Fane-KG: A Semantic Knowledge Graph for Context-Based Fake News Detection on Social Media

被引:0
作者
Hani, Anoud Bani [1 ]
Adedugbe, Oluwasegun [2 ]
Al-Obeidat, Feras [1 ]
Benkhelifa, Elhadj [2 ]
Majdalawieh, Munir [1 ]
机构
[1] Zayed Univ, Coll Technol Innovat, Abu Dhabi, U Arab Emirates
[2] Staffordshire Univ, Sch Comp, Cloud Comp Applicat & Res Lab, Stoke On Trent, Staffs, England
来源
2020 SEVENTH INTERNATIONAL CONFERENCE ON SOCIAL NETWORK ANALYSIS, MANAGEMENT AND SECURITY (SNAMS) | 2020年
关键词
Knowledge Graphs; Semantic Graphs; Graph Database; Semantic Web; Fake News Detection; Social Data Analysis;
D O I
10.1109/SNAMS52053.2020.9336542
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Fake news detection on social media has been very challenging, with diverse techniques already implemented based on content of social media data. However, there is a growing need for use of social data context as well for detection techniques. Leveraging semantic technologies capabilities, this research focused on contextual modelling for social media data, with Twitter data utilised as case study. The raw data is aggregated, processed and transformed into a semantic knowledge graph based on RDF data which is subsequently stored within a graph database. With the tweets initially classified as either fake or real using Fakenewsnet application, the knowledge graph facilitates advanced data analytics and potential extension to the social context modelling developed. Furthermore, the modelled data, alongside ensuing inferential data based on class relationships within the knowledge graph constitute a vital input for data analytics with machine learning towards subsequent classification of other news articles as either fake or not.
引用
收藏
页码:123 / 128
页数:6
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