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2019 | OriginalPaper | Chapter

Unfolding the Mixed and Intertwined: A Multilevel View of Topic Evolution on Twitter

Authors : Yunwei Zhao, Can Wang, Han Han, Willem-Jan van den Heuvel, Chi-Hung Chi, Weimin Li

Published in: Advanced Data Mining and Applications

Publisher: Springer International Publishing

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Abstract

Despite the extensive research efforts in information diffusion, most previous studies focus on the speed and coverage of the diffused information in the network. A better understanding on the semantics of information diffusion can provide critical information for the domain-specific/socio-economic phenomenon studies based on diffused topics. More specifically, it still lacks (a) a comprehensive understanding of the multiplexity in the diffused topics, especially with respect to the temporal relations and inter-dependence between topic semantics; (b) the similarities and differences in these dimensions under different diffusion degrees. In this paper, the semantics of a topic is described by sentiment, controversy, content richness, hotness, and trend momentum. The multiplexity in the diffusion mechanisms is also considered, namely, hashtag cascade, url cascade, and retweet. Our study is conducted upon 840, 362 topics from about 42 million tweets during 2010.01–2010.10. The results show that the topics are not randomly distributed in the Twitter space, but exhibiting a unique pattern at each diffusion degree, with a significant correlation among content richness, hotness, and trend momentum. Moreover, under each diffusion mechanism, we also find the remarkable similarity among topics, especially when considering the shifting and scaling in both the temporal and amplitude scales of these dimensions.

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Footnotes
1
The magnitude is calculated over non-vacant values.
 
2
See webpage http://​cool-smileys.​com/​text-emoticons, containing 938 text emoticons.
 
3
See webpage http://​www.​noslang.​com/​, containing 5396 slangs and abbreviations.
 
4
There are some exceptions in url-based topics and retweet-based topics (the abnormally low correlation in the most diffused level): (a) the content richness is not positively correlated with hotness and trend momentum for the url- and retweet-based topics that are content self-replicating; (b) the content richness is positively correlated with hotness and trend momentum for the url- and retweet-based topics that have various subjects ongoing. For example, the 12th most diffused url-based topic http://​faxo.​com/​t include “Harry Potter vs. Twilight”, “Top YouTube Musician”, “Musician of the Month”, etc.
 
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Metadata
Title
Unfolding the Mixed and Intertwined: A Multilevel View of Topic Evolution on Twitter
Authors
Yunwei Zhao
Can Wang
Han Han
Willem-Jan van den Heuvel
Chi-Hung Chi
Weimin Li
Copyright Year
2019
DOI
https://doi.org/10.1007/978-3-030-35231-8_26

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