General Information
    • ISSN: 1793-8201 (Print), 2972-4511 (Online)
    • Abbreviated Title: Int. J. Comput. Theory Eng.
    • Frequency: Quarterly
    • DOI: 10.7763/IJCTE
    • Editor-in-Chief: Prof. Mehmet Sahinoglu
    • Associate Editor-in-Chief: Assoc. Prof. Alberto Arteta, Assoc. Prof. Engin Maşazade
    • Managing Editor: Ms. Mia Hu
    • Abstracting/Indexing: Scopus (Since 2022), INSPEC (IET), CNKI,  Google Scholar, EBSCO, etc.
    • Average Days from Submission to Acceptance: 192 days
    • APC: 800 USD
    • E-mail:
    • Journal Metrics:
    • SCImago Journal & Country Rank
Prof. Mehmet Sahinoglu
Computer Science Department, Troy University, USA
I'm happy to take on the position of editor in chief of IJCTE. We encourage authors to submit papers concerning any branch of computer theory and engineering.

IJCTE 2023 Vol.15(2): 62-67
DOI: 10.7763/IJCTE.2023.V15.1332

Using Graph Evolutionary to Retrieve More Related Tweets

Patta Yovithaya* and Sukree Sinthupinyo

Manuscript received August 16, 2022; revised September 10, 2022; accepted November 2, 2022.

Abstract—Due to its popularity and daily active users, social media has become powerful and influential in the last decade. With the nature of a micro-blogging platform, instant messages and the latest short posts are sent throughout the network on Twitter. Therefore, most users utilize Twitter to update breaking news or the latest events. Since a huge volume of tweet messages have been published on Twitter, event evolution has also rapidly developed into related events within similar topics. In this study, we present a novel method to retrieve tweets that relate to a given query term. Not only perfectly matched tweets, but more related tweets will be retrieved. The collected tweet data are processed and constructed as an original network. With the benefits of social network analysis, a simplification-based summarization approach is applied to ignore information that has less importance while preserving significant information in the network based on centrality measurement and clustering coefficient. Using the evolutionary of graph-based representation extends the relationship diffusion to assist related information retrieval. Experiments were performed using Thai news datasets and the framework performance was evaluated by precision, recall, and f-score. The experimental results show that our framework outperformed the baseline methods which derived a similarity score based on the word embedded vector to find relevant documents.

Index Terms—Graph evolutionary, graph summarization, information retrieval

Patta Yovithaya and Sukree Sinthupinyo are with Department of Computer Engineering, Faculty of Engineering, Chulalongkorn University, Thailand.
*Correspondence: (P.Y.)


Cite:Patta Yovithaya and Sukree Sinthupinyo, "Using Graph Evolutionary to Retrieve More Related Tweets," International Journal of Computer Theory and Engineering vol. 15, no. 2, pp. 62-67, 2023.

Copyright © 2023 by the authors. 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).

Copyright © 2008-2024. International Association of Computer Science and Information Technology. All rights reserved.