@inproceedings{eb0d60d4ac0e4c81b1d3f5dea6f65d21,
title = "Machine Learning Approach to Detect Fake News, Misinformation in COVID-19 Pandemic",
abstract = "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.",
keywords = "Covid-19 misinformation, Fake news, machine learning, misinformation",
author = "Sirisha Bojjireddy and Chun, \{Soon Ae\} and James Geller",
note = "Publisher Copyright: {\textcopyright} 2021 Owner/Author.; 22nd Annual International Conference on Digital Government Research: Digital Innovations for Public Values: Inclusive Collaboration and Community, DGO 2021 ; Conference date: 09-06-2021 Through 11-06-2021",
year = "2021",
month = jun,
day = "9",
doi = "10.1145/3463677.3463762",
language = "English (US)",
series = "ACM International Conference Proceeding Series",
publisher = "Association for Computing Machinery",
pages = "575--578",
editor = "Jooho Lee and Pereira, \{Gabriela Viale\} and Sungsoo Hwang",
booktitle = "Proceedings of the 22nd Annual International Conference on Digital Government Research",
}