Topic Characteristics of Large-Scale Online Public Opinion Based on Coword Networks and Event-Driven Methods

Guoqing Liu; Weihong Li

doi:doi:10.11648/j.ss.20221103.13

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Topic Characteristics of Large-Scale Online Public Opinion Based on Coword Networks and Event-Driven Methods

Guoqing Liu, Weihong Li

Published in Social Sciences (Volume 11, Issue 3)

Received: 6 May 2022 Accepted: 19 May 2022 Published: 31 May 2022

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Abstract

In June 2019, Anti-Extradition Law Amendment Bill (Anti-ELAB) Movement occurred in Hong Kong. The movement generated a huge impact on online public opinion. This online public opinion lasts for a long time and has a wide range of influence, which is often called large-scale online public opinion. There is a lack of research, as well as a limited research perspective, on large-scale online public opinion. In order to study this kind of large-scale online public opinion. Therefore, starting from the topic perspective, this study investigated topic evolution and spatiotemporal characteristics using coword networks and event-driven methods. The proposed methods were applied to a case study based on the corpus related to the Anti-ELAB Movement on Sina Weibo. The results revealed public opinion hotness trends and their influencing factors, as well as the topic content, evolution characteristics, and spatiotemporal characteristics of the three evolution stages of the Anti-ELAB Movement. They also revealed the guiding role of events in topic content and evolution and discovered the clustering characteristics of the topic’s spatiotemporal hotspots. In the whole process of large-scale online public opinion, the content of online public opinion changes according to the secondary events, and the space-time hot topics are also related to the events.

Published in	Social Sciences (Volume 11, Issue 3)
DOI	10.11648/j.ss.20221103.13
Page(s)	144-152
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2022. Published by Science Publishing Group

Keywords

Topic Evolution, Coword Network, Spatiotemporal Hotspots, Large-Scale Online Public Opinion, ITF/PDF (Integrated Term Frequency/Proportional Document Frequency)

References

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[16]	Tan, X., Zhuang, M., Mao, T., & Zhang, Q. (2020). Analysis of Emotional Evolution of Large-scale Internet Public Opinion Events Based on LDA-ARAM Hybrid Model. Journal Of Intelligence, 1-10. Retrieved from http://kns.cnki.net/kcms/detail/61.1167.G3.20200722.1124.022.html

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Guoqing Liu, Weihong Li. (2022). Topic Characteristics of Large-Scale Online Public Opinion Based on Coword Networks and Event-Driven Methods. Social Sciences, 11(3), 144-152. https://doi.org/10.11648/j.ss.20221103.13

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ACS Style

Guoqing Liu; Weihong Li. Topic Characteristics of Large-Scale Online Public Opinion Based on Coword Networks and Event-Driven Methods. Soc. Sci. 2022, 11(3), 144-152. doi: 10.11648/j.ss.20221103.13

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AMA Style

Guoqing Liu, Weihong Li. Topic Characteristics of Large-Scale Online Public Opinion Based on Coword Networks and Event-Driven Methods. Soc Sci. 2022;11(3):144-152. doi: 10.11648/j.ss.20221103.13

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@article{10.11648/j.ss.20221103.13,
  author = {Guoqing Liu and Weihong Li},
  title = {Topic Characteristics of Large-Scale Online Public Opinion Based on Coword Networks and Event-Driven Methods},
  journal = {Social Sciences},
  volume = {11},
  number = {3},
  pages = {144-152},
  doi = {10.11648/j.ss.20221103.13},
  url = {https://doi.org/10.11648/j.ss.20221103.13},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ss.20221103.13},
  abstract = {In June 2019, Anti-Extradition Law Amendment Bill (Anti-ELAB) Movement occurred in Hong Kong. The movement generated a huge impact on online public opinion. This online public opinion lasts for a long time and has a wide range of influence, which is often called large-scale online public opinion. There is a lack of research, as well as a limited research perspective, on large-scale online public opinion. In order to study this kind of large-scale online public opinion. Therefore, starting from the topic perspective, this study investigated topic evolution and spatiotemporal characteristics using coword networks and event-driven methods. The proposed methods were applied to a case study based on the corpus related to the Anti-ELAB Movement on Sina Weibo. The results revealed public opinion hotness trends and their influencing factors, as well as the topic content, evolution characteristics, and spatiotemporal characteristics of the three evolution stages of the Anti-ELAB Movement. They also revealed the guiding role of events in topic content and evolution and discovered the clustering characteristics of the topic’s spatiotemporal hotspots. In the whole process of large-scale online public opinion, the content of online public opinion changes according to the secondary events, and the space-time hot topics are also related to the events.},
 year = {2022}
}

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TY  - JOUR
T1  - Topic Characteristics of Large-Scale Online Public Opinion Based on Coword Networks and Event-Driven Methods
AU  - Guoqing Liu
AU  - Weihong Li
Y1  - 2022/05/31
PY  - 2022
N1  - https://doi.org/10.11648/j.ss.20221103.13
DO  - 10.11648/j.ss.20221103.13
T2  - Social Sciences
JF  - Social Sciences
JO  - Social Sciences
SP  - 144
EP  - 152
PB  - Science Publishing Group
SN  - 2326-988X
UR  - https://doi.org/10.11648/j.ss.20221103.13
AB  - In June 2019, Anti-Extradition Law Amendment Bill (Anti-ELAB) Movement occurred in Hong Kong. The movement generated a huge impact on online public opinion. This online public opinion lasts for a long time and has a wide range of influence, which is often called large-scale online public opinion. There is a lack of research, as well as a limited research perspective, on large-scale online public opinion. In order to study this kind of large-scale online public opinion. Therefore, starting from the topic perspective, this study investigated topic evolution and spatiotemporal characteristics using coword networks and event-driven methods. The proposed methods were applied to a case study based on the corpus related to the Anti-ELAB Movement on Sina Weibo. The results revealed public opinion hotness trends and their influencing factors, as well as the topic content, evolution characteristics, and spatiotemporal characteristics of the three evolution stages of the Anti-ELAB Movement. They also revealed the guiding role of events in topic content and evolution and discovered the clustering characteristics of the topic’s spatiotemporal hotspots. In the whole process of large-scale online public opinion, the content of online public opinion changes according to the secondary events, and the space-time hot topics are also related to the events.
VL  - 11
IS  - 3
ER  -

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Author Information

Guoqing Liu

School of Geography, South China Normal University, Guangzhou, China
Weihong Li

School of Geography, South China Normal University, Guangzhou, China

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APA Style

Guoqing Liu, Weihong Li. (2022). Topic Characteristics of Large-Scale Online Public Opinion Based on Coword Networks and Event-Driven Methods. Social Sciences, 11(3), 144-152. https://doi.org/10.11648/j.ss.20221103.13

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ACS Style

Guoqing Liu; Weihong Li. Topic Characteristics of Large-Scale Online Public Opinion Based on Coword Networks and Event-Driven Methods. Soc. Sci. 2022, 11(3), 144-152. doi: 10.11648/j.ss.20221103.13

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AMA Style

Guoqing Liu, Weihong Li. Topic Characteristics of Large-Scale Online Public Opinion Based on Coword Networks and Event-Driven Methods. Soc Sci. 2022;11(3):144-152. doi: 10.11648/j.ss.20221103.13

Copy | Download

@article{10.11648/j.ss.20221103.13,
  author = {Guoqing Liu and Weihong Li},
  title = {Topic Characteristics of Large-Scale Online Public Opinion Based on Coword Networks and Event-Driven Methods},
  journal = {Social Sciences},
  volume = {11},
  number = {3},
  pages = {144-152},
  doi = {10.11648/j.ss.20221103.13},
  url = {https://doi.org/10.11648/j.ss.20221103.13},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ss.20221103.13},
  abstract = {In June 2019, Anti-Extradition Law Amendment Bill (Anti-ELAB) Movement occurred in Hong Kong. The movement generated a huge impact on online public opinion. This online public opinion lasts for a long time and has a wide range of influence, which is often called large-scale online public opinion. There is a lack of research, as well as a limited research perspective, on large-scale online public opinion. In order to study this kind of large-scale online public opinion. Therefore, starting from the topic perspective, this study investigated topic evolution and spatiotemporal characteristics using coword networks and event-driven methods. The proposed methods were applied to a case study based on the corpus related to the Anti-ELAB Movement on Sina Weibo. The results revealed public opinion hotness trends and their influencing factors, as well as the topic content, evolution characteristics, and spatiotemporal characteristics of the three evolution stages of the Anti-ELAB Movement. They also revealed the guiding role of events in topic content and evolution and discovered the clustering characteristics of the topic’s spatiotemporal hotspots. In the whole process of large-scale online public opinion, the content of online public opinion changes according to the secondary events, and the space-time hot topics are also related to the events.},
 year = {2022}
}

Copy | Download

TY  - JOUR
T1  - Topic Characteristics of Large-Scale Online Public Opinion Based on Coword Networks and Event-Driven Methods
AU  - Guoqing Liu
AU  - Weihong Li
Y1  - 2022/05/31
PY  - 2022
N1  - https://doi.org/10.11648/j.ss.20221103.13
DO  - 10.11648/j.ss.20221103.13
T2  - Social Sciences
JF  - Social Sciences
JO  - Social Sciences
SP  - 144
EP  - 152
PB  - Science Publishing Group
SN  - 2326-988X
UR  - https://doi.org/10.11648/j.ss.20221103.13
AB  - In June 2019, Anti-Extradition Law Amendment Bill (Anti-ELAB) Movement occurred in Hong Kong. The movement generated a huge impact on online public opinion. This online public opinion lasts for a long time and has a wide range of influence, which is often called large-scale online public opinion. There is a lack of research, as well as a limited research perspective, on large-scale online public opinion. In order to study this kind of large-scale online public opinion. Therefore, starting from the topic perspective, this study investigated topic evolution and spatiotemporal characteristics using coword networks and event-driven methods. The proposed methods were applied to a case study based on the corpus related to the Anti-ELAB Movement on Sina Weibo. The results revealed public opinion hotness trends and their influencing factors, as well as the topic content, evolution characteristics, and spatiotemporal characteristics of the three evolution stages of the Anti-ELAB Movement. They also revealed the guiding role of events in topic content and evolution and discovered the clustering characteristics of the topic’s spatiotemporal hotspots. In the whole process of large-scale online public opinion, the content of online public opinion changes according to the secondary events, and the space-time hot topics are also related to the events.
VL  - 11
IS  - 3
ER  -

Copy | Download