Stemming the Tide of Fake News about the COVID-19 Pandemic

Chih Yuan Li; Soon Ae Chun; James Geller

doi:10.32473/flairs.v35i.130716

Stemming the Tide of Fake News about the COVID-19 Pandemic

Chih Yuan Li, Soon Ae Chun, James Geller

Research output: Contribution to journal › Conference article › peer-review

2 Scopus citations

Abstract

While the world has been combating COVID, there has also been an ongoing “Infodemic,” caused by the spread of fake news about the pandemic. Due to the rapid data sharing on social media, the impact of fake news can be quite damaging. Citizens might mistake fakes news for real news. Human lives have been lost due to fake information about COVID. Our goal is to identify fake news on social media and help stem the spread by deep learning approaches. To understand the different characteristics in fake and real news, we conducted behavioral and sentiment analyses between fake and real news regarding the COVID pandemic. We then further built detection models based on feature elimination, and we identified differences of model robustness based on selected features.

Original language	English (US)
Journal	Proceedings of the International Florida Artificial Intelligence Research Society Conference, FLAIRS
Volume	35
DOIs	https://doi.org/10.32473/flairs.v35i.130716
State	Published - 2022
Event	35th International Florida Artificial Intelligence Research Society Conference, FLAIRS-35 2022 - Jensen Beach, United States Duration: May 15 2022 → May 18 2022

All Science Journal Classification (ASJC) codes

Artificial Intelligence
Software

Keywords

COVID-19
Fake News Identification
Infodemic
Machine Learning
Metadata Tags
Sentiment Analysis

Access to Document

10.32473/flairs.v35i.130716

Cite this

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title = "Stemming the Tide of Fake News about the COVID-19 Pandemic",

abstract = "While the world has been combating COVID, there has also been an ongoing “Infodemic,” caused by the spread of fake news about the pandemic. Due to the rapid data sharing on social media, the impact of fake news can be quite damaging. Citizens might mistake fakes news for real news. Human lives have been lost due to fake information about COVID. Our goal is to identify fake news on social media and help stem the spread by deep learning approaches. To understand the different characteristics in fake and real news, we conducted behavioral and sentiment analyses between fake and real news regarding the COVID pandemic. We then further built detection models based on feature elimination, and we identified differences of model robustness based on selected features.",

keywords = "COVID-19, Fake News Identification, Infodemic, Machine Learning, Metadata Tags, Sentiment Analysis",

author = "Li, {Chih Yuan} and Chun, {Soon Ae} and James Geller",

note = "Publisher Copyright: {\textcopyright} 2022, by the authors. All rights reserved.; 35th International Florida Artificial Intelligence Research Society Conference, FLAIRS-35 2022 ; Conference date: 15-05-2022 Through 18-05-2022",

year = "2022",

doi = "10.32473/flairs.v35i.130716",

language = "English (US)",

volume = "35",

journal = "Proceedings of the International Florida Artificial Intelligence Research Society Conference, FLAIRS",

issn = "2334-0754",

publisher = "Florida Online Journals, University of Florida",

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TY - JOUR

T1 - Stemming the Tide of Fake News about the COVID-19 Pandemic

AU - Li, Chih Yuan

AU - Chun, Soon Ae

AU - Geller, James

PY - 2022

Y1 - 2022

N2 - While the world has been combating COVID, there has also been an ongoing “Infodemic,” caused by the spread of fake news about the pandemic. Due to the rapid data sharing on social media, the impact of fake news can be quite damaging. Citizens might mistake fakes news for real news. Human lives have been lost due to fake information about COVID. Our goal is to identify fake news on social media and help stem the spread by deep learning approaches. To understand the different characteristics in fake and real news, we conducted behavioral and sentiment analyses between fake and real news regarding the COVID pandemic. We then further built detection models based on feature elimination, and we identified differences of model robustness based on selected features.

AB - While the world has been combating COVID, there has also been an ongoing “Infodemic,” caused by the spread of fake news about the pandemic. Due to the rapid data sharing on social media, the impact of fake news can be quite damaging. Citizens might mistake fakes news for real news. Human lives have been lost due to fake information about COVID. Our goal is to identify fake news on social media and help stem the spread by deep learning approaches. To understand the different characteristics in fake and real news, we conducted behavioral and sentiment analyses between fake and real news regarding the COVID pandemic. We then further built detection models based on feature elimination, and we identified differences of model robustness based on selected features.

KW - COVID-19

KW - Fake News Identification

KW - Infodemic

KW - Machine Learning

KW - Metadata Tags

KW - Sentiment Analysis

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U2 - 10.32473/flairs.v35i.130716

DO - 10.32473/flairs.v35i.130716

M3 - Conference article

AN - SCOPUS:85131136560

SN - 2334-0754

VL - 35

JO - Proceedings of the International Florida Artificial Intelligence Research Society Conference, FLAIRS

JF - Proceedings of the International Florida Artificial Intelligence Research Society Conference, FLAIRS

T2 - 35th International Florida Artificial Intelligence Research Society Conference, FLAIRS-35 2022

Y2 - 15 May 2022 through 18 May 2022

ER -

Stemming the Tide of Fake News about the COVID-19 Pandemic

Abstract

All Science Journal Classification (ASJC) codes

Keywords

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