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2016 | OriginalPaper | Buchkapitel

Detecting Community Pacemakers of Burst Topic in Twitter

verfasst von : Guozhong Dong, Wu Yang, Feida Zhu, Wei Wang

Erschienen in: Web Technologies and Applications

Verlag: Springer International Publishing

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Abstract

Twitter has become one of largest social networks for users to broadcast burst topics. Influential users usually have a large number of followers and play an important role in the diffusion of burst topic. There have been many studies on how to detect influential users. However, traditional influential users detection approaches have largely ignored influential users in user community. In this paper, we investigate the problem of detecting community pacemakers. Community pacemakers are defined as the influential users that promote early diffusion in the user community of burst topic. To solve this problem, we present DCPBT, a framework that can detect community pacemakers in burst topics. In DCPBT, a burst topic user graph model is proposed, which can represent the topology structure of burst topic propagation across a large number of Twitter users. Based on the model, a user community detection algorithm based on random walk is applied to discover user community. For large-scale user community, we propose a ranking method to detect community pacemakers in each large-scale user community. To test our framework, we conduct the framework over Twitter burst topic detection system. Experimental results show that our method is more effective to detect the users that influence other users and promote early diffusion in the early stages of burst topic.

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Literatur
1.
Zurück zum Zitat Kasiviswanathan, S.P., Melville, P., Banerjee, A., Sindhwani, V.: Emerging topic detection using dictionary learning. In: Proceedings of the 20th ACM International Conference on Information and Knowledge Management, pp. 745–754. ACM (2011) Kasiviswanathan, S.P., Melville, P., Banerjee, A., Sindhwani, V.: Emerging topic detection using dictionary learning. In: Proceedings of the 20th ACM International Conference on Information and Knowledge Management, pp. 745–754. ACM (2011)
2.
Zurück zum Zitat Agarwal, M.K., Ramamritham, K., Bhide, M.: Real time discovery of dense clusters in highly dynamic graphs: identifying real world events in highly dynamic environments. Proc. VLDB Endow. 5(10), 980–991 (2012)CrossRef Agarwal, M.K., Ramamritham, K., Bhide, M.: Real time discovery of dense clusters in highly dynamic graphs: identifying real world events in highly dynamic environments. Proc. VLDB Endow. 5(10), 980–991 (2012)CrossRef
3.
Zurück zum Zitat Alvanaki, F., Sebastian, M., Ramamritham, K., Weikum, G.: EnBlogue: emergent topic detection in Web 2.0 streams. In: Proceedings of the 2011 ACM SIGMOD International Conference on Management of data, pp. 1271–1274. ACM (2011) Alvanaki, F., Sebastian, M., Ramamritham, K., Weikum, G.: EnBlogue: emergent topic detection in Web 2.0 streams. In: Proceedings of the 2011 ACM SIGMOD International Conference on Management of data, pp. 1271–1274. ACM (2011)
4.
Zurück zum Zitat Takahashi, T., Tomioka, R., Yamanishi, K.: Discovering emerging topics in social streams via link anomaly detection. In: IEEE 11th International Conference on Data Mining (ICDM), pp. 1230–1235. IEEE (2011) Takahashi, T., Tomioka, R., Yamanishi, K.: Discovering emerging topics in social streams via link anomaly detection. In: IEEE 11th International Conference on Data Mining (ICDM), pp. 1230–1235. IEEE (2011)
5.
Zurück zum Zitat Wang, Y., Liu, H., Lin, H., Wu, J., Wu, Z., Cao, J.: SEA: a system for event analysis on Chinese tweets. In: Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1498–1501. ACM (2013) Wang, Y., Liu, H., Lin, H., Wu, J., Wu, Z., Cao, J.: SEA: a system for event analysis on Chinese tweets. In: Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1498–1501. ACM (2013)
6.
Zurück zum Zitat Xie, W., Zhu, F., Jiang, J., Lim, E.P., Wang, K.: Topicsketch: real-time bursty topic detection from Twitter. In: IEEE 13th International Conference on Data Mining (ICDM), pp. 837–846. IEEE (2013) Xie, W., Zhu, F., Jiang, J., Lim, E.P., Wang, K.: Topicsketch: real-time bursty topic detection from Twitter. In: IEEE 13th International Conference on Data Mining (ICDM), pp. 837–846. IEEE (2013)
7.
Zurück zum Zitat Xie, R., Zhu, F., Ma, H., Xie, W., Lin, C.: CLEar: a real-time online observatory for bursty and viral events. Proc. VLDB Endow. 7(13), 1637–1640 (2014)CrossRef Xie, R., Zhu, F., Ma, H., Xie, W., Lin, C.: CLEar: a real-time online observatory for bursty and viral events. Proc. VLDB Endow. 7(13), 1637–1640 (2014)CrossRef
8.
Zurück zum Zitat Shen, G., Yang, W., Wang, W.: Burst topic detection oriented large-scale microblogs streams. J. Comput. Res. Dev. 52(2), 512–521 (2015). (in Chinese) Shen, G., Yang, W., Wang, W.: Burst topic detection oriented large-scale microblogs streams. J. Comput. Res. Dev. 52(2), 512–521 (2015). (in Chinese)
9.
Zurück zum Zitat Cha, M., Haddadi, H., Benevenuto, F., Gummadi, K.P.: Measuring user influence in Twitter: the million follower fallacy. In: Fourth International AAAI Conference on Weblogs and Social Media (ICWSM 2010), pp. 10–17. AAAI Press (2010) Cha, M., Haddadi, H., Benevenuto, F., Gummadi, K.P.: Measuring user influence in Twitter: the million follower fallacy. In: Fourth International AAAI Conference on Weblogs and Social Media (ICWSM 2010), pp. 10–17. AAAI Press (2010)
10.
Zurück zum Zitat Lee, C., Kwak, H., Park, H., Moon, S.: Finding influentials based on the temporal order of information adoption in Twitter. In: Proceedings of the 19th International Conference on World Wide Web, pp. 1137–1138. ACM (2010) Lee, C., Kwak, H., Park, H., Moon, S.: Finding influentials based on the temporal order of information adoption in Twitter. In: Proceedings of the 19th International Conference on World Wide Web, pp. 1137–1138. ACM (2010)
11.
Zurück zum Zitat Weng, J., Lim, E.P., Jiang, J., He, Q.: Twitterrank: finding topic-sensitive influential twitterers. In: Proceedings of the Third ACM International Conference on Web Search and Data Mining, pp. 261–270. ACM (2010) Weng, J., Lim, E.P., Jiang, J., He, Q.: Twitterrank: finding topic-sensitive influential twitterers. In: Proceedings of the Third ACM International Conference on Web Search and Data Mining, pp. 261–270. ACM (2010)
12.
Zurück zum Zitat Brown, P.E., Feng, J.: Measuring user influence on Twitter using modified K-shell decomposition. In: Fifth International AAAI Conference on Weblogs and Social Media, pp. 18–23. AAAI Press (2011) Brown, P.E., Feng, J.: Measuring user influence on Twitter using modified K-shell decomposition. In: Fifth International AAAI Conference on Weblogs and Social Media, pp. 18–23. AAAI Press (2011)
13.
Zurück zum Zitat Fang, Q., Sang, J., Xu, C., Rui, Y.: Topic-sensitive influencer mining in interest-based social media networks via hypergraph learning. IEEE Trans. Multimedia 16(3), 796–812 (2014)CrossRef Fang, Q., Sang, J., Xu, C., Rui, Y.: Topic-sensitive influencer mining in interest-based social media networks via hypergraph learning. IEEE Trans. Multimedia 16(3), 796–812 (2014)CrossRef
14.
Zurück zum Zitat Saez-Trumper, D., Comarela, G., Almeida, V., Baeza-Yates, R., Benevenuto, F.: Finding trendsetters in information networks. In: Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1014–1022. ACM (2012) Saez-Trumper, D., Comarela, G., Almeida, V., Baeza-Yates, R., Benevenuto, F.: Finding trendsetters in information networks. In: Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1014–1022. ACM (2012)
15.
Zurück zum Zitat Wu, Y., Hu, Y., He, X., Deng, K.: Impact of user influence on information multi-step communication in a microblog. Chin. Phys. B 23(6), 5–12 (2014) Wu, Y., Hu, Y., He, X., Deng, K.: Impact of user influence on information multi-step communication in a microblog. Chin. Phys. B 23(6), 5–12 (2014)
16.
Zurück zum Zitat 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) 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)
17.
Zurück zum Zitat Liu, D., Wu, Q., Han, W.: Measuring micro-blogging user influence based on user-tweet interaction model. In: Tan, Y., Shi, Y., Mo, H. (eds.) ICSI 2013, Part II. LNCS, vol. 7929, pp. 146–153. Springer, Heidelberg (2013) Liu, D., Wu, Q., Han, W.: Measuring micro-blogging user influence based on user-tweet interaction model. In: Tan, Y., Shi, Y., Mo, H. (eds.) ICSI 2013, Part II. LNCS, vol. 7929, pp. 146–153. Springer, Heidelberg (2013)
18.
Zurück zum Zitat Rosvall, M., Bergstrom, C.T.: Maps of random walks on complex networks reveal community structure. Proc. Natl. Acad. Sci. 105(4), 1118–1123 (2008)CrossRef Rosvall, M., Bergstrom, C.T.: Maps of random walks on complex networks reveal community structure. Proc. Natl. Acad. Sci. 105(4), 1118–1123 (2008)CrossRef
19.
Zurück zum Zitat Lambiotte, R., Rosvall, M.: Ranking and clustering of nodes in networks with smart teleportation. Phys. Rev. E 85(5), 056107(1–9) (2012) Lambiotte, R., Rosvall, M.: Ranking and clustering of nodes in networks with smart teleportation. Phys. Rev. E 85(5), 056107(1–9) (2012)
Metadaten
Titel
Detecting Community Pacemakers of Burst Topic in Twitter
verfasst von
Guozhong Dong
Wu Yang
Feida Zhu
Wei Wang
Copyright-Jahr
2016
DOI
https://doi.org/10.1007/978-3-319-45814-4_20