Predicting the spread of a new tweet in twitter

M. Anwar, Jianxin Li, C. Liu

Research output: Chapter in Book/Conference paperConference paperpeer-review

5 Citations (Scopus)


© Springer International Publishing Switzerland 2015.Online social network services serve as vehicles for users to share user-generated contents (e.g. blogs, tweets, videos etc.) with any number of peers. Predicting the spread of such contents is important for obtaining latest information on different topics, viral marketing etc. Existing approaches on spread prediction are mainly focused on content and past behavior of users. However, not enough attention is paid to the structural characteristics of the network. In this paper, we propose topic based approach to predict the spread of a new tweet from a particular user in online social network namely in Twitter based on latent content interests of users and the structural characteristics of the underlying social network. We apply Latent Dirichlet Allocation (LDA) model on users’ past tweets of learn the latent topic distribution of the users. Using word-topic distribution from LDA, we next identify top-k topics relevant to the new tweet. Finally, we measure the spread prediction of the new tweet considering its acceptance in the underlying social network by taking into account the possible effect of all the propagation paths between tweet owner and the recipient user. Our experimental results on real dataset show the efficacy of the proposed approach.
Original languageEnglish
Title of host publicationDatabases theory and applications
Subtitle of host publicationADC 2015
EditorsM Sharaf, M Cheema, J Qi
ISBN (Print)9783319195476
Publication statusPublished - 2015
Externally publishedYes
EventAustralasian Database Conference - Melbourne, Australia
Duration: 1 Jan 20117 Jun 2016

Publication series

NameLecture Notes in Computer Science


ConferenceAustralasian Database Conference
Abbreviated titleADC


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