publications

2024

1. 

   How effective is the Shorts Transformation of Traditional Media? An analysis from the perspective of user-generated content

   Pu Zhang, and Corey Kewei XU

   2024

   ChineseCSCW 2024, In Press

   Abs

   The digital transformation of traditional media outlets has become a critical imper-ative in the evolving media landscape. As traditional media grapple with the chal-lenges of adapting to the rise of digital and social media platforms, understanding the factors that shape their performance and user engagement on these emerging channels is of paramount importance. This study examines the comparative per-formance of a traditional media account (Hubei Daily) and a specialized new me-dia account (Cover News) on Douyin platform, with a focus on the distribution of emotional sentiment and thematic diversity in user-generated content. The analysis reveals that while the traditional media account exhibited a more positive sentiment in its user comments, the overall sentiment across both accounts tended to be negative. This discrepancy suggests potential challenges in the traditional media’s ability to fully resonate with the audience and shift the sentiment in a more positive direction on the shorts platform. Furthermore, the topic modeling analysis indicates that the new media account was able to capture a wider range of discussion topics among its users, in contrast to the more concentrated thematic focus of the traditional media account. This finding underscores the need for tra-ditional media to better understand and adapt to the content preferences and en-gagement patterns of shorts platform users as part of their digital transformation efforts. The study highlights the complexities involved in traditional media’s tran-sition to emerging media platforms, emphasizing the importance of developing appropriate content strategies, fostering meaningful user engagement, and align-ing organizational structures and capabilities with the demands of the digital landscape.

2. 

   Study on the Evolution of Online Public Opinion and Government Response Strategies for the “7-20” extraordinary rainstorm and flooding disaster in Zhengzhou, China

   Pu Zhang, Hao Zhang, and Feng Kong

   Natural Hazards, 2024

   Abs Bib DOI PDF

   Disaster-related online public opinion develops rapidly, the actual situation is complex and volatile, and the public opinion environment should be regulated through appropriate guidance. 2021 Zhengzhou 7-20 extraordinary rainstorm and flooding disaster has attracted widespread public attention. In order to analyze the online public opinion triggered by extraordinary rainstorm disasters and propose targeted management measures so as to improve the comprehensive disaster reduction efficiency. This paper collected information about the "Zhengzhou rainstorm" posted on the Sina Weibo platform from July 11 to August 14, 2021. We analyzed the characteristics of related Weibo from two dimensions: sentiment analysis and thematic analysis. The results are as follows: Online public opinion will be generated rapidly and last for a long time; the different emotional colors change at different periods of the disaster; the focus of online public opinion discussion varies at different periods of the disaster. Given this result, the following suggestions are made: the cooperation level between departments should be improved, and an early warning mechanism for public opinion should be established so that once the relevant public opinion is generated, a quick response can be made; a relevant responsibility mechanism should be established to realize that a specific department is responsible for the handling of public opinion in the corresponding section, to realize a scientific and practical normalized control mechanism for public opinion; Relevant departments should improve the openness and transparency of appropriate handling methods, to establish a public opinion control and guidance mechanism. This study has specific significance for improving the level of governance of online public opinion caused by sudden natural disasters.

   @article{PZHANG03,
     author = {Zhang, Pu and Zhang, Hao and Kong, Feng},
     title = {Study on the Evolution of Online Public Opinion and Government Response Strategies for the “7-20” extraordinary rainstorm and flooding disaster in Zhengzhou, China},
     year = {2024},
     url = {https://link.springer.com/article/10.1007/s11069-024-06904-7},
     journal = {Natural Hazards},
     volume = {TBC},
     pages = {TBC},
     doi = {10.1007/s11069-024-06904-7},
     dimensions = {true},
     issn = {1573-0840},
     keywords = {Disaster public opinion, Major natural disasters, Zhengzhou rainstorm, Emergency management, Natural language processing}
   }

3. 

   Research on online public opinion in the investigation of the “7–20” extraordinary rainstorm and flooding disaster in Zhengzhou, China

   Pu Zhang, Hao Zhang, and Feng Kong

   International Journal of Disaster Risk Reduction, 2024

   Abs Bib DOI PDF

   Sorting out the changing characteristics of online public opinion triggered by a series of events in the investigation and assessment of major natural disasters is of great practical significance for optimizing the work of disaster investigation and assessment, governing the ecology of online public opinion, and enhancing the effect of comprehensive disaster reduction. In this paper, we collected relevant comments from several official media accounts, such as People’s Daily, and evaluated their emotional color using a sentiment analysis method based on the BERT fine-tuning model. Furthermore, keyword co-occurrence semantic network theme analysis is conducted for texts presenting negative emotional overtones to assess the changes in public opinion hotspots. The impact of the relevant online public opinion characteristics and the release of the disaster investigation report on them was assessed in the context of the investigation report itself. Based on sorting out the characteristics of online public opinion on several related topics, targeted public opinion governance initiatives and relevant suggestions for improving the disaster investigation system are proposed. This paper is of positive significance for studying disaster public opinion and improving the effectiveness of emergency management of rainstorms and flooding disasters.

   @article{PZHANG02,
     title = {Research on online public opinion in the investigation of the “7–20” extraordinary rainstorm and flooding disaster in Zhengzhou, China},
     journal = {International Journal of Disaster Risk Reduction},
     volume = {105},
     pages = {104422},
     year = {2024},
     issn = {2212-4209},
     dimensions = {true},
     url = {https://www.sciencedirect.com/science/article/abs/pii/S2212420924001845},
     doi = {10.1016/j.ijdrr.2024.104422},
     author = {Zhang, Pu and Zhang, Hao and Kong, Feng},
     keywords = {Rainstorm and flooding disaster, Disaster investigation report, Disaster opinion research, Integrated disaster mitigation, Natural language processing},
   }

2023

1. 

   基于微博数据的暴雨洪涝灾害舆情特征研究：以2021年中国三场暴雨洪涝为例

   张谱,  张豪,  孔锋, and 1 more author

   水利水电技术(中英文), 2023

   Abs Bib DOI PDF

   [Objective] The governance of public opinion related to natural disasters is an important element of comprehensive disaster reduction. In order to explore the characteristics of network public opinion triggered by extraordinary rainstorm and flooding disasters, and summarize the experience of relevant public opinion response, [Methods] the three extraordinary rainstorm and flooding disasters that occurred in 2021 were taken as examples and Weibo data related to the topics of “Shanxi rainstorm”, “Henan rainstorm” and “Hubei rainstorm” posted on the Sina Weibo platform were collected. A machine learning method based on the BERT fine-tuning model was used for sentiment analysis on time series to sort out the changes in the hotness and sentiment intensity of disaster public opinion issues; and a knowledge graph method with keyword co-occurrence semantic network as the core explored the characteristics of changes in public opinion hotspots for different disasters. [Results] The results show that(1) public opinion on the same type of rainstorm and flooding disasters usually has similar chronological characteristics, it usually lasts for a long time and the peaks of public opinion are associated with critical events.(2) The emotional color distribution of public opinion on the same type of rainstorm and flooding disasters is similar but will change accordingly depending on the severity of the disaster. The percentage of positive emotion in the Shanxi rainstorm in this study is 36.83% and 28.81% of negative emotion; in the Henan rainstorm, it is 34.23% and 43.25%; in the Hubei rainstorm, it is 45.91% and 27.07%.(3) Public opinion on similar rainstorm and flooding disasters has similar thematic distribution, which can be divided into concerns about the disaster-causing factors and the disaster itself, concerns about the disaster-causing process and emergency rescue and relief, and concerns about the socioeconomic and ecological impacts of the floods. [Conclusion] The evolutionary characteristics of online public opinion triggered by rainstorm and flooding disasters are roughly similar but show some differences due to the difference in the severity of the disasters. In response to this characteristic, the following recommendations are made:(1) daily public opinion control measures and timely response to sudden disasters should be done well;(2) the timely release of official disaster information and reasonably channel and regulate public opinion should be taken seriously;(3) a public opinion control linkage mechanism should be established and excellent management experiences should be learned. The research results are of positive significance to study and judge the changing trends of disaster public opinion and improve the disaster public opinion governance system and capacity.

   @article{SJWJ202302005,
     author = {张谱 and 张豪 and 孔锋 and 孔赟珑},
     title = {基于微博数据的暴雨洪涝灾害舆情特征研究：以2021年中国三场暴雨洪涝为例},
     journal = {水利水电技术(中英文)},
     volume = {54},
     pages = {47-59},
     year = {2023},
     issn = {1000-0860},
     doi = {10.13928/j.cnki.wrahe.2023.02.005}
   }