Social Network Analysis Based on Topic Model with Temporal Factor

Social Network Analysis Based on Topic Model with Temporal Factor

Thanh Ho (University of Economics and Law, VNU-HCM, Ho Chi Minh City, Vietnam) and Phuc Do (University of Information Technology, VNU-HCM, Ho Chi Minh City, Vietnam)
Copyright: © 2018 |Pages: 16
DOI: 10.4018/IJKSS.2018010105


On social networks, each message has many features where the interested topics and the actors sending and receiving topics are important features. Unlike the traditional approach, which views each message belonging to a topic, the topic model is based on the approach, which indicates that each message has a mixture of many topics. However, topic model has limitations about discovering interested topics of actors with temporal factor and labelling latent topics. The article proposes a temporal-author-recipient-topic (TART) model based on: (i) discovering interested topics and analyzing the role of actors on social networks with the temporal factor; (ii) labelling the latent topics from topic model based on topic taxonomy; (iii) applying the temporal factor for finding the relation among factors in model; and (iv) finding out the variation of interested topics of actors with each period of time. An experimenting TART model on two corpora with 1,004,396 messages in Vietnamese and 25,009 actors by the software is built for SNA.
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Online social network (SN) has made great achievements in many fields (Kirchhoff, 2010; Wasserman et al., 1994; Aggarwal, 2011). Social Network Analysis (SNA) has attracted a lot of researches and applications for more than two decades with the goal of analyzing the interaction among actors together and identifying potential information in that interaction (Kirchhoff, 2010; Wasserman et al., 1994; Aggarwal, 2011; Sharma et al., 2015), a methodology to depict, diagnose, and evaluate health systems and networks therein. Social network analysis is a set of techniques to map, measure, and analyze social relationships between people, teams, and organizations (De Brún & McAuliffe, 2018), using social network analysis to track changes in collaboration over time, illustrated through a case study of a multi-tiered, three-year food systems project in North Carolina (Christensen & O’Sullivan, 2015).

The trend in recent years has focused on SNA. SN has grown rapidly because of allowing actors to interact easily. SN is not dependent on space and time when actors communicate with each other. Each actor on SN can make friends and chat with any other actors. There are typical SN such as Facebook, LinkedIn, MySpace, Twitter, etc. These SN, which are diverse and contain large amounts of data, are being exchanged messages of actors through social links.

The social links of SN can be represented by the graph structure, and the data is the message being exchanged between actors on SN including messages, multimedia data, etc. This is the source of data for analysis to find out what SN information and implicit knowledge are contained in the data on SN (Kirchhoff, 2010; Aggarwal, 2011; Hassan et al., 2014; Ho et al., 2015), make general predictions about the susceptibility of a population with a particular social structure to a new disease (Springe et al., 2017) and provide a guide on when to choose dynamic vs. static social network analysis, and how to choose the appropriate temporal scale for the dynamic network (Farine, 2017).

Because the characteristics of the SN with the quick spreading, messages exchanged on SN always contains a mixture of many important, positive, or negative topics which cause significant impact on the spread of topics. The topic of the message which is interested, exchanged and shared makes the spread from these actors to others, which makes up the community (Hassan et al., 2014; Li et al., 2014; Yin et al., 2012). Exploring interested topics of actors as well as analyzing the relationship between the information and data exchanged by each actor is a task with many challenges. For example, interested topics can change over time or sometimes a topic can be exchanged regularly and continuously in each period of time. Besides, the discussed message topic can be changed based on the level of interested actors.

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