Using Social Media to Mine and Analyze Public Sentiment during a Disaster: A Case Study of the 2018 Shouguang City Flood in China

Social media has been applied to all natural disaster risk-reduction phases, including pre-warning, response, and recovery. However, using it to accurately acquire and reveal public sentiment during a disaster still presents a significant challenge. To explore public sentiment in depth during a disaster, this study analyzed Sina-Weibo (Weibo) texts in terms of space, time, and content related to the 2018 Shouguang flood, which caused casualties and economic losses, arousing widespread public concern in China. The temporal changes within six-hour intervals and spatial distribution on sub-district and city levels of flood-related Weibo were analyzed. Based on the Latent Dirichlet Allocation (LDA) model and the Random Forest (RF) algorithm, a topic extraction and classification model was built to hierarchically identify six flood-relevant topics and nine types of public sentiment responses in Weibo texts. The majority of Weibo texts about the Shouguang flood were related to “public sentiment”, among which “questioning the government and media” was the most commonly expressed. The Weibo text numbers varied over time for different topics and sentiments that corresponded to the different developmental stages of the flood. On a sub-district level, the spatial distribution of flood-relevant Weibo was mainly concentrated in high population areas in the south-central and eastern parts of Shouguang, near the river and the downtown area. At the city level, the Weibo texts were mainly distributed in Beijing and cities in the Shandong Province, centering in Weifang City. The results indicated that the classification model developed in this study was accurate and viable for analyzing social media texts during a disaster. The findings can be used to help researchers, public servants, and officials to better understand public sentiments towards disaster events, to accelerate disaster responses, and to support post-disaster management.

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Using Social Media to Mine and Analyze Public Opinion Related to COVID-19 in China

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph17082788 ◽

2020 ◽

Vol 17 (8) ◽

pp. 2788 ◽

Cited By ~ 25

Author(s):

Xuehua Han ◽

Juanle Wang ◽

Min Zhang ◽

Xiaojie Wang

Keyword(s):

Social Media ◽

Spatial Distribution ◽

Public Opinion ◽

Large Population ◽

River Delta ◽

Classification Model ◽

Allocation Model ◽

Urban Agglomerations ◽

The Public ◽

The Government

The outbreak of Corona Virus Disease 2019 (COVID-19) is a grave global public health emergency. Nowadays, social media has become the main channel through which the public can obtain information and express their opinions and feelings. This study explored public opinion in the early stages of COVID-19 in China by analyzing Sina-Weibo (a Twitter-like microblogging system in China) texts in terms of space, time, and content. Temporal changes within one-hour intervals and the spatial distribution of COVID-19-related Weibo texts were analyzed. Based on the latent Dirichlet allocation model and the random forest algorithm, a topic extraction and classification model was developed to hierarchically identify seven COVID-19-relevant topics and 13 sub-topics from Weibo texts. The results indicate that the number of Weibo texts varied over time for different topics and sub-topics corresponding with the different developmental stages of the event. The spatial distribution of COVID-19-relevant Weibo was mainly concentrated in Wuhan, Beijing-Tianjin-Hebei, the Yangtze River Delta, the Pearl River Delta, and the Chengdu-Chongqing urban agglomeration. There is a synchronization between frequent daily discussions on Weibo and the trend of the COVID-19 outbreak in the real world. Public response is very sensitive to the epidemic and significant social events, especially in urban agglomerations with convenient transportation and a large population. The timely dissemination and updating of epidemic-related information and the popularization of such information by the government can contribute to stabilizing public sentiments. However, the surge of public demand and the hysteresis of social support demonstrated that the allocation of medical resources was under enormous pressure in the early stage of the epidemic. It is suggested that the government should strengthen the response in terms of public opinion and epidemic prevention and exert control in key epidemic areas, urban agglomerations, and transboundary areas at the province level. In controlling the crisis, accurate response countermeasures should be formulated following public help demands. The findings can help government and emergency agencies to better understand the public opinion and sentiments towards COVID-19, to accelerate emergency responses, and to support post-disaster management.

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Extracting Typhoon Disaster Information from VGI Based on Machine Learning

Journal of Marine Science and Engineering ◽

10.3390/jmse7090318 ◽

2019 ◽

Vol 7 (9) ◽

pp. 318 ◽

Cited By ~ 3

Author(s):

Yu ◽

Zhao ◽

Chin

Keyword(s):

Social Media ◽

Deep Learning ◽

Classification System ◽

High Reliability ◽

Economic Losses ◽

Application Range ◽

Disaster Information ◽

Disaster Situation ◽

Typhoon Disaster ◽

The Government

The southeastern coast of China suffers many typhoon disasters every year, causing huge casualties and economic losses. In addition, collecting statistics on typhoon disaster situations is hard work for the government. At the same time, near-real-time disaster-related information can be obtained on developed social media platforms like Twitter and Weibo. Many cases have proved that citizens are able to organize themselves promptly on the spot, and begin to share disaster information when a disaster strikes, producing massive VGI (volunteered geographic information) about the disaster situation, which could be valuable for disaster response if this VGI could be exploited efficiently and properly. However, this social media information has features such as large quantity, high noise, and unofficial modes of expression that make it difficult to obtain useful information. In order to solve this problem, we first designed a new classification system based on the characteristics of social medial data like Sina Weibo data, and made a microblogging dataset of typhoon damage with according category labels. Secondly, we used this social medial dataset to train the deep learning model, and constructed a typhoon disaster mining model based on a deep learning network, which could automatically extract information about the disaster situation. The model is different from the general classification system in that it automatically selected microblogs related to disasters from a large number of microblog data, and further subdivided them into different types of disasters to facilitate subsequent emergency response and loss estimation. The advantages of the model included a wide application range, high reliability, strong pertinence and fast speed. The research results of this thesis provide a new approach to typhoon disaster assessment in the southeastern coastal areas of China, and provide the necessary information for the authoritative information acquisition channel.

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Analisis Sentimen dan Pemodelan Topik Pariwisata Lombok Menggunakan Algoritma Naive Bayes dan Latent Dirichlet Allocation

Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) ◽

10.29207/resti.v5i1.2587 ◽

2021 ◽

Vol 5 (1) ◽

pp. 123-131

Author(s):

Ni Luh Putu Merawati Putu ◽

Ahmad Zuli Amrullah ◽

Ismarmiaty

Keyword(s):

Social Media ◽

Latent Dirichlet Allocation ◽

Naive Bayes ◽

Naïve Bayes ◽

Classification Model ◽

Bayes Method ◽

A Value ◽

Negative Class ◽

Naive Bayes Method ◽

Dirichlet Allocation

Lombok Island is one of the favorite tourist destinations. Various topics and comments about Lombok tourism experience through social media accounts are difficult to manually identify public sentiments and topics. The opinion expressed by tourists through social media is interesting for further research. This study aims to classify tourists' opinions into two classes, positive and negative, and topics modelling by using the Naive Bayes method and modeling the topic by using Latent Dirichlet Allocation (LDA). The stages of this research include data collection, data cleaning, data transformation, data classification. The results performance testing of the classification model using Naive Bayes method is shown with an accuracy value of 92%, precision of 100%, recall of 84% and specificity of 100%. The results of modeling topics using LDA in each positive and negative class from the coherence value shows the highest value for the positive class was obtained on the 8th topic with a value of 0.613 and for the negative class on the 12th topic with a value of 0.528. The use of the Naive Bayes and LDA algorithms is considered effective for analyzing the sentiment and topic modelling for Lombok tourism.

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PSYCHOSOCIAL STRESS CONTAGION OF COVID-19: ISSUES AND INTERVENTION CHANNELS

Ensemble ◽

10.37948/ensemble-2021-sp1-a006 ◽

2021 ◽

Vol SP-1 (1) ◽

pp. 44-53

Author(s):

DR. SOURAV MADHUR DEY ◽

Keyword(s):

Social Media ◽

Substance Dependence ◽

Virus Disease ◽

Financial Burden ◽

Psychosocial Impact ◽

Economic Losses ◽

Post Traumatic Stress ◽

Crisis Prevention ◽

The Government ◽

Frontline Healthcare Workers

Along with its elevated contamination and fatality rates, the 2019 Corona Virus Disease (COVID-19) has caused worldwide psychosocial impact by causing mass hysteria, financial burden and economic losses. Mass fear of COVID-19, termed as “coronaphobia”, has generated a glut of psychiatric exposition across diverse strata of the society. So, this review has been set about to portray psychosocial impact of COVID-19. Disease itself conglomerated by mandatory quarantine to counter COVID-19 applied by countrywide lockdowns can generate acute panic, angst, obsessive behaviors, stock piling, paranoia and depression, and post-traumatic stress disorder in due course. These have been stirred up by an “infodemic” spread via various platforms of social media. Outbreak of racism, stigmatization, and xenophobia against particular communities are also being extensively reported. Nonetheless, frontline healthcare workers are at elevated risk of acquiring the disease as well as experiencing detrimental psychological outcomes in shape of exhaustion, worry, trepidation of transmitting infection, augmented substance-dependence and PTSD. The present article attempts to investigate these areas of contagion and suggest intervention dynamics to address the issue and therefore observes that psychosocial crisis prevention and intervention models should be urgently devised by the government, health care personnel and other stakeholders. Appropriate application of internet services, technology and social media to control both pandemic and ‘infodemic’ require to be initiated. Psychosocial alertness by setting up mental organizations particular for future pandemics is indeed crucial.

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Public Sentiment on Chinese Social Media during the Emergence of COVID19

Journal of Quantitative Description: Digital Media ◽

10.51685/jqd.2021.013 ◽

2021 ◽

Vol 1 ◽

Author(s):

Yingdan Lu ◽

Jennifer Pan ◽

Yiqing Xu

Keyword(s):

Social Media ◽

Online Discussions ◽

Public Welfare ◽

Chinese Government ◽

Positive Outcomes ◽

Sina Weibo ◽

Public Sentiment ◽

Aggressive Action ◽

The Government ◽

Personal Reflections

When COVID-19 first emerged in China, there was speculation that the outbreak would trigger public anger and weaken the Chinese regime. By analyzing millions of social media posts from Sina Weibo made between December 2019 and February 2020, we describe the contours of public, online discussions pertaining to COVID-19 in China. We find that discussions of COVID-19 became widespread on January 20, 2020, consisting primarily of personal reflections, opinions, updates, and appeals. We find that the largest bursts of discussion, which contain simultaneous spikes of criticism and support targeting the Chinese government, coincide with the January 23 lockdown of Wuhan and the February 7 death of Dr. Li Wenliang. Criticisms are directed at the government for perceived lack of action, incompetence, and wrongdoing—in particular, censoring information relevant to public welfare. Support is directed at the government for aggressive action and positive outcomes. As the crisis unfolds, the same events are interpreted differently by different people, with those who criticize focusing on the government’s shortcomings and those who praise focusing on the government’s actions.

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Food Safety Event Detection Based on Multi-Feature Fusion

Symmetry ◽

10.3390/sym11101222 ◽

2019 ◽

Vol 11 (10) ◽

pp. 1222

Author(s):

Xiao ◽

Wang ◽

Zhang ◽

Qian

Keyword(s):

Food Safety ◽

Event Detection ◽

Latent Dirichlet Allocation ◽

Feature Fusion ◽

Online News ◽

Economic Losses ◽

Named Entity ◽

Document Frequency ◽

Safety Event ◽

The Government

Food safety event detection is a technique used to discover food safety events by monitoring online news. In general, a set of keywords are extracted as features to represent news, and then the news is clustered to generate events. The most popular method for news feature extraction is Term Frequency-Inverse Document Frequency (TF-IDF), however, it has some defects such as being prone to the “dimension disaster”, low computational efficiency, and a lack of semantic information. In addition, Latent Dirichlet Allocation (LDA) is also widely used in news representation. Despite its low dimension, it still suffers from some drawbacks such as the need to set a predefined number of clusters and has difficulty recognizing new events. In this paper, a method based on multi-feature fusion is proposed, which combines the TF-IDF features, the named entity features, and the headline features to represent the news. Based on the representations, the incremental clustering method is used to cluster the news documents and to detect food safety events. Compared with the traditional methods, the proposed method achieved higher Precision, Recall, and F1 scores. The proposed method can help regulatory authorities to make decisions and improve the reputation of the government, whilst reducing social anxiety and economic losses.

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Text Mining Approaches to Analyze Public Sentiment Changes Regarding COVID-19 Vaccines on Social Media in Korea

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph18126549 ◽

2021 ◽

Vol 18 (12) ◽

pp. 6549

Author(s):

Jae-Geum Shim ◽

Kyoung-Ho Ryu ◽

Sung Hyun Lee ◽

Eun-Ah Cho ◽

Yoon Ju Lee ◽

...

Keyword(s):

Social Media ◽

Sentiment Analysis ◽

Time Course ◽

Latent Dirichlet Allocation ◽

Allocation Model ◽

Public Response ◽

Latent Dirichlet Allocation Model ◽

Public Sentiment ◽

Before And After

The COVID-19 pandemic has affected the entire world, resulting in a tremendous change to people’s lifestyles. We investigated the Korean public response to COVID-19 vaccines on social media from 23 February 2021 to 22 March 2021. We collected tweets related to COVID-19 vaccines using the Korean words for “coronavirus” and “vaccines” as keywords. A topic analysis was performed to interpret and classify the tweets, and a sentiment analysis was conducted to analyze public emotions displayed within the retrieved tweets. Out of a total of 13,414 tweets, 3509 were analyzed after preprocessing. Eight topics were extracted using the Latent Dirichlet Allocation model, and the most frequently tweeted topic was vaccine hesitation, consisting of fear, flu, safety of vaccination, time course, and degree of symptoms. The sentiment analysis revealed a similar ratio of positive and negative tweets immediately before and after the commencement of vaccinations, but negative tweets were prominent after the increase in the number of confirmed COVID-19 cases. The public’s anticipation, disappointment, and fear regarding vaccinations are considered to be reflected in the tweets. However, long-term trend analysis will be needed in the future.

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Comparing Public Sentiment towards COVID-19 Vaccines across Canadian Cities: Analysis of Comments on Reddit (Preprint)

10.2196/preprints.32685 ◽

2021 ◽

Author(s):

Cathy Yan ◽

Melanie Law ◽

Stephanie Nguyen ◽

Janelle Cheung ◽

Jude Kong

Keyword(s):

Social Media ◽

Random Forest ◽

Latent Dirichlet Allocation ◽

Local Level ◽

The Other ◽

Vaccine Uptake ◽

Local Culture ◽

Random Forest Regression ◽

Country Level ◽

Public Sentiment

BACKGROUND Social media enables the rapid consumption of news related to COVID-19, and serves as a platform for discussions. Its richness in text-based data in the form of posts and comments allows researchers to identify popular topics and assess public sentiment. Yet, the vast majority of this type of work is done on the platform or country level, and does not account for local culture and policies. OBJECTIVE The aim of this study is to use location-based subreddits on Reddit to study city-level variations in sentiments towards vaccine-related topics. METHODS Comments made on posts providing regular updates on COVID-19 statistics in the Vancouver, Toronto, and Calgary subreddits (r/vancouver, r/toronto, r/calgary) between July 13th, 2020 and June 14th, 2021 were extracted (N = 49,291, 20,764, and 21,277, respectively). Latent Dirichlet allocation was used to identify frequently discussed topics. Sentiment (joy, sadness, fear, and anger) scores were assigned to comments using random forest regression. RESULTS Beginning by focusing on the Vancouver subreddit, the number of comments made on each post positively correlated with the number of new cases (P < 0.001). From the comments, thirteen topics were identified. Two were related to vaccines, one regarding vaccine uptake and the other about vaccine supply. The levels of discussion for both topics were linked to the total number of vaccinations given (Granger test for causality: P < 0.001). Comments pertaining to either topic displayed higher scores for joy compared to comments about other topics (P < 0.001). Calgary and Toronto also discussed vaccine uptake. Sentiment scores for this topic differed across the three cities (P < 0.001). CONCLUSIONS Overall, our work demonstrates that comments from Reddit can be used to better understand concerns and sentiments surrounding the pandemic at the local level, which enables more targeted and publicly-acceptable policies.

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Analysis of public opinion evolution of COVID-19 based on LDA-ARMA hybrid model

Complex & Intelligent Systems ◽

10.1007/s40747-021-00514-7 ◽

2021 ◽

Author(s):

Muni Zhuang ◽

Yong Li ◽

Xu Tan ◽

Lining Xing ◽

Xin Lu

Keyword(s):

Public Opinion ◽

Large Scale ◽

Latent Dirichlet Allocation ◽

Moving Average ◽

Classification Model ◽

Average Error ◽

Autoregressive Moving Average Model ◽

Treatment Measures ◽

Moving Average Model ◽

The Government

AbstractThe aim of this study was to explore a method for developing an emotional evolution classification model for large-scale online public opinion of events such as Coronavirus Disease 2019 (COVID-19), in order to guide government departments to adopt differentiated forms of emergency management and to correctly guide online public opinion for severely afflicted areas such as Wuhan and those afflicted elsewhere in China. We propose the LDA-ARMA deep neural network for dynamic presentation and fine-grained categorization of a public opinion events. This was applied to a huge quantity of online public opinion texts in a complicated setting and integrated the proposed sentiment measurement algorithm. To begin, the Latent Dirichlet Allocation (LDA) was employed to extract information about the topic of comments. The autoregressive moving average model (ARMA) was then utilized to perform multidimensional sentiment analysis and evolution prediction on large-scale textual data related to COVID-19 published by netizens from Wuhan and other countries on Sina Weibo. The results show that Wuhan netizens paid more attention to the development of the situation, treatment measures, and policies related to COVID-19 than other issues, and were under greater emotional pressure, whereas netizens in the rest of the country paid more attention to the overall COVID-19 prevention and control, and were more positive and optimistic with the assistance of the government and NGOs. The average error in predicting public opinion sentiment was less than 5.64%, demonstrating that this approach may be effectively applied to the analysis of large-scale online public sentiment evolution.

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Top Concerns of Tweeters During the COVID-19 Pandemic: Infoveillance Study (Preprint)

10.2196/preprints.19016 ◽

2020 ◽

Author(s):

Alaa Abd-Alrazaq ◽

Dari Alhuwail ◽

Mowafa Househ ◽

Mounir Hamdi ◽

Zubair Shah

Keyword(s):

Public Health ◽

Health Care ◽

Social Media ◽

Latent Dirichlet Allocation ◽

Well Being ◽

Crisis Response ◽

Economic Losses ◽

Surveillance Systems ◽

Health Crisis ◽

The Mean

BACKGROUND The recent coronavirus disease (COVID-19) pandemic is taking a toll on the world’s health care infrastructure as well as the social, economic, and psychological well-being of humanity. Individuals, organizations, and governments are using social media to communicate with each other on a number of issues relating to the COVID-19 pandemic. Not much is known about the topics being shared on social media platforms relating to COVID-19. Analyzing such information can help policy makers and health care organizations assess the needs of their stakeholders and address them appropriately. OBJECTIVE This study aims to identify the main topics posted by Twitter users related to the COVID-19 pandemic. METHODS Leveraging a set of tools (Twitter’s search application programming interface (API), Tweepy Python library, and PostgreSQL database) and using a set of predefined search terms (“corona,” “2019-nCov,” and “COVID-19”), we extracted the text and metadata (number of likes and retweets, and user profile information including the number of followers) of public English language tweets from February 2, 2020, to March 15, 2020. We analyzed the collected tweets using word frequencies of single (unigrams) and double words (bigrams). We leveraged latent Dirichlet allocation for topic modeling to identify topics discussed in the tweets. We also performed sentiment analysis and extracted the mean number of retweets, likes, and followers for each topic and calculated the interaction rate per topic. RESULTS Out of approximately 2.8 million tweets included, 167,073 unique tweets from 160,829 unique users met the inclusion criteria. Our analysis identified 12 topics, which were grouped into four main themes: origin of the virus; its sources; its impact on people, countries, and the economy; and ways of mitigating the risk of infection. The mean sentiment was positive for 10 topics and negative for 2 topics (deaths caused by COVID-19 and increased racism). The mean for tweet topics of account followers ranged from 2722 (increased racism) to 13,413 (economic losses). The highest mean of likes for the tweets was 15.4 (economic loss), while the lowest was 3.94 (travel bans and warnings). CONCLUSIONS Public health crisis response activities on the ground and online are becoming increasingly simultaneous and intertwined. Social media provides an opportunity to directly communicate health information to the public. Health systems should work on building national and international disease detection and surveillance systems through monitoring social media. There is also a need for a more proactive and agile public health presence on social media to combat the spread of fake news.

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