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

Noha Alnazzawi, Najlaa Alsaedi, Fahad Alharbi and Najla Alaswad
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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

Data, 2022, vol. 7, issue 4, 1-13

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.

Keywords: social media; fake news; corpus construction; text mining (search for similar items in EconPapers)
JEL-codes: C8 C80 C81 C82 C83 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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