Work

Fake social media news and distorted campaign detection framework using sentiment analysis & machine learning

Year: 2024

Type: article

Abstract: Social networking platforms have become one of the most engaging portals on the Internet, enabling global users to express views, share news and campaigns, or simply exchange information. Yet there is an increasing number of fake and spam profiles spreading and disseminating fake information. There have been several conscious attempts to determine and distinguish genuine news from fake campaigns, which spread malicious disinformation among social network users. Manual verification of the huge volume of posts and news disseminated via social media is not feasible and humanly impossible. To overcome the issue, this research presents a framework to use sentiment analysis based on emotions to investigate news, posts, and opinions on social media. The proposed model computes the sentiment score of content-based entities to detect fake or spam and detect Bot accounts. The authors also present an investigation of fake news campaigns and their impact using a machine learning algorithm with highly accurate results as compared to other similar methods. The results presented an accuracy of 99.68 %, which is significantly higher as compared to other methodologies delivering lower accuracy.

Source: Heliyon

DOI: 10.1016/j.heliyon.2024.e36049

Authors: Akashdeep Bhardwaj, Salil Bharany, SeongKi Kim

Institutions: University of Petroleum and Energy Studies, Chitkara University, Chosun University

Language: English

Topic: Misinformation and Its Impacts

Subfield: Sociology and Political Science

Field: Social Sciences

Domain: Social Sciences

Open Access status: gold

Estimated APC paid: $2,100

Funders: National Research Foundation, Ministry of Science, ICT and Future Planning, National Research Foundation of Korea, Ministry of Science and ICT, South Korea