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Predicting the scale of trending topic diffusion among online communities
Citation
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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Official URL: http://pkaw.org/pkaw2016/
DOI: doi:10.1007/978-3-319-42706-5_12
Abstract
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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