Deep Learning for Combating Misinformation in Multicategorical Text Contents

Author:

Kozik Rafał1,Mazurczyk Wojciech2,Cabaj Krzysztof2,Pawlicka Aleksandra3ORCID,Pawlicki Marek1,Choraś Michał1

Affiliation:

1. Faculty of Telecommunications, Computer Science and Electrical Engineering, Bydgoszcz University of Science and Technology, 85-796 Bydgoszcz, Poland

2. Institute of Computer Science, Division of Software Engineering and Computer Architecture, Warsaw University of Technology, 00-661 Warsaw, Poland

3. Faculty of Applied Linguistics, University of Warsaw, 00-927 Warsaw, Poland

Abstract

Currently, one can observe the evolution of social media networks. In particular, humans are faced with the fact that, often, the opinion of an expert is as important and significant as the opinion of a non-expert. It is possible to observe changes and processes in traditional media that reduce the role of a conventional ‘editorial office’, placing gradual emphasis on the remote work of journalists and forcing increasingly frequent use of online sources rather than actual reporting work. As a result, social media has become an element of state security, as disinformation and fake news produced by malicious actors can manipulate readers, creating unnecessary debate on topics organically irrelevant to society. This causes a cascading effect, fear of citizens, and eventually threats to the state’s security. Advanced data sensors and deep machine learning methods have great potential to enable the creation of effective tools for combating the fake news problem. However, these solutions often need better model generalization in the real world due to data deficits. In this paper, we propose an innovative solution involving a committee of classifiers in order to tackle the fake news detection challenge. In that regard, we introduce a diverse set of base models, each independently trained on sub-corpora with unique characteristics. In particular, we use multi-label text category classification, which helps formulate an ensemble. The experiments were conducted on six different benchmark datasets. The results are promising and open the field for further research.

Funder

National Center for Research and Development

EIG CONCERT-Japan call to the project Detection of fake newS on SocIal MedIa pLAtfoRms “DISSIMILAR”

Publisher

MDPI AG

Subject

Electrical and Electronic Engineering,Biochemistry,Instrumentation,Atomic and Molecular Physics, and Optics,Analytical Chemistry

Reference44 articles.

1. Shu, K., Cui, L., Wang, S., Lee, D., and Liu, H. (2019, January 4–8). DEFEND: Explainable Fake News Detection. Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD ’19, Anchorage, AK, USA.

2. O’Brien, N., Latessa, S., Evangelopoulos, G., and Boix, X. (2018, January 2–8). The Language of Fake News: Opening the Black-Box of Deep Learning Based Detectors. Proceedings of the Workshop on “AI for Social Good”, NIPS 2018, Montreal, QC, Canada.

3. Convolutional neural network with margin loss for fake news detection;Goldani;Inf. Process. Manag.,2021

4. A benchmark study of machine learning models for online fake news detection;Khan;Mach. Learn. Appl.,2021

5. Shu, K., Wang, S., and Liu, H. (2017). Exploiting Tri-Relationship for Fake News Detection. arXiv.

Cited by 3 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3