Title: ANALYSIS OF SOCIAL NETWORKS BASED ON TOPIC MODELING AND APPLICATIONS
[Doctoral Thesis in Computer Science]
Author: Hồ Trung Thành
Summary:
Today, social networks play a crucial role in various fields such as economics, society, politics, education, and many others. Social networks enable individuals or communities to share messages, discuss, or provide feedback on topics of interest through social links. Among these, messages serve as implicit social links containing valuable information and knowledge. Each message has multiple characteristics, among which the topic of interest and the individuals involved in sending or receiving the topic are crucial. Unlike traditional approaches that view each message belonging to a single topic, the topic modeling approach reveals that each message contains a mixture of multiple topics, with each topic being of interest to multiple individuals or communities.
The topic modeling approach is suitable for analyzing social networks. However, previous studies still have limitations in exploring, labeling, and analyzing the temporal variations of topics of interest of individuals and communities based on social links with temporal factors. The thesis aims to address the limitations of previous studies. Specifically, the thesis focuses on two main objectives: (i) Building a Time-Author-Recipient-Topic (TART) model based on the topic modeling approach.
The tasks of the TART model include exploring topics of interest and analyzing the role of individuals in topics within messages exchanged on social networks; labeling topics; using temporal factors to divide factors such as the set of individuals sending and receiving topics, the set of topics, and identifying the temporal variations of topics of interest of individuals over time; analyzing changes in individuals' topics of interest; (ii) Developing a method for exploring communities on social networks based on the topic modeling approach with temporal factors and the Kohonen neural network method. The tasks of the community exploration method involve clustering individuals based on features such as topics and levels of interest to identify communities of individuals with common interests; analyzing the characteristic variations of communities on social networks.
Through experiments on proposed models and methods using two Vietnamese message datasets (collected from social networks in universities and online newspapers) with a software system built for social network analysis, the thesis has achieved its objectives.
The main contributions of the thesis are:
(1) Developing a method combining exploration and labeling of topics of interest on social networks based on the topic modeling approach and hierarchical topic trees.
(2) Building the TART model to explore the role of individuals based on the topic modeling approach with temporal factors. This model also plays an important role in exploring social links between individuals on social networks.
(3) Developing a method for exploring individual communities based on the topic modeling approach. The community exploration method is a combination of the TART model and the Kohonen neural network method to discover communities of individuals with common interests at each time period.
(4) Building social network analysis software to fully implement the six modules on the overall research framework of the thesis from data collection, preprocessing, topic exploration and labeling experiments, TART model experiments, and community exploration experiments.
The research results and experiments of the thesis have been published in specialized journals, conferences, and indexed by reputable scientific databases such as Thomson Reuters, Scopus, IEEE, Springer.
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Hạ Băng - Media Collaborator, University of Information Technology
English version: Phan Huy Hoang
