Abstract
Language usage behavior of users evolves over time, as they interact on social media such as Twitter. We study the evolution of language usage behavior of individuals, across topics, on microblogs. We propose Man-O-Meter, a framework to model such evolution. We model the evolution using a combination of three dimensions: (a) time, (b) content (topics) and (c) influence flow over social relationships. We assert the goodness of our approach, by predicting ranks of experts, with respect to their influence in their respective expertise category, using the change in language used in time. We apply our framework on 2, 273 influential microbloggers on Twitter, across 62 categories, spanning over 10 domains. Our work is applicable in predicting activity and influence, interest evolution, job change and community change expected to happen to a user, in future.
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Angeletou, S., Rowe, M., Alani, H.: Modelling and analysis of user behaviour in online communities. In: Aroyo, L., Welty, C., Alani, H., Taylor, J., Bernstein, A., Kagal, L., Noy, N., Blomqvist, E. (eds.) ISWC 2011, Part I. LNCS, vol. 7031, pp. 35–50. Springer, Heidelberg (2011)
Backstrom, L., Huttenlocher, D., Kleinberg, J., Lan, X.: Group formation in large social networks: membership, growth and evolution. In: KDD (2006)
Bakshy, E., Hoffman, J.M., Mason, W.A., Watts, D.J.: Everyone’s an influencer: quantifying influence on twitter. In: WSDM (2011)
Bell, H.: Language style as audience design. In: Language in society (1984)
Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent Dirichlet allocation. J. Mach. Learn. Res. 3, 993–1022 (2003)
Cha, M., Haddadi, H., Benevenuto, F., Gummadi, P.K.: Measuring user influence in twitter: the million follower fallacy. In: ICWSM (2010)
Clarke, H.H., Wilkies-Gibbs, D.: Referring as a collaborative process. In: Cognition (1986)
Coupland, J., Coupland, N., Giles, H.: Accommodation theory: communication, context and consequences. In: Contexts of Accommodation, Cambridge, pp. 1–68 (1991)
Cunha, E., Magno, G., Comarela, G., Almeida, V., Gonalves, M.A., Benevenuto, F.: Analyzing the dynamic evolution of hashtags on twitter: a language-based approach. In: Workshop on Language in Social Media - ACL, pp. 58–65 (2011)
Danescu-Niculescu-Mizil, C., West, R., Jurafsky, D., Leskovec, J., Potts, C.: No country for old members: user lifecycle and linguistic change in online communities. In: WWW (2013)
Ducheneaut, N., Yee, N., Nickell, E., Moore, R.: The life and death of online gaming communities: a look at guilds in world of warcraft. In: CHI (2007)
Garley, M., Hockenmaier, J.: Dissemination, diversity and dynamics of English borrowings in german hip hop forum. In: ACL (2012)
Ghosh, S., Sharma, N.K., Benevenuto, F., Ganguly, N., Gummadi, K.P.: Whom to follow? discover topic authorities on twitter! (2012). http://twitter-app.mpi-sws.org/whom-to-follow/
Joachims, T.: Training linear SVMs in linear time. In: KDD (2006)
Kairam, S., Wang, D., Leskovec, J.: The life and death of online groups: predicting group growth and longevity. In: WSDM (2012)
Kooti, F., Yang, M.C.H., Gummadi, K.P., Mason, W.A.: The emergence of conventions in online social networks. In: ICWSM (2012)
Kooti, F., Mason, W.A., Gummadi, K.P., Cha, M.: Predicting emerging social conventions in online social networks. In: CIKM (2012)
Kumar, R., Novak, J., Tomkins, A.: Structure and evolution of online social networks. In: KDD (2006)
Kwak, H., Lee, C., Park, H., Moon, S.B.: What is twitter, a social network or a news media? In: WWW (2010)
L. L, J. Tang, J. Han, M. Jiang, S. Yang.: Mining topic-level influence in heterogeneous networks. In: CIKM (2010)
Leskovec, J., Kleinberg, J., Faloustos, C.: Graph evolution: densification and shrinking diameters. In: TKDD (2007)
McCallum, A.K.: Mallet: a machine learning for language toolkit (2002). http://mallet.cs.umass.edu
Mizil, C.D.-N., Lee, L., Pang, B., Kleinberg, J.: Echoes of power: language effects and power differences in social interaction. In: WWW (2012)
Morrison, D., McLoughin, I., Hogan, A., Hayes, C.: Evolutionary clustering and analysis of user behaviour in online forums. In: ICWSM (2012)
Pak, A., Paroubek, P.: Twitter as a corpus for sentiment analysis and opinion mining. In: LREC (2010)
Postmes, T., Spears, R., Lea, M.: The formation of group norms in computer-mediated communication. In: Human Communications Research (2000)
Romero, D.M., Galuba, W., Asur, S., Huberman, B.A.: Influence and passivity in social media. In: Gunopulos, D., Hofmann, T., Malerba, D., Vazirgiannis, M. (eds.) ECML PKDD 2011, Part III. LNCS, vol. 6913, pp. 18–33. Springer, Heidelberg (2011)
Romero, D.M., Meeder, B., Kleinberg, J.M.: Differences in the mechanics of information diffusion across topics: idioms, political hashtags, and complex contagion on twitter. In: WWW, pp. 695–704 (2011)
Rubinstein, R.Y.: The cross-entropy method for combinatorial and continuous optimization. Methodol. Comput. Appl. Probab. 1(2), 127–190 (1993)
Tsur, O., Rappoport, A.: What’s in a hashtag?: content based prediction of the spread of ideas in microblogging communities. In: WSDM, pp. 643–652 (2012)
Weng, J., Lim, E., He, Q.: Finding topic-sensitive influential twitterers. In: WSDM (2011)
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Dey, K., Kaushik, S., Lamba, H., Nagar, S. (2016). Man-O-Meter: Modeling and Assessing the Evolution of Language Usage of Individuals on Microblogs. In: Li, F., Shim, K., Zheng, K., Liu, G. (eds) Web Technologies and Applications. APWeb 2016. Lecture Notes in Computer Science(), vol 9931. Springer, Cham. https://doi.org/10.1007/978-3-319-45814-4_28
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