Machine Learning Approach to Detect Fake News, Misinformation in COVID-19 Pandemic

被引:3
|
作者
Bojjireddy, Sirisha [1 ]
Chun, Soon Ae [2 ]
Geller, James [1 ]
机构
[1] New Jersey Inst Technol, Newark, NJ 07102 USA
[2] CUNY Coll Staten Isl, Staten Isl, NY USA
关键词
Fake news; misinformation; machine learning; Covid-19;
D O I
10.1145/3463677.3463762
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 is false information about current events, intentionally created to mislead readers. The spread of such fake news has the potential to create a negative impact on individuals and society. With today's straightforward creation of social media posts, there has been an increasing amount of fake news, compared to traditional media in the past. We present one of the most serious societal issue of misinformation, specifically using Presidential Election and COVID-19 health related fake news. We present multi-dimensional approaches that organizations and individuals could utilize for detecting fake news, ranging from human/social approaches, to technical approaches to organizational trust/policy approaches. The Machine Learning approach as a technical solution is presented for automating the detection of fake news and misleading contents. A fake news detection web application is presented to make it easy for end users to determine whether an article is legitimate or fake.
引用
收藏
页码:575 / 578
页数:4
相关论文
共 50 条
  • [1] Machine Learning to Identify Fake News for COVID-19
    Isaakidou, Marianna
    Zoulias, Emmanouil
    Diomidous, Marianna
    [J]. PUBLIC HEALTH AND INFORMATICS, PROCEEDINGS OF MIE 2021, 2021, 281 : 108 - 112
  • [2] Perceptions of Fake News, Misinformation, and Disinformation Amid the COVID-19 Pandemic: A Qualitative Exploration
    Hadlington, Lee
    Harkin, Lydia J.
    Kuss, Daria
    Newman, Kristina
    Ryding, Francesca C.
    [J]. PSYCHOLOGY OF POPULAR MEDIA, 2023, 12 (01) : 40 - 49
  • [3] A Deep Learning Model to Detect Fake News about COVID-19
    Shanmugavel, Selva Birunda
    Rangaswamy, Kanniga Devi
    Muthukannan, Muthiah
    [J]. Recent Advances in Computer Science and Communications, 2023, 16 (09) : 58 - 66
  • [4] A Comparative Approach to Detecting COVID-19 Fake News through Machine Learning Models
    Al-Azazi, Zyad
    Haraty, Ramzi A.
    [J]. 38TH INTERNATIONAL CONFERENCE ON INFORMATION NETWORKING, ICOIN 2024, 2024, : 490 - 495
  • [5] Fake news in face of the COVID-19 pandemic
    de Matos, Rafael Christian
    [J]. VIGILANCIA SANITARIA EM DEBATE-SOCIEDADE CIENCIA & TECNOLOGIA, 2020, 8 (03): : 78 - 85
  • [6] Using Deep Learning Models to Detect Fake News about COVID-19
    Chen, Mu-Yen
    Lai, Yi-Wei
    Lian, Jiunn-Woei
    [J]. ACM TRANSACTIONS ON INTERNET TECHNOLOGY, 2023, 23 (02)
  • [7] Comparing Traditional Machine Learning Methods for COVID-19 Fake News
    Almatarneh, Sattam
    Gamallo, Pablo
    Al Shargabi, Bassam
    Al-Khassawneh, Yazan
    Alzubi, Raed
    [J]. 2021 22ND INTERNATIONAL ARAB CONFERENCE ON INFORMATION TECHNOLOGY (ACIT), 2021, : 736 - 739
  • [8] Fighting Against Fake News During Pandemic Era: Does Providing Related News Help Student Internet Users to Detect COVID-19 Misinformation?
    Uddin, Borhan
    Reza, Md. Nahid
    Islam, Md Saiful
    Ahsan, Hasib
    Amin, Mohammad Ruhul
    [J]. PROCEEDINGS OF THE 13TH ACM WEB SCIENCE CONFERENCE, WEBSCI 2021, 2020, : 178 - 186
  • [9] Toxic Fake News Detection and Classification for Combating COVID-19 Misinformation
    Wani, Mudasir Ahmad
    ELAffendi, Mohammad
    Shakil, Kashish Ara
    Abuhaimed, Ibrahem Mohammed
    Nayyar, Anand
    Hussain, Amir
    Abd El-Latif, Ahmed A.
    [J]. IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS, 2024, 11 (04): : 5101 - 5118
  • [10] The COVID-19 pandemic and the fake news: a literature review
    Rosa, Tiago
    Delduque, Maria Celia
    Alves, Sandra Mara Campos
    [J]. SAUDE E SOCIEDADE, 2023, 32