, D.: Political Twitter beyond influentials and the twittertariat. New Media & Society, Serial activists (2015)
4.
Bermingham, A., Smeaton, A.F.: On using twitter to monitor political sentiment and predict election results. In: Joint Conference for Natural Language Processing (IJCNLP) (2011)
5.
Bollen, J., Mao, H., Zeng, X.: Twitter mood predicts the stock market. J. Comput. Sci. 2(1), 1–8 (2011)CrossRef
6.
Chang, L., Krosnick, J.A.: National surveys via rdd telephone interviewing versus the internet comparing sample representativeness and response quality. Pub. Opin. Quart. 73(4), 641–678 (2009)CrossRef
7.
Chetviorkin, I., Loukachevitch, N.: Evaluating sentiment analysis systems in Russian. In: Proceedings of ACL (2013)
8.
Chew, C., Eysenbach, G.: Pandemics in the age of Twitter: content analysis of tweets during the 2009 H1N1 outbreak. PloS one 5(11), e14118 (2010)CrossRef
9.
Corley, C.D., Cook, D.J., Mikler, A.R., Singh, K.P.: Text and structural data mining of influenza mentions in web and social media. Int. J. Environ. Res. Pub. Health 7(2), 596–615 (2010)CrossRef
10.
De Choudhury, M., Gamon, M., Counts, S.: Happy, nervous or surprised? Classification of human affective states in social media. In: Proceedings of ICWSM (2012)
11.
Ekman, P.: An argument for basic emotions. Cogn. Emot. 6(3–4), 169–200 (1992)CrossRef
12.
Fan, R., Zhao, J., Chen, Y., Ke, X.: Anger is more influential than joy: sentiment correlation in Weibo. arXiv preprint arXiv:​1309.​2402 (2013)
13.
González-Ibáñez, R., Muresan, S., Wacholder, N.: Identifying sarcasm in Twitter: a closer look. In: Proceedings of ACL, pp. 581–586 (2011)
14.
Ho, S.S., Lieberman, M., Wang, P., Samet, H.: Mining future spatiotemporal events and their sentiment from online news articles for location-aware recommendation system. In: Proceedings of the First ACM SIGSPATIAL International Workshop on Mobile Geographic Information Systems, pp. 25–32. ACM (2012)
15.
Hodas, N.O., Lerman, K.: The simple rules of social contagion. Scientific reports, 4 (2014)
16.
17.
Kim, N.W., Card, S.K., Heer, J.: Tracing genealogical data with timenets. In: Proceedings of the International Conference on Advanced Visual Interfaces, pp. 241–248. ACM (2010)
18.
Kuznetsova, E.S., Loukachevitch, N.V., Chetviorkin, I.I.: Testing rules for a sentiment analysis system. In: Proceedings of International Conference Dialog, pp. 71–80 (2013)
19.
Lampos, V., Bie, T., Cristianini, N.: Flu detector - tracking epidemics on Twitter. In: Balcázar, J.L., Bonchi, F., Gionis, A., Sebag, M. (eds.) ECML PKDD 2010. LNCS (LNAI), vol. 6323, pp. 599–602. Springer, Heidelberg (2010). doi:10.​1007/​978-3-642-15939-8_​42 CrossRef
20.
Lampos, V., Preotiuc-Pietro, D., Cohn, T.: A user-centric model of voting intention from social media. In: Proceedings of ACL, pp. 993–1003 (2013)
21.
Lansdall-Welfare, T., Lampos, V., Cristianini, N.: Effects of the recession on public mood in the UK. In: Proceedings of the 21st International Conference Companion on World Wide Web, pp. 1221–1226. ACM (2012)
22.
23.
24.
Liu, S., Yingcai, W., Wei, E., Liu, M., Liu, Y.: Storyflow: tracking the evolution of stories. IEEE Trans. Visual Comput. Graphics 19(12), 2436–2445 (2013)CrossRef
25.
Loukachevitch, N., Chetviorkin, I.: Open evaluation of sentiment-analysis systems based on the material of the Russian language. Sci. Tech. Inf. Process. 41(6), 370–376 (2014)CrossRef
26.
Loukachevitch, N.V. and Chetviorkin, I.I.:Refinement of Russian sentiment lexicons using ruthes thesaurus. In: Proceedings of the 16th All-Russian Conference on Digital Libraries: Advanced Methods and Technologies, Digital Collections (2014)
27.
Mandel, B., Culotta, A., Boulahanis, J., Stark, D., Lewis, B., Rodrigue, J.: A demographic analysis of online sentiment during hurricane Irene. In: Proceedings of the Second Workshop on Language in Social Media, pp. 27–36. Association for Computational Linguistics (2012)
28.
Mohammad, S.M., Kiritchenko, S.: Using hashtags to capture fine emotion categories from tweets. Comput. Intell. 31(2), 301–326 (2014)MathSciNetCrossRef
29.
Nguyen, L.T., Wu, P., Chan, W., Peng, W., Zhang, Y.: Predicting collective sentiment dynamics from time-series social media. In: Proceedings of the 1st International Workshop on Issues of Sentiment Discovery and Opinion Mining, ACM (2012)
30.
O’Connor, B., Balasubramanyan, R., Routledge, B.R., Smith, N.A.: From tweets to polls: linking text sentiment to public opinion time series. In: Proceedings of ICWSM, pp. 122–129 (2010)
31.
Ogawa, M., Ma, K.L.: Software evolution storylines. In: Proceedings of the 5th International Symposium on Software Visualization, pp. 35–42 (2010)
32.
Pang, B., Lee, L., Vaithyanathan, S., Thumbs up?: sentiment classification using machine learning techniques. In: Proceedings of EMNLP, pp. 79–86 (2002)
33.
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., Duchesnay, E.: Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825–2830 (2011)MathSciNetMATH
34.
35.
36.
37.
Reda, K., Tantipathananandh, C., Johnson, A., Leigh, J., Berger-Wolf, T.: Visualizing the evolution of community structures in dynamic social networks. In: Computer Graphics Forum, vol. 30, pp. 1061–1070. Wiley Online Library (2011)
38.
Rekatsinas, T., Ghosh, S., Mekaru, S.R., Nsoesie, E.O., Brownstein, J.S., Getoor, L., Ramakrishnan, N.: Sourceseer: forecasting rare disease outbreaks using multiple data sources. Timeline 7, 8 (2007)
39.
Salathé, M., Khandelwal, S.: Assessing vaccination sentiments with online social media: implications for infectious disease dynamics and control. PLoS Comput. Biol. 7(10), e1002199 (2011)CrossRef
40.
Santillana, M., Nguyen, A.T., Dredze, M., Paul, M.J., Nsoesie, E.O., Brownstein, J.S.: Combining search, social media, and traditional data sources to improve influenza surveillance. PLoS Comput. Biol. 11(10), e1004513 (2015)CrossRef
41.
Sap, M., Park, G., Eichstaedt, J.C., Kern, M.L., Stillwell, D., Kosinski, M., Ungar, L.H., Schwartz, H.A.: Developing age and gender predictive lexica over social media. In: Proceedings of EMNLP (2014)
42.
Tanahashi, Y., Hsueh, C.-H., Ma, K.-L.: An efficient framework for generating storyline visualizations from streaming data. IEEE Trans. Vis. Comput. Graph. 21(6), 730–742 (2015)CrossRef
43.
Terpstra, T., de Vries, A., Stronkman, R., Paradies, G.L.: Towards a realtime twitter analysis during crises for operational crisis management. In: Proceedings of ISCRAM (2012)
44.
Tufekci, Z.: Big questions for social media big data: representativeness, validity and other methodological pitfalls. arXiv preprint arXiv:​1403.​7400 (2014)
45.
Tumasjan, A., Sprenger, T.O., Sandner, P.G., Welpe, I.M.: Predicting elections with Twitter: what 140 characters reveal about political sentiment. In: Proceedings of ICWSM, pp. 178–185 (2010)
46.
Valitutti, R.: Wordnet-affect: an affective extension of wordnet. In: Proceedings of LREC, pp. 1083–1086 (2004)
47.
Vieweg, S., Hughes, A.L., Starbird, K., Palen, L.: Microblogging during two natural hazards events: what Twitter may contribute to situational awareness. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 1079–1088 (2010)
48.
Volkova, S., Bachrach, Y.: On predicting sociodemographic traits and emotions from communications in social networks and their implications to online self-disclosure. Cyberpsychology Behav. Soc. Networking 18(12), 726–736 (2015)CrossRef
49.
50.
Wang, J., Zhu, T.: Classify sina weibo users into high or low happiness groups using linguistic, behavior features. arXiv preprint arXiv:​ 1507.​01796 (2015)
51.
Wang, Y., Clark, T., Agichtein, E., Staton, J.: Towards tracking political sentiment through microblog data. In: Proceedings of ACL (2014)
Metadaten
Titel
Contrasting Public Opinion Dynamics and Emotional Response During Crisis
verfasst von
Svitlana Volkova
Ilia Chetviorkin
Dustin Arendt
Benjamin Van Durme
Copyright-Jahr
2016
Buch
Social Informatics

Print ISBN: 978-3-319-47879-1

Electronic ISBN: 978-3-319-47880-7

Copyright-Jahr: 2016

DOI
https://doi.org/10.1007/978-3-319-47880-7_19

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