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Using Social Media to Detect Fake News Information Related to Product Marketing: The FakeAds Corpus

Author

Listed:
  • Noha Alnazzawi

    (Computer Science and Engineering Department, Yanbu University College, Royal Commission for Jubail and Yanbu, Yanbu Industrial City 41912, Saudi Arabia)

  • Najlaa Alsaedi

    (Computer Science Department, King Abdul Aziz University, Jeddah 21589, Saudi Arabia)

  • Fahad Alharbi

    (Data Management Specialist, Ministry of Interior, Public Security, Riyadh 12732, Saudi Arabia)

  • Najla Alaswad

    (Data Analyst Specialist, Princess Norah University, Riyadh 11671, Saudi Arabia)

Abstract

Nowadays, an increasing portion of our lives is spent interacting online through social media platforms, thanks to the widespread adoption of the latest technology and the proliferation of smartphones. Obtaining news from social media platforms is fast, easy, and less expensive compared with other traditional media platforms, e.g., television and newspapers. Therefore, social media is now being exploited to disseminate fake news and false information. This research aims to build the FakeAds corpus, which consists of tweets for product advertisements. The aim of the FakeAds corpus is to study the impact of fake news and false information in advertising and marketing materials for specific products and which types of products (i.e., cosmetics, health, fashion, or electronics) are targeted most on Twitter to draw the attention of consumers. The corpus is unique and novel, in terms of the very specific topic (i.e., the role of Twitter in disseminating fake news related to production promotion and advertisement) and also in terms of its fine-grained annotations. The annotation guidelines were designed with guidance by a domain expert, and the annotation is performed by two domain experts, resulting in a high-quality annotation, with agreement rate F-scores as high as 0.815.

Suggested Citation

  • Noha Alnazzawi & Najlaa Alsaedi & Fahad Alharbi & Najla Alaswad, 2022. "Using Social Media to Detect Fake News Information Related to Product Marketing: The FakeAds Corpus," Data, MDPI, vol. 7(4), pages 1-13, April.
    • Handle: RePEc:gam:jdataj:v:7:y:2022:i:4:p:44-:d:788723
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    References listed on IDEAS

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    1. Carvalho, Carlos & Klagge, Nicholas & Moench, Emanuel, 2011. "The persistent effects of a false news shock," Journal of Empirical Finance, Elsevier, vol. 18(4), pages 597-615, September.
    2. Alexandre Bovet & Hernán A. Makse, 2019. "Influence of fake news in Twitter during the 2016 US presidential election," Nature Communications, Nature, vol. 10(1), pages 1-14, December.
    3. Nida Aslam & Irfan Ullah Khan & Farah Salem Alotaibi & Lama Abdulaziz Aldaej & Asma Khaled Aldubaikil & M. Irfan Uddin, 2021. "Fake Detect: A Deep Learning Ensemble Model for Fake News Detection," Complexity, Hindawi, vol. 2021, pages 1-8, April.
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    Cited by:

    1. Andreea Nistor & Eduard Zadobrischi, 2022. "The Influence of Fake News on Social Media: Analysis and Verification of Web Content during the COVID-19 Pandemic by Advanced Machine Learning Methods and Natural Language Processing," Sustainability, MDPI, vol. 14(17), pages 1-24, August.
    2. Sergio Bolívar & Alicia Nieto-Reyes & Heather L. Rogers, 2023. "Statistical Depth for Text Data: An Application to the Classification of Healthcare Data," Mathematics, MDPI, vol. 11(1), pages 1-20, January.
    3. Vandana Sharma & Anurag Sinha & Ahmed Alkhayyat & Ankit Agarwal & Peddi Nikitha & Sable Ramkumar & Tripti Rathee & Mopuru Bhargavi & Nitish Kumar, 2024. "FL-XGBTC: federated learning inspired with XG-boost tuned classifier for YouTube spam content detection," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 15(10), pages 4923-4946, October.

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