%0 Conference Proceedings
%T An Approach Utilizing Linguistic Features for Fake News Detection
%+ International Hellenic University
%A Kasseropoulos, Dimitrios, Panagiotis
%A Tjortjis, Christos
%Z Part 15: Natural Language
%< avec comité de lecture
%( IFIP Advances in Information and Communication Technology
%B 17th IFIP International Conference on Artificial Intelligence Applications and Innovations (AIAI)
%C Hersonissos, Crete, Greece
%Y Ilias Maglogiannis
%Y John Macintyre
%Y Lazaros Iliadis
%I Springer International Publishing
%3 Artificial Intelligence Applications and Innovations
%V AICT-627
%P 646-658
%8 2021-06-25
%D 2021
%R 10.1007/978-3-030-79150-6_51
%K Fake news
%K Social media
%K Machine Learning (ML)
%K Natural Language Processing (NLP)
%K Association Rule (AR) Mining
%K Data mining
%Z Computer Science [cs]Conference papers
%X Easy propagation and access to information on the web has the potential to become a serious issue when it comes to disinformation. The term “fake news” describes the intentional propagation of news with the intention to mislead and harm the public and has gained more attention recently. This paper proposes a style-based Machine Learning (ML) approach, which relies on the textual information from news, such as manually extracted lexical features e.g. part of speech counts, and evaluates the performance of several ML algorithms. We identified a subset of the best performing linguistic features, using information-based metrics, which tend to agree with the literature. We also, combined Named Entity Recognition (NER) functionality with the Frequent Pattern (FP) Growth association rule algorithm to gain a deeper perspective of the named entities used in the two classes. Both methods reinforce the claim that fake and real news have limited differences in content, setting limitations to style-based methods. Results showed that convolutional neural networks resulted in the best accuracy, outperforming the rest of the algorithms.
%G English
%Z TC 12
%Z WG 12.5
%2 https://inria.hal.science/hal-03287679/document
%2 https://inria.hal.science/hal-03287679/file/509922_1_En_51_Chapter.pdf
%L hal-03287679
%U https://inria.hal.science/hal-03287679
%~ IFIP
%~ IFIP-AICT
%~ IFIP-TC
%~ IFIP-WG
%~ IFIP-TC12
%~ IFIP-AIAI
%~ IFIP-WG12-5
%~ IFIP-AICT-627