IDEAS home Printed from https://ideas.repec.org/a/bcp/journl/v8y2024i3p2294-2310.html
   My bibliography  Save this article

Capacity to Detect Fake News: Its Relationship to the Utilization of Online Platforms of BSED Social Studies Student

Author

Listed:
  • Ricardo O. Quiñones

    (College of Education, Notre Dame of Midsayap College, Midsayap, Cotabato, Philippines)

  • Sergio D. Mahinay, JR.

    (College of Education, Notre Dame of Midsayap College, Midsayap, Cotabato, Philippines)

  • Francis Dave C. Amiler

    (College of Education, Notre Dame of Midsayap College, Midsayap, Cotabato, Philippines)

  • Amy Jazz B. Flauta

    (College of Education, Notre Dame of Midsayap College, Midsayap, Cotabato, Philippines)

  • James A. Tubongbanua

    (College of Education, Notre Dame of Midsayap College, Midsayap, Cotabato, Philippines)

Abstract

The present study investigated the capacity to detect fake news of Social Studies students and if there is a significant relationship between the respondents’ sex, year level, age as well as their utilization of online platforms. This study was conducted only at Notre Dame of Midsayap College, specifically among the BSED Social Studies students during the second semester of the Academic Year 2022-2023. The method used in this study was causal-comparative and correlational research designs with a stratified random sampling technique. The instrument used was a researcher-made questionnaire with 34 randomly selected respondents. Despite the results, the data gathered showed that the respondents have a high capacity to detect fake news because they always check the legitimacy of the article before believing the news, compare the news with those of the more trusted sources, look for evidence that will support the claim of the new, among others. The findings of this study showed that age has a weak direct or positive relationship to the capacity to detect fake news, and that relationship is not significant; that year level has a moderately direct solid or positive relationship to the capacity to detect fake news, and that relationship is highly significant. There is no significant difference in the capacity to detect fake news according to sex. Furthermore, the utilization of online platforms also has no significant relationship with the capacity to detect fake news from the respondents. Finally, there is a very weak inverse or negative relationship between the capacity to detect fake news and the utilization of online platforms.

Suggested Citation

  • Ricardo O. Quiñones & Sergio D. Mahinay, JR. & Francis Dave C. Amiler & Amy Jazz B. Flauta & James A. Tubongbanua, 2024. "Capacity to Detect Fake News: Its Relationship to the Utilization of Online Platforms of BSED Social Studies Student," International Journal of Research and Innovation in Social Science, International Journal of Research and Innovation in Social Science (IJRISS), vol. 8(3), pages 2294-2310, March.
  • Handle: RePEc:bcp:journl:v:8:y:2024:i:3:p:2294-2310
    as

    Download full text from publisher

    File URL: https://www.rsisinternational.org/journals/ijriss/Digital-Library/volume-8-issue-3/2294-2310.pdf
    Download Restriction: no

    File URL: https://rsisinternational.org/journals/ijriss/articles/capacity-to-detect-fake-news-its-relationship-to-the-utilization-of-online-platforms-of-bsed-social-studies-student/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Chengcheng Shao & Giovanni Luca Ciampaglia & Onur Varol & Kai-Cheng Yang & Alessandro Flammini & Filippo Menczer, 2018. "The spread of low-credibility content by social bots," Nature Communications, Nature, vol. 9(1), pages 1-9, December.
    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. Kai-Cheng Yang & Emilio Ferrara & Filippo Menczer, 2022. "Botometer 101: social bot practicum for computational social scientists," Journal of Computational Social Science, Springer, vol. 5(2), pages 1511-1528, November.
    2. Massimo Marchiori & Lino Possamai, 2020. "Strategies of Success for Social Networks: Mermaids and Temporal Evolution," Future Internet, MDPI, vol. 12(2), pages 1-30, February.
    3. Samuel F Rosenblatt & Jeffrey A Smith & G Robin Gauthier & Laurent Hébert-Dufresne, 2020. "Immunization strategies in networks with missing data," PLOS Computational Biology, Public Library of Science, vol. 16(7), pages 1-21, July.
    4. Hugo Queiroz Abonizio & Janaina Ignacio de Morais & Gabriel Marques Tavares & Sylvio Barbon Junior, 2020. "Language-Independent Fake News Detection: English, Portuguese, and Spanish Mutual Features," Future Internet, MDPI, vol. 12(5), pages 1-18, May.
    5. Wen Shi & Haohuan Fu & Peinan Wang & Changfeng Chen & Jie Xiong, 2020. "#Climatechange vs. #Globalwarming: Characterizing Two Competing Climate Discourses on Twitter with Semantic Network and Temporal Analyses," IJERPH, MDPI, vol. 17(3), pages 1-22, February.
    6. Matilde Giaccherini & Joanna Kopinska & Gabriele Rovigatti, 2022. "Vax Populi: The Social Costs of Online Vaccine Skepticism," CESifo Working Paper Series 10184, CESifo.
    7. Hyehyun Hong & Hyun Jee Oh, 2020. "Utilizing Bots for Sustainable News Business: Understanding Users’ Perspectives of News Bots in the Age of Social Media," Sustainability, MDPI, vol. 12(16), pages 1-16, August.
    8. John Higgins & Tarun Sabarwal, 2023. "Control and spread of contagion in networks with global effects," Journal of Public Economic Theory, Association for Public Economic Theory, vol. 25(6), pages 1149-1187, December.
    9. Malik, Nishtha & Kar, Arpan Kumar & Tripathi, Shalini Nath & Gupta, Shivam, 2023. "Exploring the impact of fairness of social bots on user experience," Technological Forecasting and Social Change, Elsevier, vol. 197(C).
    10. Xia, Huosong & Wang, Yuan & Zhang, Justin Zuopeng & Zheng, Leven J. & Kamal, Muhammad Mustafa & Arya, Varsha, 2023. "COVID-19 fake news detection: A hybrid CNN-BiLSTM-AM model," Technological Forecasting and Social Change, Elsevier, vol. 195(C).
    11. Min, Yong & Zhou, Yuying & Liu, Yuhang & Zhang, Jian & Xuan, Qi & Jin, Xiaogang & Cai, He, 2021. "The role of degree correlation in shaping filter bubbles in social networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 584(C).
    12. Andrea Moscadelli & Giuseppe Albora & Massimiliano Alberto Biamonte & Duccio Giorgetti & Michele Innocenzio & Sonia Paoli & Chiara Lorini & Paolo Bonanni & Guglielmo Bonaccorsi, 2020. "Fake News and Covid-19 in Italy: Results of a Quantitative Observational Study," IJERPH, MDPI, vol. 17(16), pages 1-13, August.
    13. Ahmed Abouzeid & Ole-Christoffer Granmo & Christian Webersik & Morten Goodwin, 2021. "Learning Automata-based Misinformation Mitigation via Hawkes Processes," Information Systems Frontiers, Springer, vol. 23(5), pages 1169-1188, September.
    14. Chuhan Wu & Fangzhao Wu & Tao Qi & Wei-Qiang Zhang & Xing Xie & Yongfeng Huang, 2022. "Removing AI’s sentiment manipulation of personalized news delivery," Palgrave Communications, Palgrave Macmillan, vol. 9(1), pages 1-9, December.
    15. Alan C. Logan & Susan H. Berman & Brian M. Berman & Susan L. Prescott, 2021. "Healing Anthropocene Syndrome: Planetary Health Requires Remediation of the Toxic Post-Truth Environment," Challenges, MDPI, vol. 12(1), pages 1-25, January.
    16. Kinga Makovi & Anahit Sargsyan & Wendi Li & Jean-François Bonnefon & Talal Rahwan, 2023. "Trust within human-machine collectives depends on the perceived consensus about cooperative norms," Nature Communications, Nature, vol. 14(1), pages 1-12, December.
    17. Menghan Zhang & Xue Qi & Ze Chen & Jun Liu, 2022. "Social Bots’ Involvement in the COVID-19 Vaccine Discussions on Twitter," IJERPH, MDPI, vol. 19(3), pages 1-14, January.
    18. Csaba Both & Nima Dehmamy & Rose Yu & Albert-László Barabási, 2023. "Accelerating network layouts using graph neural networks," Nature Communications, Nature, vol. 14(1), pages 1-9, December.
    19. Prasha Shrestha & Arun Sathanur & Suraj Maharjan & Emily Saldanha & Dustin Arendt & Svitlana Volkova, 2020. "Multiple social platforms reveal actionable signals for software vulnerability awareness: A study of GitHub, Twitter and Reddit," PLOS ONE, Public Library of Science, vol. 15(3), pages 1-28, March.
    20. John Higgins & Tarun Sabarwal, 2021. "Control and Spread of Contagion in Networks," WORKING PAPERS SERIES IN THEORETICAL AND APPLIED ECONOMICS 202201, University of Kansas, Department of Economics, revised Jan 2022.

    More about this item

    Statistics

    Access and download statistics

    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:bcp:journl:v:8:y:2024:i:3:p:2294-2310. 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: Dr. Pawan Verma (email available below). General contact details of provider: https://www.rsisinternational.org/journals/ijriss/ .

    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.