Available via license: CC BY-NC 4.0
Content may be subject to copyright.
Open Journal of Social Sciences, 2023, 11, 397-403
https://www.scirp.org/journal/jss
ISSN Online: 2327-5960
ISSN Print: 2327-5952
DOI:
10.4236/jss.2023.115025 May 30, 2023 397 Open
Journal of Social Sciences
Analysis of the 7.20 Zhengzhou Rainstorm
Based on Bilibili Video Data
Xiyue Cui
Zhengzhou Foreign Language Middle School, Zhengzhou, China
Abstract
The 7.20 Zhengzhou rainstorm was a serious rainstorm disaster event that
caused severe flooding and significant damage to
infrastructure, homes, and
vehicles, and issues such as the event itself and the emergency response of re-
lated departments have triggered continuous and extensive discussions. In
this study, we collected live videos from the incident period of Bilibili, an
d
analyzed the video data using text analysis and social network analysis to ex-
plore the reflections, concerns and needs of people in the situation of major
natural disasters. In recent years, various natural disasters have occurred fre-
quently, and recreating the scenes and analyzing people’
s demands from the
perspective of self-
media is of practical significance to remind people of the
lessons learned and improve urban planning strategies.
Keywords
Bilibili Video, Zhengzhou Rainstorm, Event Analysis
1. Introduction
According to official reports, from July 17th to July 23rd, 2021, Henan Province
experienced an extremely rare and severe rainstorm that resulted in serious
flooding and disasters, especially on July 20th, when Zhengzhou City suffered
significant human casualties and property losses. The disaster affected a total of
150 counties (cities, districts) in Henan Province, with 14.786 million people af-
fected. The disaster caused 398 deaths or missing persons, including 380 in
Zhengzhou City, accounting for 95.5% of the entire province. The direct eco-
nomic losses amounted to 120.06 billion yuan (approximately 18.6 billion US
dollars as the exchange rate in July, 2021), of which 40.9 billion yuan was in
Zhengzhou City, accounting for 34.1% of the entire province (Yu, 2022). Short-
comings such as poorly organized prevention and improper emergency response
How to cite this paper:
Cui, X. Y. (2023).
Analysis of the 7.20 Zhengzhou Rainstorm
Based on Bilibili Video Data
.
Open Journal
of Social Sciences
, 11,
397-403.
https://doi.org/10.4236/jss.2023.115025
Received:
March 28, 2023
Accepted:
May 27, 2023
Published:
May 30, 2023
Copyright © 20
23 by author(s) and
Scientific Research Publishing Inc.
This work is licensed under the Creative
Commons Attribution
-NonCommercial
International License (
CC BY-NC 4.0).
http://creativecommons.org/licenses/by
-nc/4.0/
Open Access
X. Y. Cui
DOI:
10.4236/jss.2023.115025 398 Open
Journal of Social Sciences
deserve deep reflection. A large number of scholars have reflected on this, search-
ing the China National Knowledge Infrastructure and using the search formula
TKA = (“urban governance” + “social governance” + “emergency management”
+ smart city “ + “resilient city” + “early warning” + “emotional communica-
tion”) was conducted, and a total of 2765 papers related papers were found,
which shows that a large number of scholars have analyzed the event. However,
the scholars’ reflections basically analyze the event from the restoration of the
event in the news reports as the data base, and have not yet seen the analysis
from the people’s reaction at the scene when the event occurred. In contrast, the
video of the disaster scene on Bilibili, which was uploaded by the people sponta-
neously, objectively reflects the real scene and needs at that time, and if from this
perspective, different findings may be obtained. This paper is based on such an
idea of collecting data from Bilibili and analyzing the videos of the event, in or-
der to get some new discoveries.
2. Data Acquisition
2.1. Data Collection and Research Methods
In this study, Bilibili was selected as the data sample for several reasons.
1) Bilibili has a large and active user community in China. In the fourth quar-
ter of 2022, Bilibili’s daily active users reached 92.8 million, and the monthly ac-
tive users increased to 326 million (Financial Report Assistant, 2023). This means
that the videos uploaded to the platform are likely to be reflective of the perspec-
tives and experiences of a significant segment of the population affected by the
Zhengzhou rainstorm.
2) Bilibili is known for its rich and diverse intellectual content, which includes
not only entertainment but also educational and informative videos. This sug-
gests that the videos uploaded to the platform may contain a wealth of informa-
tion and insights that could be valuable in analyzing the crux of the disaster.
3) Bilibili has attracted numerous official accounts of large newspapers and
news media, such as Xinhua News Agency. This suggests that Bilibili is seen as a
reputable and trustworthy platform by many official sources, which could en-
hance the credibility of the data collected.
Overall, Bilibili’s large and active user community, diverse intellectual content,
and reputation for reliability make it an ideal data source for analyzing the crux
of the Zhengzhou rainstorm.
Octopus Data Collector was used for data collection, establishing the collec-
tion process, cyclically extracting data, then editing fields, and starting the col-
lection to automatically generate an Excel data file, and a total of 340 data were
collected from July 20 to August 20, 2021 by this method.
2.2. Research Methodology
In this study, textual analysis and social network analysis were mainly used to
analyze the data. Textual Analysis is the representation of text and the selection
X. Y. Cui
DOI:
10.4236/jss.2023.115025 399
Open Journal of Social Sciences
of its feature terms; textual analysis is a fundamental problem in text mining and
information retrieval, which quantifies feature words extracted from text to
represent textual information (Jiang, 2023). In this study, text analysis was used
to analyze the title text in the collected videos as a way to discover what people
were concerned about before the major floods. The Social Network Analysis
(SNA) method, also known as Structural Analysis, is used to analyze the rela-
tional structure of social networks and their attributes. The significance of SNA
is that it can provide a precise quantitative analysis of relationships, which can
provide a quantitative tool for the construction of certain middle-level theories
and the testing of empirical propositions, and can even build a bridge between
“macro and micro” (Gao & Yang, 2017). In this study, we use social network
analysis to analyze the co-textual content of the split video and finally, to identi-
fy the content of people’s live expressions.
3. Data Analysis
3.1. Descriptive Statistical Analysis of Video Samples
In this study, we collected live videos from Bilibili during the incident period of
the 7.20 Zhengzhou rainstorm. To ensure the reliability and representativeness
of the data, we established specific criteria for selecting videos. Firstly, we only
included videos that were geotagged in Zhengzhou to ensure that they were
filmed at the scene of the disaster. Second, we excluded videos with low video
quality or those that contained irrelevant or inappropriate content. By following
these criteria, the data of 340 videos that met the standards for our analysis of
720 Zhengzhou rainstorm posted on Billibili were collected, and the collection
included total views, bullet comments, coins, likes and shares in a total of five
fields. The number of views represents the exposure and attention of the video
or event, but does not reflect the recognition of users. In contrast, the interactive
indicators of likes, comments, shares and coins are more realistic and have rich-
er connotations. For example, likes represent users’ affirmation and support for
video content, coins represent users’ incentive for video makers to continue creat-
ing relevant content, bullet comments represent the discussion heat of events,
and shares represent users’ desire for video content to be spread. For this pur-
pose, the number of views, bullet comments, coins given by users and likes are
selected for statistical analysis of the collected videos.
As can be seen from Table 1, the number of three indicators, total number
of bullet comments, coins and shares, is relatively low. Videos with strong
Table 1. Number of various types of evaluations of Bilibili videos of the 720 Zhengzhou rainstorm incident.
Indicators
Number of views
Number of bullet
comments
Number of coins given
by users
Number of
Likes
Number of shares
Total volume
4867.39 million
117,700
542,600
284.19 million
156,500
Average value
143,600
0.03 million
0.16 million
0.84 million
0.05 million
X. Y. Cui
DOI:
10.4236/jss.2023.115025 400 Open
Journal of Social Sciences
entertainment and interaction are more likely to get the number of bullet com-
ments and coins, and videos with skills and opinions are more likely to get more
shares; therefore, the number of these three indicators for disaster videos is less,
which is more in line with the law of self-media.
The total number of views and the number of likes are the important indica-
tors in this case. The distribution of these two indicators is further analyzed.
After sorting the videos by their play volume from high to low, we found that
the the number of views of the top 63 videos accounted for 80% of the total play
volume, i.e. 16% of the videos contributed 80% of the play volume; similarly, af-
ter sorting the videos by the number of likes from high to low, we found that the
number of likes of the top 34 videos accounted for 80% of the total likes, i.e. 10%
of the videos contributed 80% of the likes. This shows that, firstly, both the
number of views and the number of likes follow the two-eight law, that is, a large
number of plays and likes come from a small number of videos; secondly, the
number of likes has a higher concentration than the number of views.
Comparing the titles of the top 63 videos in terms of views and the top 33
videos in terms of likes, we find that 30 of the top 33 videos in terms of likes are
also among the top 63 videos in terms of views. This means that the number of
likes is a more reflective indicator of the popularity of the videos.
Further comparison of the content of the 30 videos selected for both impor-
tant indicators includes the following types: 1) videos with vivid descriptions of
scenes, such as “A picture tells you how heavy the rain is in Zhengzhou!” 2) in-
structional videos, such as “Zhengzhou rainstorm relief donation anti-fraud
guide” 3) videos that inspire courage to fight against disasters, such as “the child-
ren playing instruments at Zhengzhou East Station during the rainstorm!” 4)
shocking factual videos of “the Zhengzhou rainstorm, with a 3-hour hike along
the way”.
3.2. Video Content Analysis
1) Text analysis
Text analysis and social network analysis were used to analyze the content of
340 videos related to the Zhengzhou rainstorm. Firstly, we imported the titles of
the video data using Collective Search, and performed data cleaning—removing
basic or irrelevant information such as “Henan”, “Zhengzhou”, “put away “ and
other basic or irrelevant information was removed to highlight the focus of the
visualization and avoid interference. Next, the words were screened, and words
with videos greater than 3 were selected to draw a high-frequency word cloud
map (as shown in Figure 1). By means of the word cloud, all the video text data
are transformed into a collection of words, thus simply showing the frequency
and relative importance of the words appearing in the text data.
Figure 1 clearly shows that the words “subway,” “citizens,” “hours,” “water-
logged,” The words “refueling” and “rescue” are repeatedly mentioned by people.
It can be inferred from this that a) the severity of the accident and the cause of
X. Y. Cui
DOI:
10.4236/jss.2023.115025 401
Open Journal of Social Sciences
Figure 1. 720 event b station video title high frequency words word cloud map.
the No. 5 subway tragedy that occurred during the 7.20 rainstorm in Zhengzhou
caused a great deal of controversy and reflection among the general public. b)
The waterlogging has caused a general bad impact on the lives of the public. c)
The importance and urgency of time in the face of natural disasters. d) In the
face of such a serious natural disaster, the video platform also played an active
role in spreading positive energy and updating rescue messages in a timely man-
ner.
To further semantic analysis of the video content, co-word matching was per-
formed on the above data, i.e., high-frequency words appearing in the same sen-
tence were identified to obtain a co-word matrix—the public key words were
statistically ranked and downloaded to be kept for further analysis.
2) Common word analysis
The co-word matrix obtained from the set of searchers is analyzed by using
the social network analysis software Ucinet for co-word analysis, and the corre-
lation between words is displayed graphically, so that the content relationships
in the text data can be grasped more clearly.
Start the visualization software Netdraw embedded in Ucinet, import the
“7.20 co-word analysis ##h”, and draw the co-word relationship network; for the
convenience of analysis, only words with co-word relationship greater than 1 are
selected. The following relationship network diagram was obtained (see Figure
2).
From Figure 2, we can see that the words “subway”, “waterlogging”, “mega”
and “sudden change” are associated with are numerous. Among them, “subway”
and “rescue”, “hour”, “cause” and “killed” are closely related. The close connec-
tion between “subway” and “rescue”, “hours”, “caused” and “killed” is sufficient
to prove the seriousness and urgency of this incident, as well as the public
mourning and reflection on it. The words “waterlogged” and “citizens” and
“roads” are also clearly linked, highlighting people’s concern about the real-time
X. Y. Cui
DOI:
10.4236/jss.2023.115025 402 Open
Journal of Social Sciences
Figure 2. High-frequency word co-word relationship network of 720 event b-site video titles.
traffic situation and reflecting the seriousness of traffic disruptions in natural
disasters; The words “mega” and “sudden change” describe the unusual nature
of the rainstorm and the sensation it caused.
Through textual analysis and social network analysis, the following contents
were found to be of great interest: the No. 5 subway incident, the travel of citi-
zens and the rescue and storm conditions, etc. These contents reflect the public’s
concern about the consequences of the rainstorm and the current situation of
the local area, and also show the society’s concern and attitude towards the dis-
aster. These data help us to better understand the community’s concern about
the rainstorm event and its evaluation of the relief measures provided by the lo-
cal government and social organizations.
4. Conclusion
The results of this study from the analysis of live videos from Bilibili videos
found that:
1) Social media platforms have a positive effect on natural disaster communi-
cation, which can help deliver positive energy and rescue information, as well as
trigger public attention and participation.
2) After a natural disaster, people have great controversy and reflection on the
seriousness of the accident and the causes caused. Discussions and comments on
social media platforms can reflect the public’s attitude and emotion toward nat-
ural disasters.
3) Natural disasters can have a generally bad impact on citizens’ lives and tra-
X. Y. Cui
DOI:
10.4236/jss.2023.115025 403
Open Journal of Social Sciences
vel, so more attention needs to be paid to disaster prevention and mitigation and
to improving the ability to cope with natural disasters. This is a different conclu-
sion compared with other scholars.
Through our analysis, we believe that major disasters require attention to the
following points:
1) Timely and accurate media coverage is crucial to disaster response. The gov-
ernment and the media need to strengthen cooperation to release disaster in-
formation and rescue progress in a timely manner, and to deliver accurate and
timely information to the public so that people can better cope with disasters.
2) In major disasters, the transmission of positive energy is crucial to the psy-
chological health of disaster victims and social stability. The government and all
parties in society need to strengthen propaganda to guide people to be positive
and brave in facing disasters, as well as to strengthen psychological guidance and
help for disaster victims.
3) The improvement of individual disaster prevention awareness and self-
protection ability is also crucial. When a disaster occurs, the level of personal
disaster prevention awareness and self-protection ability will directly affect the
life safety of individuals in a disaster. Therefore, the government and all parties
in society need to strengthen the publicity and training of disaster response, im-
prove people’s awareness of disaster prevention and self-protection ability, and
reduce the damage and loss caused by disasters to individuals.
Conflicts of Interest
The author declares no conflicts of interest regarding the publication of this pa-
per.
References
Financial Report Assistant (2023).
Bilibili Releases 2022 Q4 and Annual Financial Report:
The Annual Revenue Is 21.9 Billion Yuan, and the Daily Active Users in Q4 Increase by
29% YoY to 92.8 Million
. Bilibili.
Gao, X. R., & Yang, N. (2017). The Construction of Thesis Evaluation Index System Based
on Social Network Analysis Method.
Intelligence Science, 35,
97-102+144.
Jiang, W. (2023).
Text Analysis and Text Mining
. Science Press Publishing House.
Yu, S. (2022).
Report on Investigation of the Severe Rainstorm Disaster in Zhengzhou on
July 20th Released
. Xinhua News Agency.