ENHANCING FAKE NEWS DETECTION ON SOCIAL MEDIA THROUGH ADVANCED MACHINE LEARNING AND USER PROFILE ANALYSIS

Title

ENHANCING FAKE NEWS DETECTION ON SOCIAL MEDIA THROUGH ADVANCED MACHINE LEARNING AND USER PROFILE ANALYSIS

Creator

Khan Z.A.; Rekha V.

Description

Social media news consumption is growing in popularity. Users find social media appealing because it's inexpensive, easy to use, and information spreads quickly. Social media does, however, also contribute to the spread of false information. The detection of fake news has gained more attention due to the negative effects it has on society. However, since fake news is created to seem like real news, the detection performance when relying solely on news contents is typically unsatisfactory. Therefore, a thorough understanding of the connection between fake news and social media user profiles is required. In order to detect fake news, this research paper investigates the use of machine learning techniques, covering important topics like feature integration, user profiles, and dataset analysis. To generate extensive feature sets, the study integrates User Profile Features (UPF), Linguistic Inquiry and Word Count (LIWC) features, and Rhetorical Structure Theory (RST) features. Principal Component Analysis (PCA) is used to reduce dimensionality and lessen the difficulties presented by high-dimensional datasets. The study entails a comprehensive assessment of multiple machine learning models using datasets from "Politifact" and "Gossipofact," which cover a range of data processing methods. The evaluation of the XGBoost classification model is further enhanced by the analysis of Receiver Operating Characteristic (ROC) curves. The results demonstrate the effectiveness of particular combinations of features and models, with XGBoost outperforming other models on the suggested unified feature set (ALL). 2023 Little Lion Scientific.

Source

Journal of Theoretical and Applied Information Technology, Vol-101, No. 1, pp. 274-286.

Date

2024-01-01

Publisher

Little Lion Scientific

Subject

Linguistic Inquiry and Word Count features; Machine learning techniques; Principal Component Analysis (PCA); Rhetorical Structure Theory (RST) features; Unified feature set (ALL); User profile features (UPF)

Coverage

Khan Z.A., Department of Computer Science and Engineering, School of Engineering and Technology, Christ University, Bangalore, India; Rekha V., Department of Computer Science and Engineering, School of Engineering and Technology, Christ University, Bangalore, India

Rights

Restricted Access

Relation

ISSN: 19928645

Format

Online

Language

English

Type

Article

Identifier

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85182870014&partnerID=40&md5=2eec1cda6575d0567c0679390e008ab7