IDEAS home Printed from https://ideas.repec.org/a/gam/jftint/v15y2023i9p311-d1239212.html
   My bibliography  Save this article

Automatic Short Text Summarization Techniques in Social Media Platforms

Author

Listed:
  • Fahd A. Ghanem

    (Department of Computer Science & Engineering, PES College of Engineering, (Affiliated to University of Mysore), Mandya 571401, Karnataka, India
    Department of Computer Science, College of Education-Zabid, Hodeidah University, Hodeidah P.O. Box 3114, Yemen)

  • M. C. Padma

    (Department of Computer Science & Engineering, PES College of Engineering, (Affiliated to University of Mysore), Mandya 571401, Karnataka, India)

  • Ramez Alkhatib

    (BMB Nord, Research Center Borstel, Parkallee 35, 23845 Borstel, Germany)

Abstract

The rapid expansion of social media platforms has resulted in an unprecedented surge of short text content being generated on a daily basis. Extracting valuable insights and patterns from this vast volume of textual data necessitates specialized techniques that can effectively condense information while preserving its core essence. In response to this challenge, automatic short text summarization (ASTS) techniques have emerged as a compelling solution, gaining significant importance in their development. This paper delves into the domain of summarizing short text on social media, exploring various types of short text and the associated challenges they present. It also investigates the approaches employed to generate concise and meaningful summaries. By providing a survey of the latest methods and potential avenues for future research, this paper contributes to the advancement of ASTS in the ever-evolving landscape of social media communication.

Suggested Citation

  • Fahd A. Ghanem & M. C. Padma & Ramez Alkhatib, 2023. "Automatic Short Text Summarization Techniques in Social Media Platforms," Future Internet, MDPI, vol. 15(9), pages 1-27, September.
    • Handle: RePEc:gam:jftint:v:15:y:2023:i:9:p:311-:d:1239212
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1999-5903/15/9/311/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1999-5903/15/9/311/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Arkaitz Zubiaga, 2018. "A longitudinal assessment of the persistence of twitter datasets," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 69(8), pages 974-984, August.
    2. Nirmalya Thakur, 2022. "A Large-Scale Dataset of Twitter Chatter about Online Learning during the Current COVID-19 Omicron Wave," Data, MDPI, vol. 7(8), pages 1-16, August.
    3. Koustav Rudra & Ashish Sharma & Niloy Ganguly & Muhammad Imran, 2018. "Classifying and Summarizing Information from Microblogs During Epidemics," Information Systems Frontiers, Springer, vol. 20(5), pages 933-948, October.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Zhichao Fang & Jonathan Dudek & Rodrigo Costas, 2022. "Facing the volatility of tweets in altmetric research," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 73(8), pages 1192-1195, August.
    2. Stefan Stieglitz & Christian Meske & Björn Ross & Milad Mirbabaie, 2020. "Going Back in Time to Predict the Future - The Complex Role of the Data Collection Period in Social Media Analytics," Information Systems Frontiers, Springer, vol. 22(2), pages 395-409, April.
    3. Saptarshi Ghosh & Kripabandhu Ghosh & Debasis Ganguly & Tanmoy Chakraborty & Gareth J. F. Jones & Marie-Francine Moens & Muhammad Imran, 2018. "Exploitation of Social Media for Emergency Relief and Preparedness: Recent Research and Trends," Information Systems Frontiers, Springer, vol. 20(5), pages 901-907, October.
    4. Amir Hassan Zadeh & Hamed M. Zolbanin & Ramesh Sharda & Dursun Delen, 2019. "Social Media for Nowcasting Flu Activity: Spatio-Temporal Big Data Analysis," Information Systems Frontiers, Springer, vol. 21(4), pages 743-760, August.
    5. Shalak Mendon & Pankaj Dutta & Abhishek Behl & Stefan Lessmann, 2021. "A Hybrid Approach of Machine Learning and Lexicons to Sentiment Analysis: Enhanced Insights from Twitter Data of Natural Disasters," Information Systems Frontiers, Springer, vol. 23(5), pages 1145-1168, September.
    6. Libby Hemphill & Margaret L. Hedstrom & Susan Hautaniemi Leonard, 2021. "Saving social media data: Understanding data management practices among social media researchers and their implications for archives," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 72(1), pages 97-109, January.
    7. Muhammad Yasir & Sitara Afzal & Khalid Latif & Ghulam Mujtaba Chaudhary & Nazish Yameen Malik & Farhan Shahzad & Oh-young Song, 2020. "An Efficient Deep Learning Based Model to Predict Interest Rate Using Twitter Sentiment," Sustainability, MDPI, vol. 12(4), pages 1-16, February.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jftint:v:15:y:2023:i:9:p:311-:d:1239212. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.