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Predicting the scale of trending topic diffusion among online communities


Kim, D and Han, SC and Lee, S and Kang, BH, Predicting the scale of trending topic diffusion among online communities, Lecture Notes in Computer Science 8867: Proceedings of the 14th Pacific Rim Knowledge Acquisition Workshop - Knowledge Management & Acquisition for Intelligent Systems), 22-23 August 2016, Phuket, Thailand, pp. 153-165. ISSN 0302-9743 (2016) [Refereed Conference Paper]


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DOI: doi:10.1007/978-3-319-42706-5_12


Online trending topics represent the most popular topics among users in certain online community, such as a country community. Trending topics in one community are different from others since the users in the community may discuss different topics from other communities. Surprisingly, almost 90% of trending topics are diffused among multiple online communities, so it shows peoples interests in a certain community can be shared to others in another community. The aim of this research is to predict the scale of trending topic diffusion among different online communities. The scale of diffusion represents the number of online communities that a trending topic diffuses. We proposed a diffusion scale prediction model for trending topics with the following four features, including community innovation feature, context feature, topic feature, and rank feature. We examined the proposed model with four different machine learning in predicting the scale of diffusion in Twitter Trending Topics among 8 English-speaking countries. Our model achieved the highest prediction accuracy (80.80%) with C4.5 decision tree.

Item Details

Item Type:Refereed Conference Paper
Keywords:Twitter, Twitter Trending Topics, information diffusion, trending topic diffusion, diffusion prediction
Research Division:Information and Computing Sciences
Research Group:Artificial intelligence
Research Field:Artificial intelligence not elsewhere classified
Objective Division:Information and Communication Services
Objective Group:Information systems, technologies and services
Objective Field:Information systems, technologies and services not elsewhere classified
UTAS Author:Han, SC (Ms Caren Han)
UTAS Author:Kang, BH (Professor Byeong Kang)
ID Code:113457
Year Published:2016
Deposited By:Engineering
Deposited On:2017-01-03
Last Modified:2018-02-28
Downloads:205 View Download Statistics

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