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Lexicon-based sentiment analysis of fake news on social media
Sabanci University, Turkey.
Linnaeus University, School of Business and Economics, Department of Marketing. (DISA;DISA-IDP)ORCID iD: 0000-0003-2248-0802
Sabanci University, Turkey.ORCID iD: 0000-0001-5435-6633
2022 (English)In: Presented at the AIRSI2022 Conference: Technologies 4.0 in Tourism, Services & Marketing, Zaragoza, Spain, July 11-13, 2022, Zaragoza, Spain, 2022, Zaragoza, Spain, 2022Conference paper, Published paper (Refereed)
Sustainable development
SDG 16: Promote peaceful and inclusive societies for sustainable development, provide access to justice for all and build effective, accountable and inclusive institutions at all levels
Abstract [en]

Social media is considered one of the primary sources of information. Besides all benefits that social media bring to human life, the popularity of social media simultaneously caused a rapid spread of fake news. Fake news poses a serious threat to societies since it enhances the polarity among different ideas, such as political parties. The fake news issue was further exacerbated during the COVID-19 Pandemic, and fake news studies attracted the attention of plenty of researchers (e.g., Apuke & Omar, 2021; Elías & Catalan-Matamoros, 2020). For example, Fake news claiming that 5G cell towers affect the human immune system has led to the burning of some cell towers in Europe (Mourad et al., 2020). Researchers claimed that fake stories spread more rapidly than true ones on social media (Vosoughi et al., 2018). The rapid spread of fake news makes companies and organizations vulnerable. Fake news about a company can directly affect the company's stock price and cause financial losses. A literature review reveals that scholars from multidisciplinary areas are interested in this topic; for instance, psychology scholars aim to answer research questions such as why people believe and share fake news (Talwar et al., 2019) and what are the characteristics of people who share or are involved in the spread of fake news (Ben-Gal et al., 2019; Brashier & Schacter, 2020). Computer science scholars aim to find ways to detect fake news, using machine learning techniques to create detection models (Faustini & Covões, 2020; Ozbay & Alatas, 2020). Emotion and sentiment analysis of fake news have not been studied in the literature; thus, this research will contribute to the field significantly.

Place, publisher, year, edition, pages
Zaragoza, Spain, 2022.
Keywords [en]
Lexicon, Sentiment Analysis, Fake news
National Category
Human Aspects of ICT
Identifiers
URN: urn:nbn:se:lnu:diva-115057OAI: oai:DiVA.org:lnu-115057DiVA, id: diva2:1679322
Conference
AIRSI2022 Conference: Technologies 4.0 in Tourism, Services & Marketing, Zaragoza, Spain, July 11-13, 2022
Available from: 2022-06-30 Created: 2022-06-30 Last updated: 2023-09-25Bibliographically approved

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Ozturkcan, Selcen

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CiteExportLink to record
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Citation style
  • apa
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