Abstract
The tremendous growth and impact of fake news as a hot research field gained the public’s attention and threatened their safety in recent years. However, there is a wide range of developed fashions to detect fake contents, either those human-based approaches or machine-based approaches; both have shown inadequacy and limitations, especially those fully automatic approaches. The purpose of this analytic study of media news language is to investigate and identify the linguistic features and their contribution in analyzing data to detect, filter, and differentiate between fake and authentic news texts. This study outlines promising uses of linguistic indicators and adds a rather unconventional outlook to prior literature. It utilizes qualitative and quantitative data analysis as an analytic method to identify systematic nuances between fake and factual news in terms of detecting and comparing 16 attributes under three main linguistic features categories (lexical, grammatical, and syntactic features) assigned manually to news texts. The obtained datasets consist of publicly available right documents on the Politi-fact website and the raw (test) data set collected randomly from news posts on Facebook pages. The results show that linguistic features, especially grammatical features, help determine untrustworthy texts and demonstrate that most of the test news tends to be unreliable articles.
Highlights
Words played a critical role in shaping the public’s attitudes and opinions in news media
This study’s obtained data consists of 20 articles collected from the Politi-fact website and 20 articles randomly chosen from different Facebook pages to assess their authenticity based on the actual reports discriminated linguistic features cues
This study aims to perform a qualitative and quantitative linguistic analysis of the content structure and news articles’ style to identify fake news articles’ linguistic features and classify news texts, either false or authentic
Summary
Words played a critical role in shaping the public’s attitudes and opinions in news media. Fake news has attracted worldwide attention and multiplied organized efforts have been dedicated to fact-checking. They attempted to counter online misinformation transmit raises in media outlets. According to Conroy (2015), Fake news detection is the projection of a news article (news report, editorial, and expose) to be intentionally deceiving. It is not a new idea, but what makes it a world attractive topic is that most people worldwide get their news from social media as it breaks the distance barriers among individuals and societies (Shu et al, 2019). It is the easiest, cheapest, and fastest way to publish fake news online, promoting malicious entities to create, print, and spread fake news
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
