Weibo Data Research Articles

Culture vs. distance: comparing the effects of geographic segmentation variables on tourists\u2019 destination images based on social media data

Culture and distance are two major factors for geographically segmenting tourists in tourism marketing and advertising. Previous empirical studies on the destination image, however, have examined extensively the effect of the culture while inadequately the effect of the distance, let alone comparing the effects of the two variables. Using social media data, this study compares the effect of distance-based segments of tourists with that of culture-based segments in producing diverse perceived images of a destination. From Sina Weibo data, 282,532 Chinese mainland tourists who visited Suzhou, China during 2012–2016 and their perceived destination images are extracted and analyzed. The main results include: 1) for distance-based segments, the image differences increased with distance and the short-haul tourists perceived a more comprehensive image than the long-haul tourists; 2) for culture-based segments, the image differences were clear and relatively complex, while tourists from Wuyue cultural region had similar image perceptions with the local visitors; 3) the q-statistic of the Geodetector method shows that the culture-based segmentation can explain 65.8% of image variations while the distance-based segmentation can explain 46.6% of image variations, suggesting that culture is a more appropriate variable to segment the tourism market.

Perceiving Residents’ Festival Activities Based on Social Media Data: A Case Study in Beijing, China

Social media data contains real-time expressed information, including text and geographical location. As a new data source for crowd behavior research in the era of big data, it can reflect some aspects of the behavior of residents. In this study, a text classification model based on the BERT and Transformers framework was constructed, which was used to classify and extract more than 210,000 residents’ festival activities based on the 1.13 million Sina Weibo (Chinese “Twitter”) data collected from Beijing in 2019 data. On this basis, word frequency statistics, part-of-speech analysis, topic model, sentiment analysis and other methods were used to perceive different types of festival activities and quantitatively analyze the spatial differences of different types of festivals. The results show that traditional culture significantly influences residents’ festivals, reflecting residents’ motivation to participate in festivals and how residents participate in festivals and express their emotions. There are apparent spatial differences among residents in participating in festival activities. The main festival activities are distributed in the central area within the Fifth Ring Road in Beijing. In contrast, expressing feelings during the festival is mainly distributed outside the Fifth Ring Road in Beijing. The research integrates natural language processing technology, topic model analysis, spatial statistical analysis, and other technologies. It can also broaden the application field of social media data, especially text data, which provides a new research paradigm for studying residents’ festival activities and adds residents’ perception of the festival. The research results provide a basis for the design and management of the Chinese festival system.

Exploring the Spatiotemporal Patterns of Residents’ Daily Activities Using Text-Based Social Media Data: A Case Study of Beijing, China

The use of social media data provided powerful data support to reveal the spatiotemporal characteristics and mechanisms of human activity, as it integrated rich spatiotemporal and textual semantic information. However, previous research has not fully utilized its semantic and spatiotemporal information, due to its technical and algorithmic limitations. The efficiency of the deep mining of textual semantic resources was also low. In this research, a multi-classification of text model, based on natural language processing technology and the Bidirectional Encoder Representations from Transformers (BERT) framework is constructed. The residents’ activities in Beijing were then classified using the Sina Weibo data in 2019. The results showed that the accuracy of the classifications was more than 90%. The types and distribution of residents’ activities were closely related to the characteristics of the activities and holiday arrangements. From the perspective of a short timescale, the activity rhythm on weekends was delayed by one hour as compared to that on weekdays. There was a significant agglomeration of residents’ activities that presented a spatial co-location cluster pattern, but the proportion of balanced co-location cluster areas was small. The research demonstrated that location conditions, especially the microlocation condition (the distance to the nearest subway station), were the driving factors that affected the resident activity cluster patterns. In this research, the proposed framework integrates textual semantic analysis, statistical method, and spatial techniques, broadens the application areas of social media data, especially text data, and provides a new paradigm for the research of residents’ activities and spatiotemporal behavior.

Effects of PM2.5 on People's Emotion: A Case Study of Weibo (Chinese Twitter) in Beijing.

PM2.5 not only harms physical health but also has negative impacts on the public’s wellbeing and cognitive and behavioral patterns. However, traditional air quality assessments may fail to provide comprehensive, real-time monitoring of air quality because of the sparse distribution of air quality monitoring stations. Overcoming some key limitations of traditional surface monitoring data, Web-based social media platforms, such as Twitter, Weibo, and Facebook, provide a promising tool and novel perspective for environmental monitoring, prediction, and evaluation. This study aims to investigate the relationship between PM2.5 levels and people’s emotional intensity by observing social media postings. This study defines the “emotional intensity” indicator, which is measured by the number of negative posts on Weibo, based on Weibo data related to haze from 2016 and 2017. This study estimates sentiment polarity using a recurrent neural networks model based on LSTM (Long Short-Term Memory) and verifies the correlation between high PM2.5 levels and negative posts on Weibo using a Pearson correlation coefficient and multiple linear regression model. This study makes the following observations: (1) Taking the two-year data as an example, this study recorded the significant influence of PM2.5 levels on netizens’ posting behavior. (2) Air quality, meteorological factors, the seasons, and other factors have a strong influence on netizens’ emotional intensity. (3) From a quantitative viewpoint, the level of PM2.5 varies by 1 unit, and the number of negative Weibo posts fluctuates by 1.0168 units. Thus, it can be concluded that netizens’ emotional intensity is significantly positively affected by levels of PM2.5. The high correlation between PM2.5 levels and emotional intensity and the sensitivity of social media data shows that social media data can be used to provide a new perspective on the assessment of air quality.

Classification and Evaluation for Microblog Popularity Prediction

In recent years, with the rapid development of the Internet, especially the mobile Internet, social networks have entered the stage of vigorous development and become one of the main sources of information. User-generated contents (UGC) on social platforms can spread information along social networks at an astonishing speed. Existing literature has proposed many prediction methods for the popularity prediction on social networks. This paper presents a classification and establishes a unified evaluation framework of popularity prediction methods for microblogs. More specifically, we divide these mainstream prediction methods into four types: feature based methods, time series methods, collaborative filtering methods and deep learning methods and conduct experiments on the real-world weibo data using these methods to predict. Finally, according to four indicators, including accuracy, efficiency, robustness and bias, we evaluate and compare the methods. Based on the prediction and evaluation results, this paper summarizes and draws the following research conclusions:(1) The deep learning method has the characteristics of high accuracy, high robustness and low bias. The DeepFM method, one of the deep learning methods, performs better than the other three prediction methods when using temporal data as its input. (2) The feature based methods only using temporal features are basically consistent with those using all available features, indicating that the temporal feature has strong prediction power. Therefore, the ‘peeking’ strategy that monitors the early response of users in the initial period after the items are posted is effective. Additionally, the predictive power of temporary features can be further amplified in time series methods and deep learning methods. (3) Due to the sparse user-item interaction in social networks, the accuracy and efficiency of collaborative filtering methods are low, which makes it impossible to predict the popularity of items in social networks well.

EKGTF: A knowledge-enhanced model for optimizing social network-based meteorological briefings

With the frequent occurrence of extreme natural phenomena, news about meteorological disasters has increased. As a timely and effective social sensor, social networks have gradually become an important data source for the perception of extreme meteorological events. Meteorological briefing refers to screening valuable knowledge from massive data to provide decision-makers with efficient situational awareness support. However, social network-based briefing content has challenges, including colloquialisms and informal text styles. How to optimize these data in a formal text style is of great significance to improve decision-making efficiency. This paper proposes a meteorological briefing formalization module composed of three models: the text form judgment model, the formalization words detection model, and the event knowledge guided text formalization (EKGTF) model. These models are concatenated to optimize the meteorological briefing, specifically formalizing the briefing content’s language style based on Sina Weibo data. As a knowledge-enhanced model, the EKGTF model focuses on describing the core meteorological event knowledge while formalizing the content. Compared to baseline models, the EKGTF model achieves the best results on the BLEU score. Based on the meteorological briefing formalization module, a meteorological briefing formalization service framework is constructed, which is to be applied to the China Meteorological Administration (CMA) Public Meteorological Service Center.

Communicative functions of emoji sequences in the context of self-presentation: A comparative study of Weibo and Twitter users

Focusing on Weibo (a Chinese Microblogging site) and Twitter, this study adopts computer-mediated discourse analysis to examine how influencers use emoji sequences when engaging in self-presentation. It identified a variety of text-based speech acts, emoji functions, and functional relations by conducting speech act and pragmatic function analysis. ‘Claim’ is the most common text-based speech act accompanying with emoji sequences in both data groups; however, the former had a higher percentage than the later. Moreover, emoji functioning as a combination of ‘stance and action’ in sequences comprise the most prominent category in the Twitter data, whereas the ‘concept’ function accounts for the largest percentage within the Weibo data. Finally, emoji sequences serving as ‘emphasis on text’ is most employed in connection with accompanying texts in both data. This study also observed how a lack of user-desired emoji may be compensated for through collocation of emoji sequences, a device which involves significant creativity and intricacy.

The impact of family violence incidents on personality changes: An examination of social media users' messages in China.

Changes in personality tend to be intertwined with life events (e.g., family violence [FV]). This study aimed to examine the personality changes before and after an FV incident using Weibo data. Samples were selected from 1.16 million Weibo users in China who had posted their own FV experience as victims. We used Linguistic Inquiry and Word Count (LIWC) to extract the linguistic features of these unstructured texts as the scores of participants' personality. We built prediction models to measure and compare personality differences between the victim group and control group in Sample 1; and personality changes between the victim group and control group before and after an FV incident in Sample 2. Results showed that the victims' neuroticism increased and conscientiousness decreased after experiencing FV. At the same time, their agreeableness and openness levels were lower than those of the control group. Implications and limitations are also discussed.

Examining Public Concerns and Attitudes toward Unfair Events Involving Elderly Travelers during the COVID-19 Pandemic Using Weibo Data.

The Chinese government has launched a digital health code system to detect people potentially exposed to the coronavirus 2019 (COVID-19) disease and to curb its spread. Citizens are required to show the health code on their smartphones when using public transport. However, many seniors are not allowed to use public transport due to their difficulties in obtaining health codes, leading to widespread debates about these unfair events. Traditionally, public perceptions and attitudes toward such unfair events are investigated using analytical methods based on interviews or questionnaires. This study crawled seven-month messages from Sina Weibo, the Chinese version of Twitter, and developed a hybrid approach integrating term-frequency–inverse-document-frequency, latent Dirichlet allocation, and sentiment classification. Results indicate that a rumor about the unfair treatment of elderly travelers triggered public concerns. Primary subjects of concern were the status quo of elderly travelers, the provision of transport services, and unfair event descriptions. Following the government’s responses, people still had negative attitudes toward transport services, while they became more positive about the status quo of elderly travelers. These findings will guide government authorities to explore new forms of automated social control and to improve transport policies in terms of equity and fairness in future pandemics.

Analyzing Urban Spatial Patterns and Functional Zones Using Sina Weibo POI Data: A Case Study of Beijing

With the development of Web2.0 and mobile Internet, urban residents, a new type of “sensor”, provide us with massive amounts of volunteered geographic information (VGI). Quantifying the spatial patterns of VGI plays an increasingly important role in the understanding and development of urban spatial functions. Using VGI and social media activity data, this article developed a method to automatically extract and identify urban spatial patterns and functional zones. The method is put forward based on the case of Beijing, China, and includes the following three steps: (1) Obtain multi-source urban spatial data, such as Weibo data (equivalent to Twitter in Chinese), OpenStreetMap, population data, etc.; (2) Use the hierarchical clustering algorithm, term frequency-inverse document frequency (TF-IDF) method, and improved k-means clustering algorithms to identify functional zones; (3) Compare the identified results with the actual urban land uses and verify its accuracy. The experiment results proved that our method can effectively identify urban functional zones, and the results provide new ideas for the study of urban spatial patterns and have great significance in optimizing urban spatial planning.

Bidirectional Spatio-Temporal Association Between the Observed Results of Ulva Prolifera Green Tides in the Yellow Sea and the Social Response in Sina Weibo

Massive green tides caused by Ulva prolifera have annually occurred in the Yellow Sea since 2007, which has attracted much attention from the government and society. There are associations between the green tides in the Yellow Sea and social response in the social media (i.e., Sina Weibo), which are bidirectional and could be captured by the bidirectional neural network. For instance, how to detect daily U. prolifera green tides by fusing remote sensing data with social media data, and how to use the observed U. prolifera green tides to infer the social response are two challenges of State Oceanic Administration, China. This article first illustrated that there are bidirectional associations between green tides and Sina Weibo data. Then, this article introduced a bidirectional spatio-temporal associative memory neural network (BSAMNN) model for modeling this bidirectional association from the spatio-temporal perspective. BSAMNN first extracted six characteristics from green tides and nine characteristics from the social responses in 2016-2019. Second, these characteristics were split by year, and the characteristics in 2016-2018 were, respectively, put into the bidirectional associative memory neural network (BAM), which is a two-layer artificial neural network. Based on the BAM results and the observed data, the residual network was constructed. Third, BSAMNN extracted the spatio-temporal rules from the characteristics in 2016-2018 as the constraints through the mining rule algorithm and modify the results via sea surface wind and ocean surface current. Last, BSAMNN put the characteristics in 2019 into BAM and used the residual network to modify the results, which was constrained by the spatio-temporal rules. The feasibility and reliability of our approach were demonstrated by using the U. prolifera green tides in 2019. The average accuracy, false alarm rate, and missing alarm rate of BSAMNN results were 0.69, 0.25, and 0.31, respectively, which was 0.11 higher, 0.10 lower, and 0.11 lower than that of the traditional BAM. The results indicated that our method is an effective alternative of linking the U. prolifera green tides and its public sentiments on social media.

The information propagation model of Weibo network based on spiking neural P systems

Information propagation models in the Weibo network play a primary role in analyzing user behaviors, obtaining the propagation paths, determining the opinion leaders, and discovering the hot spots of public opinion. Existing research recognizes the critical role played by information propagation models from different aspects. However, few studies have investigated the specific details of information propagation in any systematic way. Spiking neural P (SNP, for short) systems are one of the most potential research carriers of information propagation by applying their concurrent structures and asynchronous firing rules. This paper proposes a simple and intuitive SNP variant, namely DWIP-SNP, for user behavior analysis in Weibo. The fundamental objects of information propagation in Weibo are represented by a similar SNP formalization. The forward, comment, delete, and other users’ behaviors in the Weibo network can be observed and proceeded more intuitively. Then, the DWIP-SNP systems are combined with time delays to indicate the dynamic information diffusion from the perspective of the Bio-computing systems. Finally, a real-world example of information propagation with Weibo data set is utilized to verify the effectiveness and feasibility of the model. The insights of the DWIP-SNP based propagation model gained from this study may be of assistance to user behavior understanding and information propagation in other complex networks.

Understanding Chinese tourist mobility and consumption-related behaviours in London using Sina Weibo check-ins

In this paper, we detail an individual-level analysis of under-exploited location-based social network (LBSN) data extracted from Sina Weibo, a comprehensive source for data-driven research focused on Chinese populations. The richness of the Sina Weibo data, coupled with high-quality venue and attraction information from Foursquare, enables us to track Chinese tourists visiting London and understand behaviours and mobility patterns revealed by their activities and venue-based ‘check-ins’. We use these check-ins to derive a series of indicators of mobility which reveal aggregate and individual-level behaviours associated with Chinese tourists in London, and which act as a tool to segment tourists based on those behaviours. Our data-driven tourist segmentation indicates that different groups of Chinese tourists have distinctive activity preferences and travel patterns. Our primary interest is in tourists’ consumption behaviours, and we reveal that tourists with similar activity preferences still exhibit individualised behaviours with regards to the nature and location of key consumption activities such as shopping and dining out. We aim to understand more about Chinese tourist shopping behaviours as a secondary activity associated with multi-purpose trips, demonstrating that these data could permit insights into tourist behaviours and mobility patterns which are not well captured by official tourism statistics, especially at a localised level. This analysis could be up-scaled to incorporate additional LBSN data sources and broader population subgroups in order to support data-driven urban analytics related to tourist mobilities and consumption behaviours.

Using data mining to explore the spatial and temporal dynamics of perceptions of metro services in China: The case of Shenzhen

Improving perceptions of service quality is a promising planning strategy for increasing the attractiveness and retaining the ridership of public transport systems. The widespread use of social media presents an opportunity to investigate the performance of transport services from the customer’s perspective. This study proposes a framework for integrating quantitative and qualitative analyses to investigate the perceptions of transport services by mining data from social media. We utilise Sina Weibo data related to the Shenzhen metro system to illustrate a text mining process categorised by semantic, spatial and temporal information. On the semantic front, in addition to identifying service attributes and sentiment polarity consistent with previous literature, we find attributes specific to the Chinese context. We also identify clear temporal variations among different service attributes by visualising the number of corresponding microblogs across varying time scales such as hours, days and weekdays. The spatial variations reveal five main clusters around central business districts and transport hubs, which produce the highest density of reports about crowdedness, waiting times, reliability and frequency. However, microblogs that report on perceptions of safety and personnel behaviour present a different spatial pattern. This research offers insights into the ways in which we can use social media data to identify key service areas for immediate improvement and to monitor and manage metro systems more effectively over the long term.

Similarity-based emergency event detection in social media

Emergency events need early detection, quick response, and accuracy recover. In the era of big data, the use of social media platforms is being popularized. Social media users can be seen as social sensors to monitor real time emergency events. In this paper, a similarity-based method is proposed to early detect all kinds of emergency events in social media, including natural disasters, accidents, public health events and social security events. The method focuses on clustering social media texts based on the 3 W attribute information (What, When, and Where) of events. First, with the two-step classification, emergency related messages are detected and divided into different types from the massive and irrelevant data. Second, the time and location information are respectively extracted with the regular expression matching and the BiLSTM model. Finally, the text similarity is calculated using the type, time and location information, based on which social media texts are clustered into different events. The experiments on Sina Weibo data demonstrate the superiority of the proposed framework. Case studies on some real emergency events show the proposed framework has good performance and high timeliness. As the attribute information of events is extracted during the algorithm flow, it can be described what emergency, and when and where it happened.

Modeling User Activity Space from Location-Based Social Media: A Case Study of Weibo

Activity space studies are beneficial for discovering meaningful activity patterns and providing a deeper understanding of human behaviors. There is insufficient research, however, on how reliable location-based social media (LBSM) is as a new data source for discovering user activity spaces. To this end, this research calculates four external and three internal activity space indicators based on Weibo data from three Chinese cities. We compared the strengths and weaknesses of these indicators for approximating user activity spaces from LBSM data with a low sampling resolution. We also tested how different amounts of check-in data affect the calculation of these activity space indicators. The results provide a useful reference for future experimental design in human activity modeling based on social media data.

Traffic Information Mining From Social Media Based on the MC-LSTM-Conv Model

Social media (e.g., Sina Weibo) have the advantage of reflecting traffic information, including the reasons for jams, illegal behaviors, and emergency recourses on roads. However, there remains a challenging issue regarding how to sufficiently mine traffic information. In this paper, we propose a deep learning-based method that uses social media data for traffic jam management. The core ideas of the proposed method are twofold. First, a multichannel network with a Long Short-Term Memory layer (LSTM-layer) and a Convolution layer (Conv-layer) (termed as MC-LSTM-Conv) is proposed. This model consists of two information channels for extracting abstract features from input text. Each channel includes two Conv-layers, and an LSTM-layer is added to one of the four Conv-layers. The MC-LSTM-Conv model is used to extract check-in microblogs reflecting traffic jams from mass Sina Weibo data. Second, a series of matching rules are constructed based on the keywords that are related to traffic-jam scenes. These rules further classify the microblogs extracted by the first step into four classes, and each of the classes reflects a specific road condition (i.e., traffic accidents or large-scale activities, road construction, traffic lights, and the low efficiency of government agencies). Experiments on Sina Weibo data demonstrate that the proposed multichannel network has superior performance in extracting microblogs about traffic jams. The keyword fuzzy matching method can fetch detailed information about traffic jams efficiently.

Research on Construction Method of Urban Simulated Rainfall Station Based on Sina Weibo Geographical Location Data

In recent years, with the continuous improvement of the level of urbanization, the phenomenon of urban infighting has intensified. At present, urban rainfall data is mainly measured by rainfall stations, while the number of traditional rainfall stations and their uneven distribution result in the inability to obtain high-precision surface rainfall data. With the advent of the era of big data, more and more experts and scholars have applied big data to the research of natural disasters. Therefore, this article uses web crawler technology to obtain Sina Weibo data with geographic location information. By analyzing the correlation between the number of micro-blogs related to rainfall and the rainfall of the field, establishing the relationship between the two functions, and constructing the simulated rainfall station in the urban area of Zhengzhou City, more refined surface rainfall data can be obtained by interpolation. The experimental results show that the method of construction of simulated rainfall station can effectively improve the accuracy of interpolation through traditional rainfall stations.

Categorization of Green Spaces for a Sustainable Environment and Smart City Architecture by Utilizing Big Data

Urban green spaces promote outdoor activities and social interaction, which make a significant contribution to the health and well-being of residents. This study presents an approach that focuses on the real spatial and temporal behavior of park visitors in different categories of green parks. We used the large dataset available from the Chinese micro-blog Sina Weibo (often simply referred to as “Weibo”) to analyze data samples, in order to describe the behavioral patterns of millions of people with access to green spaces. We select Shanghai as a case study because urban residential segregation has already taken place, which was expected to be followed by concerns of environmental sustainability. In this research, we utilized social media check-in data to measure and compare the number of visitations to different kinds of green parks. Furthermore, we divided the green spaces into different categories according to their characteristics, and our main findings were: (1) the most popular category based upon the check-in data; (2) changes in the number of visitors according to the time of day; (3) seasonal impacts on behavior in public in relation to the different categories of parks; and (4) gender-based differences. To the best of our knowledge, this is the first study carried out in Shanghai utilizing Weibo data to focus upon the categorization of green space. It is also the first to offer recommendations for planners regarding the type of facilities they should provide to residents in green spaces, and regarding the sustainability of urban environments and smart city architecture.

Exploring Urban Spatial Features of COVID-19 Transmission in Wuhan Based on Social Media Data

During the early stage of the COVID-19 outbreak in Wuhan, there was a short run of medical resources, and Sina Weibo, a social media platform in China, built a channel for novel coronavirus pneumonia patients to seek help. Based on the geo-tagging Sina Weibo data from February 3rd to 12th, 2020, this paper analyzes the spatiotemporal distribution of COVID-19 cases in the main urban area of Wuhan and explores the urban spatial features of COVID-19 transmission in Wuhan. The results show that the elderly population accounts for more than half of the total number of Weibo help seekers, and a close correlation between them has also been found in terms of spatial distribution features, which confirms that the elderly population is the group of high-risk and high-prevalence in the COVID-19 outbreak, needing more attention of public health and epidemic prevention policies. On the other hand, the early transmission of COVID-19 in Wuhan could be divide into three phrases: Scattered infection, community spread, and full-scale outbreak. This paper can help to understand the spatial transmission of COVID-19 in Wuhan, so as to propose an effective public health preventive strategy for urban space optimization.