Open AccessApplying topic model combined with Kohonen networks to discover and visualize communities on social networks
Author(s) -
Ho Trung Thanh,
Nguyễn Quang Hưng,
Tran Duy Thanh
Publication year - 2020
Publication title -
khoa học và công nghệ: kinh tế - luật - quản lý
Language(s) - English
Resource type - Journals
ISSN - 2588-1051
DOI - 10.32508/stdjelm.v3i3.572
Subject(s) - computer science , self organizing map , vietnamese , newspaper , social network (sociolinguistics) , world wide web , online community , data science , artificial neural network , social media , artificial intelligence , sociology , linguistics , philosophy , media studies
Users are members of communities on social networks. Users’ interested topics keep changing, resulting in the change of their communities’ interested topics as well. Level, period of time, and interested topics represent features of a community which (i) change upon preferences of each user on social networks for making friends or being interested in topics (based on message content); (ii) are formed or change from online groups of friends or the suggestions to make friends. Hence, the link of users in communities can be viewed as a network of users by their features in social network communities. In this paper, the author studies and proposes a new model for discovering communities using Temporal-Author-Recipient-Topic (TART) model combined with Kohonen neural networks to discover communities of users with the same interested topics over different periods of time. The research goal is achieved through testing models on two Vietnamese datasets (collected from social networks at universities and online newspapers).