Identification and Analysis of COVID-19-related Misinformation Tweets via Kullback-Leibler Divergence for Informativeness and Phraseness and Biterm Topic Modeling-Reference-Cited by-同舟云学术

Identification and Analysis of COVID-19-related Misinformation Tweets via Kullback-Leibler Divergence for Informativeness and Phraseness and Biterm Topic Modeling

Published:2022-02-21 Issue: Volume: Page:
ISSN:
Container-title:2022 International Conference on Artificial Intelligence in Information and Communication (ICAIIC)
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Author:

Clamor Thomas Daniel S.¹,Solano Geoffrey A.¹,Oco Nathaniel²,Catapang Jasper Kyle³,Cleofas Jerome²,Isip-Tan Iris Thiele¹

Affiliation:

1. University of the Philippines Manila,Manila City,Philippines

2. De La Salle University,Manila City,Philippines

3. University of Birmingham,Birmingham,United Kingdom

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9722611/9722613/09722623.pdf?arnumber=9722623

Reference13 articles.

1. A language model approach to keyphrase extraction

2. Qualitative data analysis of disaster risk reduction suggestions assisted by topic modeling and word2vec

3. A biterm topic model for short texts

4. Toward an enriched understanding of factors influencing Filipino behavior during elections through the analysis of Twitter data

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1. Interdisciplinary Approach to Identify and Characterize COVID-19 Misinformation on Twitter: Mixed Methods Study;JMIR Formative Research;2023-06-28

2. Topic and sentiment analysis of responses to Muslim clerics’ misinformation correction about COVID-19 vaccine: Comparison of three machine learning models;Online Media and Global Communication;2022-09-01

3. Interdisciplinary Approach to Identify and Characterize COVID-19 Misinformation on Twitter: Mixed Methods Study (Preprint);2022-07-17