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Erschienen in:
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2017 | OriginalPaper | Buchkapitel

“Come Together!”: Interactions of Language Networks and Multilingual Communities on Twitter

verfasst von : Nabeel Albishry, Tom Crick, Theo Tryfonas

Erschienen in: Computational Collective Intelligence

Verlag: Springer International Publishing

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Abstract

Emerging tools and methodologies are providing insight into the factors that promote the propagation of information in online social networks following significant activities, such as high-profile international social or societal events. This paper presents an extensible approach for analysing how different language communities engage and interact on the social networking platform Twitter via an analysis of the Eurovision Song Contest held in Stockholm, Sweden, in May 2016. By utilising language information from user profiles (N = 1,226,959) and status updates (N = 7,926,746) to identify and categorise communities, our approach is able to categorise these interactions, as well as construct network graphs to provide further insight on these multilingual communities. The results show that multilingualism is positively correlated with activity whilst negatively correlated with posting in the user’s own language.

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Vorheriges Kapitel Study for Automatic Classification of Arabic Spoken Documents
Nächstes Kapitel Bangla News Summarization
Fußnoten
1
For Twitter, status may also be referred to as post, or tweet.
 
2
In this context, different language refers to tweet’s language that is different to the user profile language settings.
 
Literatur
1.
Hong, L., Convertino, G., Chi, E.H.: Language matters in Twitter: a large scale study. In: Proceedings of the 5th International AAAI Conference on Weblogs and Social Media (2011)
2.
Takhteyev, Y., Gruzd, A., Wellman, B.: Geography of Twitter networks. Soc. Netw. 34(1), 73–81 (2012)CrossRef
3.
Magdy, A., Ghanem, T.M., Musleh, M., Mokbel, M.F.: Understanding language diversity in local Twitter communities. In: Proceedings of the 27th ACM Conference on Hypertext and Social Media, pp. 331–332 (2016)
4.
Kulshrestha, J., Kooti, F., Nikravesh, A., Gummadi, K.P.: Geographic dissection of the Twitter network. In: Proceedings of the 6th International AAAI Conference on Weblogs and Social Media (2012)
5.
Cunha, E., Magno, G., Comarela, G., Almeida, V., Gonçalves, M., Benevenuto, F.: Analyzing the dynamic evolution of hashtags on Twitter: a language-based approach. In: Proceedings of the Workshop on Languages in Social Media, pp. 58–65 (2011)
6.
Bruns, A., Highfield, T., Burgess, J.: The arab spring and social media audiences: English and Arabic Twitter users and their networks. Am. Behav. Sci. 57(7), 871–898 (2013)CrossRef
7.
Zappavigna, M., Martin, J.R.: Discourse of Twitter and Social Media: How We Use Language to Create Affiliation on the Web. Continnuum, New York (2012)
8.
Zhuravleva, A., de Bot, K., Haug Hilton, N.: Using social media to measure language use. J. Multiling. Multicult. Dev. 37(6), 601–614 (2015)CrossRef
9.
Gruffydd Jones, E., Uribe-Jongbloed, E. (eds.): Social Media and Minority Languages: Convergence and the Creative Industries. Multilingual Matters Ltd., Bristol (2013)
10.
Gisnburgh, V., Weber, S.: How Many Languages Do We Need? The Economics of Linguistic Diversity. Princeton University Press, Princeton (2011)
11.
Willis, A., Fisher, A., Lvov, I.: Mapping networks of influence: tracking Twitter conversations through time and space. Particip. J. Audience Reception Stud. 12(1), 494–530 (2015)
12.
Oatley, G., Crick, T.: Measuring UK crime gangs: a social network problem. Soc. Netw. Anal. Mining 5(1), 1–16 (2015)CrossRef
13.
Borgatti, S.P., Everett, M.G.: Models of core/periphery structures. Soc. Netw. 21(4), 375–395 (2000)CrossRef
14.
Rombach, M., Porter, M.A., Fowler, J.H., Mucha, P.J.: Core-Periphery Structure in Networks. SIAM J. Appl. Math. 74(1), 167–190 (2014)MathSciNetCrossRef
15.
16.
Cha, M., Benevenuto, F., Haddadi, H., Gummadi, K.: The world of connections and information flow in Twitter. IEEE Trans. Syst. Man Cybern. 42(4), 991–998 (2012)CrossRef
17.
18.
Zhang, J.X., Chen, D.B., Dong, Q., Zhao, Z.D.: Identifying a set of influential spreaders in complex networks. Sci. Rep. 6(27823) (2016). doi:10.​1038/​srep27823
19.
Kang, R., Brown, S., Kiesler, S.: Why do people seek anonymity on the internet?: informing policy and design. In: Proceedings SIGCHI Conference on Human Factors in Computing Systems, pp. 2657–2666 (2013)
20.
Morstatter, F., Pfeffer, J., Liu, H., Carley, K.M.: Is the sample good enough? Comparing data from Twitter’s streaming API with Twitter’s firehose. In: Proceedings of 7th International AAAI Conference on Web and Social Media, pp. 400–408 (2013)
21.
Tan, L., Ponnam, S., Gillham, P., Edwards, B., Johnson, E.: Analyzing the impact of social media on social movements: a computational study on Twitter and the Occupy Wall Street movement. In: Proceedings of IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (2013)
22.
23.
Liang, Y., Caverlee, J., Cheng, Z., Kamath, K.Y.: How big is the crowd?: event and location based population modeling in social media. In: Proceedings of 24th ACM Conference on Hypertext and Social Media, pp. 99–108 (2013)
24.
Cheng, Z., Caverlee, J., Lee, K.: You are where you tweet: a content-based approach to geo-locating Twitter users. In: Proceedings of 19th ACM Conference on Information and Knowledge Management, pp. 759–768 (2010)
25.
Blamey, B., Crick, T., Oatley, G.: ‘The first day of summer’: parsing temporal expressions with distributed semantics. In: Bramer, M., Petridis, M. (eds.) Research and Development in Intelligent Systems XXX, pp. 389–402. Springer, Cham (2013). doi:10.​1007/​978-3-319-02621-3_​29CrossRef
26.
Caverlee, J., Cheng, Z., Sui, D.Z., Yeswanth Kamath, K.: Towards geo-social intelligence: mining, analyzing, and leveraging geospatial footprints in social media. IEEE Data Eng. Bull. 36(3), 33–41 (2013)
27.
28.
Ginsburgh, V., Noury, A.G.: The eurovision song contest. Is voting political or cultural? Eur. J. Polit. Econ. 24(1), 41–52 (2008)CrossRef
29.
Charron, N.: Impartiality, friendship-networks and voting behavior: evidence from voting patterns in the Eurovision Song Contest. Soc. Netw. 35(3), 484–497 (2013)CrossRef
30.
Blangiardo, M., Baio, G.: Evidence of bias in the Eurovision song contest: modelling the votes using Bayesian hierarchical models. J. Appl. Stat. 41(10), 2312–2322 (2014)MathSciNetCrossRef
31.
32.
33.
34.
Sluban, B., Smailović, J., Battiston, S.: Mozetic̆ I.: Sentiment leaning of influential communities in social networks. Comput. Soc. Netw. 2(9), 1–21 (2015)
35.
Mostafa, M., Crick, T., Calderon, A.C., Oatley, G.: Incorporating emotion and personality-based analysis in user-centered modelling. In: Bramer, M., Petridis, M. (eds.) Research and Development in Intelligent Systems XXXIII. LNCS, pp. 383–389. Springer, Cham (2016). doi:10.​1007/​978-3-319-47175-4_​29CrossRef
Metadaten
Titel
“Come Together!”: Interactions of Language Networks and Multilingual Communities on Twitter
verfasst von
Nabeel Albishry
Tom Crick
Theo Tryfonas
Copyright-Jahr
2017
Buch
Computational Collective Intelligence

Print ISBN: 978-3-319-67076-8

Electronic ISBN: 978-3-319-67077-5

Copyright-Jahr: 2017

DOI
https://doi.org/10.1007/978-3-319-67077-5_45