Weibo Data Research Articles

Quantifying Spatiotemporal Heterogeneities in PM2.5-Related Health and Associated Determinants Using Geospatial Big Data: A Case Study in Beijing

Air pollution has brought about serious challenges to public health. With the limitations of available data, previous studies overlooked spatiotemporal heterogeneities in PM2.5-related health (PM2.5-RH) and multiple associated factors at the subdistrict scale. In this research, social media Weibo data was employed to extract PM2.5-RH based on the Bidirectional Encoder Representations from Transformers (BERT) model, in Beijing, China. Then, the relationship between PM2.5-RH and eight associated factors was qualified based on multi-source geospatial big data using Geographically Weighted Regression (GWR) models. The results indicate that the PM2.5-RH in the study area showed a spatial pattern of agglomeration to the city center and seasonal variation in the spatially non-stationary effects. The impacts of varied factors on PM2.5-RH were also spatiotemporally heterogeneous. Specifically, nighttime light (NTL), population density (PD) and the normalized difference built-up index (NDBI) had outstanding effects on PM2.5-RH in the four seasons, but with spatial disparities. The impact of the normalized difference vegetation index (NDVI) on PM2.5-RH was significant in summer, especially in the central urban areas, while in winter, the contribution of the air quality index (AQI) was increased. This research further demonstrates the feasibility of using social media data to indicate the effect of air pollution on public health and provides new insights into the seasonal impacts of associated driving factors on the health effects of air pollution.

The Impact of Shanghai Epidemic, China, 2022 on Public Psychology: A Sentiment Analysis of Microblog Users by Data Mining

The Shanghai epidemic (COVID-19) is another large-scale epidemic in China’s central cities in the post-epidemic era from the end of March 2022. It is of great significance to study the public’s attitude towards the Shanghai epidemic to support healthy psychology and a positive attitude. This study used crawler technology to obtain the Weibo data related to the epidemic situation in Shanghai that was published by users. The crawled microblogs were preprocessed, and the BosonNLP sentiment dictionary for attitude classification was selected. The Chinese vocabulary ontology for 21 emotion classifications was also used. The results showed that the general attitude of the public in Shanghai was positive. This fluctuated greatly in the initial stage and gradually increased in the later stage. Through text mining, it is clear that goods and materials, nucleic acid virus testing, and other aspects of the epidemic in Shanghai are concerning for the public. The public attitude in areas that are close to the epidemic center is relatively more negative. The study can provide references for policymakers to fight COVID-19 by improving public attitude and solving urgent matters.

Construction of Mental Health Education Model for College Students Based on Fine-Grained Parallel Computing Programming

Psychological education is beneficial in maintaining the psychological health of college students, resolving psychological issues, and creating a harmonious campus and society. Mental health education for college students is a development-oriented basic education activity that contributes significantly to educational quality. To improve the accuracy of college students' mental health assessments, a model for mental health education for college students based on fine-grained parallel computing programming is proposed. This study uses a deep learning algorithm to subdivide the classification of an emotion dictionary, which can be classified by adding negative word lists, polarity conversion dictionaries, and online dictionaries, among other things, based on the theory of ecological instantaneous evaluation. It can be used for both multiclass and detailed emotion analysis. The model is more accurate in assessing the mental health of college students, according to the results of the study. The current emotional state of users can be identified, as well as signs of psychological risk, using emotional analysis of Weibo data, which will become a valuable resource for users seeking clinical psychological consultation in the future.

Research on Emotion Analysis and Psychoanalysis Application With Convolutional Neural Network and Bidirectional Long Short-Term Memory.

This study mainly focuses on the emotion analysis method in the application of psychoanalysis based on sentiment recognition. The method is applied to the sentiment recognition module in the server, and the sentiment recognition function is effectively realized through the improved convolutional neural network and bidirectional long short-term memory (C-BiL) model. First, the implementation difficulties of the C-BiL model and specific sentiment classification design are described. Then, the specific design process of the C-BiL model is introduced, and the innovation of the C-BiL model is indicated. Finally, the experimental results of the models are compared and analyzed. Among the deep learning models, the accuracy of the C-BiL model designed in this study is relatively high irrespective of the binary classification, the three classification, or the five classification, with an average improvement of 2.47% in Diary data set, 2.16% in Weibo data set, and 2.08% in Fudan data set. Therefore, the C-BiL model designed in this study can not only successfully classify texts but also effectively improve the accuracy of text sentiment recognition.

Online public opinion during the first epidemic wave of COVID-19 in China based on Weibo data

AbstractAs COVID-19 spread around the world, epidemic prevention and control policies have been adopted by many countries. This process has prompted online social platforms to become important channels to enable people to socialize and exchange information. The massive use of social media data mining techniques, to analyze the development online of public opinion during the epidemic, is of great significance in relation to the management of public opinion. This paper presents a study that aims to analyze the developmental course of online public opinion in terms of fine-grained emotions presented during the COVID-19 epidemic in China. It is based on more than 45 million Weibo posts during the period from December 1, 2019 to April 30, 2020. A text emotion extraction method based on a dictionary of emotional ontology has been developed. The results show, for example, that a high emotional effect is observed during holidays, such as New Year. As revealed by Internet users, the outbreak of the COVID-19 epidemic and its rapid spread, over a comparatively short period of time, triggered a sharp rise in the emotion “fear”. This phenomenon was noted especially in Wuhan and the immediate surrounding areas. Over the initial 2 months, although this “fear” gradually declined, it remained significantly higher than the more common level of uncertainty that existed during the epidemic’s initial developmental era. Simultaneously, in the main city clusters, the response to the COVID-19 epidemic in central cities, was stronger than that in neighboring cities, in terms of the above emotion. The topics of Weibo posts, the corresponding emotions, and the analysis conclusions can provide auxiliary reference materials for the monitoring of network public opinion under similar major public events.

A Multimodal Data Analysis Approach to Social Media during Natural Disasters

During natural disasters, social media can provide real time or rapid disaster, perception information to help government managers carry out disaster response efforts efficiently. Therefore, it is of great significance to mine social media information accurately. In contrast to previous studies, this study proposes a multimodal data classification model for mining social media information. Using the model, the study employs Late Dirichlet Allocation (LDA) to identify subject information from multimodal data, then, the multimodal data is analyzed by bidirectional encoder representation from transformers (Bert) and visual geometry group 16 (Vgg-16). Text and image data are classified separately, resulting in real mining of topic information during disasters. This study uses Weibo data during the 2021 Henan heavy storm as the research object. Comparing the data with previous experiment results, this study proposes a model that can classify natural disaster topics more accurately. The accuracy of this study is 0.93. Compared with a topic-based event classification model KGE-MMSLDA, the accuracy of this study is improved by 12%. This study results in a real-time understanding of different themed natural disasters to help make informed decisions.

Spatio-Temporal Patterns of Fitness Behavior in Beijing Based on Social Media Data

Fitness is an important way to ensure the health of the population, and it is important to actively understand fitness behavior. Although social media Weibo data (the Chinese Tweeter) can provide multidimensional information in terms of objectivity and generalizability, there is still more latent potential to tap. Based on Sina Weibo social media data in the year 2017, this study was conducted to explore the spatial and temporal patterns of urban residents’ different fitness behaviors and related influencing factors within the Fifth Ring Road of Beijing. FastAI, LDA, geodetector technology, and GIS spatial analysis methods were employed in this study. It was found that fitness behaviors in the study area could be categorized into four types. Residents can obtain better fitness experiences in sports venues. Different fitness types have different polycentric spatial distribution patterns. The residents’ fitness frequency shows an obvious periodic distribution (weekly and 24 h). The spatial distribution of the fitness behavior of residents is mainly affected by factors, such as catering services, education and culture, companies, and public facilities. This research could help to promote the development of urban residents’ fitness in Beijing.

A spatial-temporal topic model with sparse prior and RNN prior for bursty topic discovering in social networks

With the rapid growth of social network users, the social network has accumulated massive social network topics. However, due to the randomness of content, it becomes sparse and noisy, accompanied by many daily chats and meaningless topics, which brings challenges to bursty topics discovery. To deal with these problems, this paper proposes the spatial-temporal topic model with sparse prior and recurrent neural networks (RNN) prior for bursty topic discovering (ST-SRTM). The semantic relationship of words is learned through RNN to alleviate the sparsity. The spatial-temporal areas information is introduced to focus on bursty topics for further weakening the semantic sparsity of social network context. Besides, we introduced the “Spike and Slab” prior to decouple the sparseness and smoothness. Simultaneously, we realized the automatic discovery of social network bursts by introducing the burstiness of words as the prior and binary switching variables. We constructed multiple sets of comparative experiments to verify the performance of ST-SRTM by leveraging different evaluation indicators on real Sina Weibo data sets. The experimental results confirm the superiority of our ST-SRTM.

Assessing Reliability of Chinese Geotagged Social Media Data for Spatiotemporal Representation of Human Mobility

Understanding the space-time dynamics of human activities is essential in studying human security issues such as climate change impacts, pandemic spreading, or urban sustainability. Geotagged social media posts provide an open and space-time continuous data source with user locations which is convenient for studying human movement. However, the reliability of Chinese geotagged social media data for representing human mobility remains unclear. This study compares human movement data derived from the posts of Sina Weibo, one of the largest social media software in China, and that of Baidu Qianxi, a high-resolution human movement dataset from ‘Baidu Map’, a popular location-based service in China with 1.3 billion users. Correlation analysis was conducted from multiple dimensions of time periods (weekly and monthly), geographic scales (cities and provinces), and flow directions (inflow and outflow), and a case study on COVID-19 transmission was further explored with such data. The result shows that Sina Weibo data can reveal similar patterns as that of Baidu Qianxi, and that the correlation is higher at the provincial level than at the city level and higher at the monthly scale than at the weekly scale. The study also revealed spatial variations in the degree of similarity between the two sources. Findings from this study reveal the values and properties and spatiotemporal heterogeneity of human mobility data extracted from Weibo tweets, providing a reference for the proper use of social media posts as the data sources for human mobility studies.

Early detection of emergency events from social media: a new text clustering approach.

Emergency events require early detection, quick response, and accurate recovery. In the era of big data, social media users can be seen as social sensors to monitor real-time emergency events. This paper proposed an integrated approach to detect all four kinds of emergency events early, including natural disasters, man-made accidents, public health events, and social security events. First, the BERT-Att-BiLSTM model is used to detect emergency-related posts from massive and irrelevant data. Then, the 3 W attribute information (what, where, and when) of the emergency event is extracted. With the 3 W attribute information, we create an unsupervised dynamical event clustering algorithm based on text similarity and combine it with the supervised logistical regression model to cluster posts into different events. Experiments on Sina Weibo data demonstrate the superiority of the proposed framework. Case studies on some real emergency events show that the proposed framework has good performance and high timeliness. Practical applications of the framework are also discussed, followed by future directions for improvement.

Research on adaption to air pollution in Chinese cities: Evidence from social media-based health sensing

Air pollution seriously threats to human health. Understanding the health effects of air pollution is of great importance for developing countermeasures. However, little is known about the real-time impacts of air pollution on the human heath in a comprehensive way in developing nations, like China. To fill this research gap, the Chinese urbanites' health were sensed from more than 210.82 million Weibo (Chinese Twitter) data in 2017. The association between air pollution and the health sensing were quantified through generalized additive models, based on which the sensitivities and adaptions to air pollution in 70 China's cities were assessed. The results documented that the Weibo data can well sense urbanites' health in real time. With the different geographical characteristics and socio-economic conditions, the Chinese residents have adaption to air pollution, indicated by the spatial heterogeneity of the sensitivities to air pollution. Cities with good air quality in South China and East China were more sensitive to air pollution, while cities with worse air quality in Northwest China and North China were less sensitive. This research provides a new perspective and methodologies for health sensing and the health effect of air pollution.

Exploring spatiotemporal changes in the multi-granularity emotions of people in the city: a case study of Nanchang, China

In the Internet age, emotions exist in cyberspace and geospatial space, and social media is the mapping from geospatial space to cyberspace. However, most previous studies pay less attention to the multidimensional and spatiotemporal characteristics of emotion. We obtained 211,526 Sina Weibo data with geographic locations and trained an emotion classification model by combining the Bidirectional Encoder Representation from Transformers (BERT) model and a convolutional neural network to calculate the emotional tendency of each Weibo. Then, the topic of the hot spots in Nanchang City was detected through a word shift graph, and the temporal and spatial change characteristics of the Weibo emotions were analyzed at the grid-scale. The results of our research show that Weibo’s overall emotion tendencies are mainly positive. The spatial distribution of the urban emotions is extremely uneven, and the hot spots of a single emotion are mainly distributed around the city. In general, the intensity of the temporal and spatial changes in emotions in the cities is relatively high. Specifically, from day to night, the city exhibits a pattern of high in the east and low in the west. From working days to weekends, the model exhibits a low center and a four-week high. These results reveal the temporal and spatial distribution characteristics of the Weibo emotions in the city and provide auxiliary support for analyzing the happiness of residents in the city and guiding urban management and planning.

Rumor detection based on graph attention network

At present, most of the existing rumor detection methods focus on the learning and fusion of various features, but due to the complexity of language, these models often rarely consider the relationship between parts of speech. This paper uses graph attention neural network model to learn text features and syntactic relations to solve this problem. It uses node attention collection text feature and edge attention collection relationship feature for syntactic dependency tree, and node attention and edge attention to enhance each other. Finally, the proposed method is verified on Twitter and Weibo data sets. The experimental results show that the proposed method has greatly improved the early detection and accuracy of rumors.

Freeway accident duration prediction based on social network information

Accident duration prediction is the basis of freeway emergency management, and timely and accurate accident duration prediction can provide a reliable basis for road traffic diversion and rescue agencies. This study proposes a method for predicting the duration of freeway accidents based on social network information by collecting Weibo data of freeway accidents in Sichuan province and using the advantage that human language can convey multi-dimensional information. Firstly, text features are extracted through a TF-IDF model to represent the accident text data quantitatively; secondly, the variability between text data is exploited to construct an ordered text clustering model to obtain clustering intervals containing temporal attributes, thus converting the ordered regression problem into an ordered classification problem; finally, two nonparametric machine learning methods, namely support vector machine (SVM) and k-nearest neighbour method (KNN), to construct an accident duration prediction model. The results show that when the ordered text clustering model divides the text dataset into four classes, both the SVM model and the KNN model show better prediction results, and their average absolute error values are less than 22 %, which is much better than the prediction results of the regression prediction model under the same method.

Is Artificial Intelligence Customer Service Satisfactory? Insights Based on Microblog Data and User Interviews

A growing number of sectors are delivering customer services powered by artificial intelligence (AI) instead of humans, with evidence indicating labor cost reduction and efficiency improvement. However, it would be worthwhile to examine the extent to which consumers are satisfied with AI service agents. In two studies based on an analysis of 17,673 Weibo data (Study 1) and 33 interviews (Study 2), we constructed a pair of theoretical models of consumer attitudes toward AI services: a sentiment model and an evaluation model. The results from Weibo data analysis showed that consumers display a stronger negative attitude toward AI customer service than toward their human counterparts. Complaints regarding AI customer service is mainly about its poor problem-solving ability, while untimely response and lack of human touch also dissatisfy customers. Whether consumers offer positive feedback mainly depends on voice traits and service attitudes. The results from the interviews confirm an overall negative attitude of consumers toward AI customer service. Consumers also recognize AI customer service agents as human like and social interaction stress relieving. Taken together, these findings reveal Chinese customers' attitudes toward AI service solutions and provide concrete suggestions for the development and upgrade of AI customer services.

Social media, gendered anxiety and disease-related misinformation: discourses in contemporary China’s online anti-African sentiments

ABSTRACT This article combines automated scraping of Weibo data and a critical discourse analysis to examine the ways in which online anti-African sentiments produce and amplify the interrelations of racial stigma, sexism and homophobia, as well as misinformation about infectious disease on Chinese social media. The paper finds that three nodal points strongly unite the online anti-African discourse: one, ‘unrestrained and promiscuous’ African men are carrying the viruses (such as AIDS and/or COVID-19); two, ‘unchaste’ Chinese women (and occasionally gay men) are receiving the virus; three, there is unidirectional transmission of these viruses from Africans to Chinese. Further, our research findings point to complicated and ambiguous relations between online racist sentiments, state censorship, and China-Africa relations.

Research of Public Service Quality Based on Weibo Data Analysis: A Case Study of Public Housing Security Service in Shanghai

Research of Public Service Quality Based on Weibo Data Analysis: A Case Study of Public Housing Security Service in Shanghai

Spatial Analysis of Urban Residential Sensitivity to Heatwave Events: Case Studies in Five Megacities in China

Urban heatwaves increase residential health risks. Identifying urban residential sensitivity to heatwave risks is an important prerequisite for mitigating the risks through urban planning practices. This research proposes a new paradigm for urban residential sensitivity to heatwave risks based on social media Big Data, and describes empirical research in five megacities in China, namely, Beijing, Nanjing, Wuhan, Xi’an and Guangzhou, which explores the application of this paradigm to real-world environments. Specifically, a method to identify urban residential sensitive to heatwave risks was developed by using natural language processing (NLP) technology. Then, based on remote sensing images and Weibo data, from the perspective of the relationship between people (group perception) and the ground (meteorological temperature), the relationship between high temperature and crowd sensitivity in geographic space was studied. Spatial patterns of the residential sensitivity to heatwaves over the study area were characterized at fine scales, using the information extracted from remote sensing information, spatial analysis, and time series analysis. The results showed that the observed residential sensitivity to urban heatwave events (HWEs), extracted from Weibo data (Chinese Twitter), best matched the temporal trends of HWEs in geographic space. At the same time, the spatial distribution of observed residential sensitivity to HWEs in the cities had similar characteristics, with low sensitivity in the urban center but higher sensitivity in the countryside. This research illustrates the benefits of applying multi-source Big Data and intelligent analysis technologies to the understand of impacts of heatwave events on residential life, and provide decision-making data for urban planning and management.

Spatiotemporal Analysis of Residents in Shanghai by Utilizing Chinese Microblog Weibo Data

Mobile applications are really important nowadays due to providing the accurate check-in data for research. The primary goal of the study is to look into the impact of several forms of entertainment activities on the density dispersal of occupants in Shanghai, China, as well as prototypical check-in data from a location-based social network using a combination of temporal, spatial, and visualization techniques and categories of visitors’ check-ins. This article explores Weibo for big data assessment and its reliability in a variety of categories rather than physically obtained information by examining the link between time, frequency, place, class, and place of check-in based on geographic attributes and related implications. The data for this study came from Weibo, a popular Chinese microblog. It was preprocessed to extract the most important and associated results elements, then converted to geographical information systems format, appraised, and finally displayed using graphs, tables, and heat maps. For data significance, a linear regression model was used, and, for spatial analysis, kernel density estimation was utilized. As per results of hours-to-day usage patterns, enjoyment activities and frequency distribution are produced. Our findings are based on the check-in behaviour of users at amusement locations, the density of check-ins, rush periods for visiting amusement locations, and gender differences. Our data provide light on different elements of human behaviour patterns, the importance of entertainment venues, and their impact in Shanghai. So it can be used in pattern recognition, endorsement structures, and additional multimedia content for these collections.

Novel Network Public Opinion Prediction and Guidance Model Based on “S-Curve”: Taking the Loss of Contact with “Malaysia Airlines”

It is of great significance for the government to control the network public opinion in time and maintain social stability to predict the network public opinion in emergency. This paper proposes a novel improvement method to “S-curve” theory in the context of big data and establishes three novel network public opinion prediction models. These models take into account the proliferation trend of initial and follow-up network public opinion over a long period of time when emergencies are formed and the objective environment suddenly changes, based on the information diffusion model conforming to the traditional “S-curve” theory. The novel improvement and establishment allow our model to have more accurate predictions than other scholars’ models that mainly study the first network public opinion in a shorter period of time. And it is more applicable to real social conditions, in line with the public’s cognition of reality, and provides more reference for the government to take preventive and corresponding positive guiding measures in advance. To better establish the model, we obtained the 24-day Weibo data associated with the incident of “Malaysia Airlines” loss of contact from big data for model establishment, public opinion prediction, and comprehensive evaluation. The result innovatively shows that, in addition to the initial public opinion that is worthy of attention, the follow-up public opinion is also noteworthy and proves that our model has more practical value.