Abstract
People use microblogging platforms like Twitter to involve with other users for a wide range of interests and practices. Twitter profiles run by different types of users such as humans, bots, spammers, businesses and professionals. This research uses a treemap visualization to identify different users profile on Twitter. For this purpose, we exploit users’ profile and tweeting behavior information. We evaluate our approach by visualizing the different Twitter profiles. We focus just on user activity, ignoring the content of messages. We take into consideration both social interactions and tweeting patterns, which allow us to profile users according to their activity patterns using treemaps.
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References
Fischer, E., Reuber, A.: Social interaction via new social media:(how) can interactions on Twitter affect effectual thinking and behavior? J. Bus. Ventur. 26(1), 1–18 (2011)
Benevenuto, F, Magno, G, Rodrigues, T, Almeida, V.: Detecting spammers on Twitter. In: Collaboration, Electronic Messaging, Anti-abuse and Spam Conference (CEAS), vol. 6 (2010)
Hannon, J, Bennett, M, Smith, B.: Recommending twitter users to follow using content and collaborative filtering approaches. In: Proceedings of the Fourth ACM Conference on Recommender Systems, pp. 199–206. ACM (2010)
Castillo, C, Mendoza, M, Poblete, B.: Information credibility on Twitter. In: Proceedings of the 20th International Conference on World Wide Web, pp. 675–684. ACM (2011)
Paul, S, Hong, L, Chi, E.: Is Twitter a good place for asking questions? A characterization study. In: ICWSM (2011)
Pennacchiotti, M, Popescu, A.: A machine learning approach to Twitter user classification. In: ICWSM (2011)
Millen, D, Patterson, J.: Stimulating social engagement in a community network. In: Proceedings of the ACM Conference on Computer-Supported Cooperative Work, pp. 306–313 (2002)
Naveed, N, Gottron, T, Kunegis, J, Alhadi, A.: Bad news travel fast: a content-based analysis of interestingness on Twitter. In: Proceedings of the ACM WebSci 2011, pp. 1–7 (2011)
Yang, J., Counts, S.: Predicting the speed, scale, and range of information diffusion in Twitter. In: International AAAI Conference on Weblogs and Social Media (2011)
Chu, Z, Gianvecchio, S, Wang, H, Jajodia, S.: Who is tweeting on Twitter: human, bot, or cyborg? In: Proceedings of the 26th Annual Computer Security Applications Conference, pp. 21–30 (2010)
Java, A, Finin, T, Song, X, Tseng, B.: Why we Twitter: understanding microblogging usage and communities. In: Joint 9th WEBKDD and 1st SNA-KDD Workshop (2007)
Pennacchiotti, M, Popescu, A.: A machine learning approach to Twitter user classification. In: Fifth International AAAI Conference on Weblogs and Social Media (2011)
Cha, M, Haddadi, H, Benevenuto, F, Gummadi, K.: Measuring user influence in Twitter: the million follower fallacy. In: Proceedings of the 4th International AAAI Conference on Weblogs and Social Media ICWSM (2010)
Gomez-Rodriguez, M, Leskovec, J, Krause, A.: Inferring networks of diffusion and influence. In: DD, pp. 1019–1028 (2010)
Shneiderman, B.: Tree visualization with treemaps: a 2D space-filling approach. Technical report, HCI Lab University of Maryland, March 1991
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López-Ornelas, E., Abascal-Mena, R. (2022). Understanding User Behavior in Social Media Using a Hierarchical Visualization. In: Stephanidis, C., Antona, M., Ntoa, S. (eds) HCI International 2022 Posters. HCII 2022. Communications in Computer and Information Science, vol 1582. Springer, Cham. https://doi.org/10.1007/978-3-031-06391-6_71
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DOI: https://doi.org/10.1007/978-3-031-06391-6_71
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