Next Article in Journal
A Dual Attack Detection Technique to Identify Black and Gray Hole Attacks Using an Intrusion Detection System and a Connected Dominating Set in MANETs
Previous Article in Journal
myDIG: Personalized Illicit Domain-Specific Knowledge Discovery with No Programming
Article Menu
Issue 3 (March) cover image

Export Article

Open AccessArticle

Hot Topic Community Discovery on Cross Social Networks

School of Computer Engineering and Science, Shanghai University, Shanghai 200444, China
School of Computer Science and Technology, Kashgar University, Kashgar 844006, China
Author to whom correspondence should be addressed.
Future Internet 2019, 11(3), 60;
Received: 7 January 2019 / Revised: 14 February 2019 / Accepted: 27 February 2019 / Published: 4 March 2019
(This article belongs to the Section Techno-Social Smart Systems)
PDF [5382 KB, uploaded 4 March 2019]


The rapid development of online social networks has allowed users to obtain information, communicate with each other and express different opinions. Generally, in the same social network, users tend to be influenced by each other and have similar views. However, on another social network, users may have opposite views on the same event. Therefore, research undertaken on a single social network is unable to meet the needs of research on hot topic community discovery. “Cross social network” refers to multiple social networks. The integration of information from multiple social network platforms forms a new unified dataset. In the dataset, information from different platforms for the same event may contain similar or unique topics. This paper proposes a hot topic discovery method on cross social networks. Firstly, text data from different social networks are fused to build a unified model. Then, we obtain latent topic distributions from the unified model using the Labeled Biterm Latent Dirichlet Allocation (LB-LDA) model. Based on the distributions, similar topics are clustered to form several topic communities. Finally, we choose hot topic communities based on their scores. Experiment result on data from three social networks prove that our model is effective and has certain application value. View Full-Text
Keywords: cross social networks; hot topic community; Labeled Biterm Latent Dirichlet Allocation topic model; clustering algorithm cross social networks; hot topic community; Labeled Biterm Latent Dirichlet Allocation topic model; clustering algorithm

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

Share & Cite This Article

MDPI and ACS Style

Wang, X.; Zhang, B.; Chang, F. Hot Topic Community Discovery on Cross Social Networks. Future Internet 2019, 11, 60.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics



[Return to top]
Future Internet EISSN 1999-5903 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top