Fake News Detection and Behavioral Analysis: Case of COVID-19
- URL: http://arxiv.org/abs/2305.16057v1
- Date: Thu, 25 May 2023 13:42:08 GMT
- Title: Fake News Detection and Behavioral Analysis: Case of COVID-19
- Authors: Chih-Yuan Li, Navya Martin Kollapally, Soon Ae Chun, James Geller
- Abstract summary: "Infodemic" due to spread of fake news regarding the pandemic has been a global issue.
Readers could mistake fake news for real news, and consequently have less access to authentic information.
It is challenging to accurately identify fake news data in social media posts.
- Score: 0.22940141855172028
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: While the world has been combating COVID-19 for over three years, an ongoing
"Infodemic" due to the spread of fake news regarding the pandemic has also been
a global issue. The existence of the fake news impact different aspect of our
daily lives, including politics, public health, economic activities, etc.
Readers could mistake fake news for real news, and consequently have less
access to authentic information. This phenomenon will likely cause confusion of
citizens and conflicts in society. Currently, there are major challenges in
fake news research. It is challenging to accurately identify fake news data in
social media posts. In-time human identification is infeasible as the amount of
the fake news data is overwhelming. Besides, topics discussed in fake news are
hard to identify due to their similarity to real news. The goal of this paper
is to identify fake news on social media to help stop the spread. We present
Deep Learning approaches and an ensemble approach for fake news detection. Our
detection models achieved higher accuracy than previous studies. The ensemble
approach further improved the detection performance. We discovered feature
differences between fake news and real news items. When we added them into the
sentence embeddings, we found that they affected the model performance. We
applied a hybrid method and built models for recognizing topics from posts. We
found half of the identified topics were overlapping in fake news and real
news, which could increase confusion in the population.
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