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Erschienen in:
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2018 | OriginalPaper | Buchkapitel

Detecting the Likely Causes Behind the Emotion Spikes of Influential Twitter Users

verfasst von : Calkin Suero Montero, Hatem Haddad, Maxim Mozgovoy, Chedi Bechikh Ali

Erschienen in: Computational Linguistics and Intelligent Text Processing

Verlag: Springer International Publishing

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Abstract

Understanding the causes of spikes in the emotion flow of influential social media users is a key component when analyzing the diffusion and adoption of opinions and trends. Hence, in this work we focus on detecting the likely reasons or causes of spikes within influential Twitter users’ emotion flow. To achieve this, once an emotion spike is identified we use linguistic and statistical analyses on the tweets surrounding the spike in order to reveal the spike’s likely explanations or causes in the form of keyphrases. Experimental evaluation on emotion flow visualization, emotion spikes identification and likely cause extraction for several influential Twitter users shows that our method is effective for pinpointing interesting insights behind the causes of the emotion fluctuation. Implications of our work are highlighted by relating emotion flow spikes to real-world events and by the transversal application of our technique to other types of timestamped text.

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Vorheriges Kapitel Using Syntactic and Semantic Features for Classifying Modal Values in the Portuguese Language
Nächstes Kapitel Age Identification of Twitter Users: Classification Methods and Sociolinguistic Analysis
Fußnoten
1
Meta-analysis on Bill Gates tweeting activity, March 2016, https://​twitter.​com/​BillGates.
 
Literatur
1.
2.
Aral, S., Walker, D.: Identifying influential and susceptible members of social networks. Science 337(6092), 337–341 (2012)MathSciNetCrossRefMATH
3.
Atefeh, F., Khreich, W.: A survey of techniques for event detection in Twitter. Comput. Intell. 31(1), 132–164 (2013)MathSciNetCrossRef
4.
Bakshy, E., Hofman, J.M., Mason, W.A., Watts, D.J.: Everyone’s an influencer: quantifying influence on Twitter. In: Proceedings of the Fourth ACM International Conference on Web Search and Data Mining, pp. 65–74. ACM (2011)
5.
Balog, K., Mishne, G., De Rijke, M.: Why are they excited?: identifying and explaining spikes in blog mood levels. In: Proceedings of the Eleventh Conference of the European Chapter of the Association for Computational Linguistics: Posters and Demonstrations, pp. 207–210. ACL (2006)
6.
7.
Bougie, R., Pieters, R., Zeelenberg, M.: Angry customers don’t come back, they get back: the experience and behavioral implications of anger and dissatisfaction in services. J. Acad. Mark. Sci. 31(4), 377–393 (2003)CrossRef
8.
Bracewell, D.B., Fuji, R., Kuriowa, S.: Multilingual single document keyword extraction for information retrieval. In: Proceedings of the IEEE NLP-KE 2005, pp. 517–522 (2005)
9.
Cha, M., Haddadi, H., Benevenuto, F., Gummadi, P.K.: Measuring user influence in Twitter: the million follower fallacy. In: ICWSM 2010, pp. 10–17, 30 (2010)
10.
Cheng, T., Wicks, T.: Event detection using Twitter: a spatio-temporal approach. PLoS ONE 9(6), e97807 (2014)CrossRef
11.
Gutwin, C., Paynter, G., Witten, I., Nevill-Manning, C., Frank, E.: Improving browsing in digital libraries with keyphrase indexes. Decis. Support Syst. 27(1), 81–104 (1999)CrossRef
12.
Kempter, R., Sintsova, V., Musat, C., Pu, P.: EmotionWatch: visualizing fine-grained emotions in event-related tweets. In: Eighth International AAAI Conference on Weblogs and Social Media (2014)
13.
Kwak, H., Lee, C., Park, H., Moon, S.: What is Twitter, a social network or a news media? In: Proceedings of the 19th International Conference on World Wide Web, pp. 591–600. ACM (2010)
14.
Litvak, M., Last, M.: Graph-based keyword extraction for single-document summarization. In: Proceedings of the Workshop on Multi-source Multilingual Information Extraction and Summarization. ACL (2008)
15.
Litvin, S.W., Goldsmith, R.E., Pan, B.: Electronic word-of-mouth in hospitality and tourism management. Tourism Manag. 29(3), 458–468 (2008)CrossRef
16.
Marcus, A., Bernstein, M.S., Badar, O., Karger, D.R., Madden, S., Miller, R.C.: Tweets as data: demonstration of TweeQL and Twitinfo. In: Proceedings of the ACM SIGMOD, pp. 1259–1262 (2011)
17.
Melville, P., Gryc, W., Lawrence, R.D.: Sentiment analysis of blogs by combining lexical knowledge with text classification. In: Proceedings of the 15th ACM SIGKDD, pp. 1275–1284 (2009)
18.
Mihalcea, R., Tarau, P.: TextRank: bringing order into texts. In: Proceedings of Conference on Empirical Methods in Natural Language Processing, pp. 404–411 (2004)
19.
Mishne, G., De Rijke, M.: Moodviews: tools for blog mood analysis. In: AAAI Spring Symposium on Computational Approaches to Analysing Weblogs, pp. 153–154 (2006)
20.
Mohammad, S.M., Turney, P.D.: Emotions evoked by common words and phrases: using mechanical turk to create an emotion lexicon. In: Proceedings of the NAACL HLT 2010 Workshop on Computational Approaches to Analysis and Generation of Emotion in Text. ACL (2010)
21.
Plutchik, R.: A general psychoevolutionary theory of emotion. Theor. Emot. 1, 3–31 (1980)CrossRef
22.
Psallidas, F., Becker, H., Naaman, M., Gravano, L.: Effective event identification in social media. IEEE Data Eng. Bull. 36(3), 42–50 (2013)
23.
Salton, G., Buckley, C.: Term-weighting approaches in automatic text retrieval. Inf. Process. Manag. 24(5), 513–523 (1988)CrossRef
24.
Song, Y.I., Lee, J.T., Rim, H.C.: Word or phrase?: learning which unit to stress for information retrieval. 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: Volume 2–Volume 2, pp. 1048–1056. ACL (2009)
25.
Turney, P.D.: Learning algorithms for keyphrase extraction. Inf. Retr. 2(4), 303–336 (2000)CrossRef
26.
Trusov, M., Bodapati, A.V., Bucklin, R.E.: Determining influential users in internet social networks. J. Mark. Res. 47(4), 643–658 (2010)CrossRef
27.
Trusov, M., Bucklin, R.E., Pauwels, K.: Effects of word-of-mouth versus traditional marketing: findings from an internet social networking site. J. Mark. 73(5), 90–102 (2009)CrossRef
28.
29.
30.
Valente, T.W., Davis, R.L.: Accelerating the diffusion of innovations using opinion leaders. Ann. Am. Acad. Polit. Soc. Sci. 566(1), 55–67 (1999)CrossRef
31.
Varga, A., Cano Basave, A.E., Rowe, M., Ciravegna, F., He, Y.: Linked knowledge sources for topic classification of microposts: a semantic graph-based approach. J. Web Semant. Sci. Serv. Agents World Wide Web 26, 36–57 (2014)CrossRef
32.
33.
Zesch, T., Gurevych, I.: Approximate matching for evaluating keyphrase extraction. In: RANLP, pp. 484–489 (2009)
Metadaten
Titel
Detecting the Likely Causes Behind the Emotion Spikes of Influential Twitter Users
verfasst von
Calkin Suero Montero
Hatem Haddad
Maxim Mozgovoy
Chedi Bechikh Ali
Copyright-Jahr
2018
Buch
Computational Linguistics and Intelligent Text Processing

Print ISBN: 978-3-319-75486-4

Electronic ISBN: 978-3-319-75487-1

Copyright-Jahr: 2018

DOI
https://doi.org/10.1007/978-3-319-75487-1_29

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