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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 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.
References
Diakopoulos, N.A., Shamma, D.A.: Characterizing debate performance via aggregated Twitter sentiment. In: CHI 2010, Atlanta, Georgia, USA, pp. 1195–1198 (2010)
Budak, C., Agrawal, D., Abbadi, A.E.: Structural trend analysis for online social networks. Proc. VLDB Endowment 4(10), 646–656 (2011)
Sitaram, A., Bernardo, A.H., et al.: Trends in social media: persistence and decay. In: ICWSM 2011, Barcelona, Spain, pp. 434–437 (2011)
Wang, C., Bo, T., Zhao, Y., et al.: Behavior-interior-aware user preference analysis based on social networks. Complexity 2018, Article ID 7371209 (2018). https://www.hindawi.com/journals/complexity/2018/7371209/cta/
Sprenger, T.O., Tumasjan, A., et al.: Tweets and trades: the information content of stock microblogs. Eur. Fin. Manag. 20(5), 926–957 (2013)
Yang, J., Leskovec, J.: Patterns of temporal variation in online media. In: WSDM 2011, Hong Kong, China, pp. 177–186 (2011)
Cremonesi, P., Koren, Y., Turrin, R.: Performance of recommender algorithms on top-n recommendation tasks. In: RecSys 2010, Barcelona, Spain, pp. 39–46 (2010)
Boyd, D., Golder, S., Lotan, G.: Tweet, Tweet, Retweet: Conversational aspects of retweeting on Twitter. In: HICSS 2010, Honolulu, HI, pp. 1–10 (2010)
Guerini, M., Strapparava, C., Ozbal, G.: Exploring text virality in social networks. IN: ICWSM 2011, Barcelona, Spain, pp. 506–509 (2011)
Chew, C., Eysenbach, G.: Pandemics in the age of Twitter: content analysis of tweets during the 2009 H1N1 outbreak. PLoS ONE 5(11), e14118 (2010)
Yang, K., Shahabi, C.: A PCA-based similarity measure for multivariate time series. In: MMDB 2004, Washington D.C, US, pp. 65–74 (2004)
Morchen, F.: Time series feature extraction for data mining using DWT and DFT. Department of Mathematics and Computer Science, University of Marburg (2003)
Galeano, P., Pena, D.: Multivariate analysis in vector time series. Resenhas 4(4), 383–403 (2000)
Chen, Y., Chen, K., Nascimento, M.A.: Effective and efficient shape-based pattern detection over streaming time series. TKDE 24(2), 265–278 (2012)
Rousseeuw, P.J.: Silhouettes: A graphical aid to the interpretation and validation of cluster analysis. J. Comput. Appl. Math. 20, 53–65 (1987)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Zhao, Y., Wang, C., Han, H., van den Heuvel, WJ., Chi, CH., Li, W. (2019). Unfolding the Mixed and Intertwined: A Multilevel View of Topic Evolution on Twitter. In: Li, J., Wang, S., Qin, S., Li, X., Wang, S. (eds) Advanced Data Mining and Applications. ADMA 2019. Lecture Notes in Computer Science(), vol 11888. Springer, Cham. https://doi.org/10.1007/978-3-030-35231-8_26
Download citation
DOI: https://doi.org/10.1007/978-3-030-35231-8_26
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-35230-1
Online ISBN: 978-3-030-35231-8
eBook Packages: Computer ScienceComputer Science (R0)