Abstract
The flourish of Web-based Online Social Networks (OSNs) has led to numerous applications that exploit social relationships to boost the influence of content in the network. However, existing approaches focus on the social ties and ignore how the topic of a post and its structure relate to its popularity. Our work assists in filling this gap. The contribution of this work is two-fold: (i) we develop a scheme that automatically identifies the topic of a post, specifically tweets, in real-time without human participation in the process, and then (ii) based on the topic of the tweet and prior related posts, we recommend appropriate structural properties to increase the popularity of the particular tweet. By exploiting Wikipedia, our model requires no training or expensive feature engineering for the classification of tweets to topics.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsNotes
- 1.
- 2.
- 3.
- 4.
Best People On Twitter: http://goo.gl/AOU0GU.
- 5.
References
Aslay, Ç., Barbieri, N., Bonchi, F., Baeza-Yates, R.A.: Online topic-aware influence maximization queries. In: EDBT 2014, Athens, Greece, pp. 295–306, March 2014
Banerjee, S., Ramanathan, K., Gupta, A.: Clustering short texts using wikipedia. In: SIGIR 2007, pp. 787–788. ACM (2007)
Baralis, E., Cerquitelli, T., Chiusano, S., Grimaudo, L., Xiao, X.: Analysis of twitter data using a multiple-level clustering strategy. In: Cuzzocrea, A., Maabout, S. (eds.) MEDI 2013. LNCS, vol. 8216, pp. 13–24. Springer, Heidelberg (2013). doi:10.1007/978-3-642-41366-7_2
Biuk-Aghai, R., Ng, K.K.: A method for automated document classification using wikipedia-derived weighted keywords. In: 2014 International Conference on ICODSE, pp. 1–6, November 2014
Chen, Q., Shipper, T., Khan, L.: Tweets mining using wikipedia and impurity cluster measurement. In: 2010 IEEE ISI, pp. 141–143 (2010)
Chen, S., Fan, J., Li, G., Feng, J., Tan, K.-L., Tang, J.: Online topic-aware influence maximization. Proc. VLDB Endow. 8(6), 666–677 (2015)
Genc, Y., Sakamoto, Y., Nickerson, J.V.: Discovering context: classifying tweets through a semantic transform based on wikipedia. In: Schmorrow, D.D., Fidopiastis, C.M. (eds.) FAC 2011. LNCS (LNAI), vol. 6780, pp. 484–492. Springer, Heidelberg (2011). doi:10.1007/978-3-642-21852-1_55
Hu, B., Song, Z., Ester, M.: User features and social networks for topic modeling in online social media. In: Proceedings of the 2012 ASONAM, ASONAM 2012, pp. 202–209 (2012)
Kiciman, E.: OMG, i have to tweet that! a study of factors that influence tweet rates. In: Breslin, J.G., Ellison, N.B., Shanahan, J.G., Tufekci, Z. (eds.) ICWSM. The AAAI Press (2012)
Machedon, R., Rand, W., Joshi, Y.: Automatic crowdsourcing-based classification of marketing messaging on twitter. In: SocialCom 2013, pp. 975–978 (2013)
Michelson, M., Macskassy, S.A.: Discovering users’ topics of interest on twitter: a first look. In: Proceedings of the Fourth Workshop on Analytics for Noisy Unstructured Text Data. ACM (2010)
Munger, T., Zhao, J.: Identifying influential users in on-line support forums using topical expertise and social network analysis. In: Proceedings of the 2015 IEEE/ACM ASONAM, pp. 721–728 (2015)
Niu, W., Liu, Z., Caverlee, J.: LExL: a learning approach for local expert discovery on twitter. In: Ferro, N., Crestani, F., Moens, M.-F., Mothe, J., Silvestri, F., Nunzio, G.M., Hauff, C., Silvello, G. (eds.) ECIR 2016. LNCS, vol. 9626, pp. 803–809. Springer, Heidelberg (2016). doi:10.1007/978-3-319-30671-1_71
Ozsoy, M.G., Polat, F., Alhajj, R.: Modeling individuals and making recommendations using multiple social networks. In: Proceedings of the 2015 IEEE/ACM ASONAM, pp. 1184–1191 (2015)
Quercia, D., Askham, H., Crowcroft, J.: Tweetlda: supervised topic classification and link prediction in twitter. In: Proceedings of the 4th Annual ACM WebSci, WebSci 2012, pp. 247–250. ACM, New York (2012)
Shafiq, M.O., Alhajj, R., Rokne, J.G.: On personalizing web search using social network analysis. Inf. Sci. 314, 55–76 (2015)
Suh, B., Hong, L., Pirolli, P., Chi, E.H.: Want to be retweeted? large scale analytics on factors impacting retweet in twitter network. In: SOCIALCOM 2010, pp. 177–184. IEEE Computer Society (2010)
Valkanas, G., Gunopulos, D.: Location extraction from social networks with commodity software and online data. In ICDMW (2012)
Wang, B., Wang, C., Bu, J., Chen, C., Zhang, W., Cai, D., He, X.: Whom to mention: expand the diffusion of tweets by @ recommendation on micro-blogging systems. In: WWW 2013, pp. 1331–1340 (2013)
Yang, J., Counts, S.: Predicting the speed, scale, and range of information diffusion in Twitter. In: 4th International AAAI ICWSM, May 2010
Yang, T., Lee, D., Yan, S.: Steeler nation, 12th man, and boo birds: classifying twitter user interests using time series. In: Proceedings of the 2013 IEEE/ACM ASONAM, pp. 684–691 (2013)
Zaman, T., Fox, E.B., Bradlow, E.T.: A bayesian approach for predicting the popularity of tweets. CoRR (2013)
Acknowledgments
This research has been financed by the European Union through the FP7 ERC IDEAS 308019 NGHCS project and the Horizon2020 688380 VaVeL project.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing AG
About this paper
Cite this paper
Litou, I., Kalogeraki, V., Gunopulos, D. (2016). On Topic Aware Recommendation to Increase Popularity in Microblogging Services (Short Paper). In: Debruyne, C., et al. On the Move to Meaningful Internet Systems: OTM 2016 Conferences. OTM 2016. Lecture Notes in Computer Science(), vol 10033. Springer, Cham. https://doi.org/10.1007/978-3-319-48472-3_40
Download citation
DOI: https://doi.org/10.1007/978-3-319-48472-3_40
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-48471-6
Online ISBN: 978-3-319-48472-3
eBook Packages: Computer ScienceComputer Science (R0)