Pandemic-Related Media Coverage in Overseas Chinese Media Based on Big Data Analysis: A Case Study of “oushi 1983”
- DOI
- 10.2991/978-94-6463-064-0_18How to use a DOI?
- Keywords
- big data analysis; K-means algorithm; overseas Chinese media; pandemic-related media coverage
- Abstract
Under the background of the big data era, analysis and processing technology based on big data has given birth to new methods of humanities research. Taking the reporting practice of overseas Chinese media during the COVID-19 pandemic as the research object and the new media platform “oushi 1983” of Nouvelles D'Europe as the study object, this paper captures the reports published between January 1, 2020, and December 31, 2021, as samples, and conducts big data analysis on the content based on relevant clustering algorithms and text analysis tools. Through iterative computations of the K-means algorithm, 1661 pieces of reports related to the COVID-19 pandemic are clustered and analyzed, so as to make an empirical study on the content theme and emotional bias of the report. By doing so, the reporting pattern and communication function of overseas Chinese media in the international public opinion field is investigated.
- Copyright
- © 2023 The Author(s)
- Open Access
- Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.
Cite this article
TY - CONF AU - Nan Dai AU - Yujie Guo AU - Miao Wang PY - 2022 DA - 2022/12/27 TI - Pandemic-Related Media Coverage in Overseas Chinese Media Based on Big Data Analysis: A Case Study of “oushi 1983” BT - Proceedings of the 2022 3rd International Conference on Big Data and Social Sciences (ICBDSS 2022) PB - Atlantis Press SP - 161 EP - 166 SN - 2589-4900 UR - https://doi.org/10.2991/978-94-6463-064-0_18 DO - 10.2991/978-94-6463-064-0_18 ID - Dai2022 ER -