@inproceedings{6dad21e114fc4e66b2b19b24451536d6,
title = "How pandemic spread in news: Text analysis using topic model",
abstract = "COVID-19 pandemic has made tremendous impact on the whole world, both the real world and the media atmosphere. Our research conducted a text analysis using LDA topic model. We first scraped 1127 articles and 5563 comments on SCMP covering COVID-19 from Jan 20 to May 19, then we trained the LDA model and tuned parameters based on the Cv coherence as the model evaluation method. With the optimal model, dominant topics, representative documents of each topic and the inconsistency between articles and comments are analyzed. Some factors of the inconsistency are discussed at last.",
keywords = "COVID-19, Text analysis, Topic modeling",
author = "Minghao Wang and Paolo Mengoni",
note = "Funding Information: This work was partly supported by the “Teaching Development Grant” - Department of Journalism, School of Communication, Hong Kong Baptist University, Kowloon Tong, Hong Kong, China Publisher Copyright: {\textcopyright} 2020 IEEE.; 2020 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology, WI-IAT 2020 (Virtual Conference) ; Conference date: 14-12-2020 Through 17-12-2020",
year = "2020",
month = dec,
doi = "10.1109/WIIAT50758.2020.00118",
language = "English",
isbn = "9781665430173",
series = "Proceedings - IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology, WI-IAT",
publisher = "IEEE",
pages = "764--770",
editor = "Jing He and Hemant Purohit and Guangyan Huang and Xiaoying Gao and Ke Deng",
booktitle = "Proceedings - 2020 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology, WI-IAT 2020",
address = "United States",
}