scholarly journals Big Data Analysis to Observe Check-in Behavior Using Location-Based Social Media Data

Information ◽  
2018 ◽  
Vol 9 (10) ◽  
pp. 257 ◽  
Author(s):  
Muhammad Rizwan ◽  
Wanggen Wan
Keyword(s):  
Social Media ◽  
Kernel Density ◽  
Big Data Analysis ◽  
Location Based Services ◽  
Social Media Data ◽  
Sina Weibo ◽  
Density Surface ◽  
Location Based Social Network ◽  
Gender Based ◽  
Media Data

With rapid advancement in location-based services (LBS), their acquisition has become a powerful tool to link people with similar interests across long distances, as well as connecting family and friends. To observe human behavior towards using social media, it is essential to understand and measure the check-in behavior towards a location-based social network (LBSN). This check-in phenomenon of sharing location, activities, and time by users has encouraged this research on the frequency of using an LBSN. In this paper, we investigate the check-in behavior of several million individuals, for whom we observe the gender and their frequency of using Chinese microblog Sina Weibo (referred as “Weibo”) over a period in Shanghai, China. To produce a smooth density surface of check-ins, we analyze the overall spatial patterns by using the kernel density estimation (KDE) by using ArcGIS. Furthermore, our results reveal that female users are more inclined towards using social media, and a difference in check-in behavior during weekday and weekend is also observed. From the results, LBSN data seems to be a complement to traditional methods (i.e., survey, census) and is used to study gender-based check-in behavior.

scholarly journals A Novel Machine Learning Framework for Comparison of Viral COVID-19–Related Sina Weibo and Twitter Posts: Workflow Development and Content Analysis

10.2196/24889 ◽  
2021 ◽  
Vol 23 (1) ◽  
pp. e24889
Author(s):  
Shi Chen ◽  
Lina Zhou ◽  
Yunya Song ◽  
Qian Xu ◽  
Ping Wang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Public Discourse ◽  
Analytical Framework ◽  
Health Issues ◽  
Social Media Data ◽  
Sina Weibo ◽  
Social Media Platforms ◽  
Media Data ◽  
Content Feature

Background Social media plays a critical role in health communications, especially during global health emergencies such as the current COVID-19 pandemic. However, there is a lack of a universal analytical framework to extract, quantify, and compare content features in public discourse of emerging health issues on different social media platforms across a broad sociocultural spectrum. Objective We aimed to develop a novel and universal content feature extraction and analytical framework and contrast how content features differ with sociocultural background in discussions of the emerging COVID-19 global health crisis on major social media platforms. Methods We sampled the 1000 most shared viral Twitter and Sina Weibo posts regarding COVID-19, developed a comprehensive coding scheme to identify 77 potential features across six major categories (eg, clinical and epidemiological, countermeasures, politics and policy, responses), quantified feature values (0 or 1, indicating whether or not the content feature is mentioned in the post) in each viral post across social media platforms, and performed subsequent comparative analyses. Machine learning dimension reduction and clustering analysis were then applied to harness the power of social media data and provide more unbiased characterization of web-based health communications. Results There were substantially different distributions, prevalence, and associations of content features in public discourse about the COVID-19 pandemic on the two social media platforms. Weibo users were more likely to focus on the disease itself and health aspects, while Twitter users engaged more about policy, politics, and other societal issues. Conclusions We extracted a rich set of content features from social media data to accurately characterize public discourse related to COVID-19 in different sociocultural backgrounds. In addition, this universal framework can be adopted to analyze social media discussions of other emerging health issues beyond the COVID-19 pandemic.

scholarly journals Crowd Detection in Mass Gatherings Based on Social Media Data: A Case Study of the 2014 Shanghai New Year’s Eve Stampede

2020 ◽  
Vol 17 (22) ◽  
pp. 8640
Author(s):  
Jiexiong Duan ◽  
Weixin Zhai ◽  
Chengqi Cheng
Keyword(s):  
Social Media ◽  
Topic Modeling ◽  
Public Attitudes ◽  
Three Dimensions ◽  
Social Media Data ◽  
Sina Weibo ◽  
Mass Gatherings ◽  
New Year’S Eve ◽  
Media Data

The Shanghai New Year’s Eve stampede on 31 December 2014, caused 36 deaths and 47 other injuries, generating attention from around the world. This research aims to explore crowd aggregation from the perspective of Sina Weibo check-in data and evaluate the potential of crowd detection based on social media data. We develop a framework using Weibo check-in data in three dimensions: the aggregation level of check-in data, the topic changes in posts and the sentiment fluctuations of citizens. The results show that the numbers of check-ins in all of Shanghai on New Years’ Eve is twice that of other days and that Moran’s I reaches a peak on this date, implying a spatial autocorrelation mode. Additionally, the results of topic modeling indicate that 72.4% of the posts were related to the stampede, reflecting public attitudes and views on this incident from multiple angles. Moreover, sentiment analysis based on Weibo posts illustrates that the proportion of negative posts increased both when the stampede occurred (40.95%) and a few hours afterwards (44.33%). This study demonstrates the potential of using geotagged social media data to analyze population spatiotemporal activities, especially in emergencies.

scholarly journals Social Media Data-Based Sentiment Analysis of Tourists’ Air Quality Perceptions

2019 ◽  
Vol 11 (18) ◽  
pp. 5070 ◽  
Author(s):  
Yuguo Tao ◽  
Feng Zhang ◽  
Chunyun Shi ◽  
Yun Chen
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Content Analysis ◽  
Air Quality ◽  
Sentiment Analysis ◽  
Social Media Data ◽  
Sina Weibo ◽  
Emotion Words ◽  
Tourist Destinations ◽  
Media Data

Analyzing tourists’ perceptions of air quality is of great significance to the study of tourist experience satisfaction and the image construction of tourism destinations. In this study, using the web crawler technique, we collected 27,500 comments regarding the air quality of 195 of China’s Class 5A tourist destinations posted by tourists on Sina Weibo from January 2011 to December 2017; these comments were then subjected to a content analysis using the Gooseeker, ROST CM (Content Mining System) and BosonNLP (Natural Language Processing) tools. Based on an analysis of the proportions of sentences with different emotional polarities with ROST EA (Emotion Analysis), we measured the sentiment value of texts using the artificial neural network (ANN) machine learning method implemented through a Chinese social media data-oriented Boson platform based on the Python programming language. The content analysis results indicated that in the adaption stage in Sina Weibo, tourists’ perceptions of air quality were mainly positive and had poor air pollution crisis awareness. Objective emotion words exhibited a similarly high proportion as subjective emotion words, indicating that taking both objective and subjective emotion words into account simultaneously helps to comprehensively understand the emotional content of the comments. The sentiment analysis results showed that for the entire text, sentences with positive emotions accounted for 85.53% of the total comments, with a sentiment value of 0.786, which belonged to the positive medium level; the direction of the temporal “up-down-up” changes and the spatial pattern of high in the south and low in the north (while having little difference between the east and the west) were basically consistent with reality. A further exploration of the theoretical basis of the semi-supervised ANN approach or the introduction of other machine learning methods using different data sources will help to analyze this phenomenon in greater depth. The paper provides evidence for new data and methods for air quality research in tourist destinations and provides a new tool for air quality monitoring.

scholarly journals Solving Competitive Location Problems with Social Media Data Based on Customers’ Local Sensitivities

2019 ◽  
Vol 8 (5) ◽  
pp. 202 ◽  
Author(s):  
Wei Jiang ◽  
Yandong Wang ◽  
Mingxuan Dou ◽  
Senbao Liu ◽  
Shiwei Shao ◽  
...  
Keyword(s):  
Social Media ◽  
Cost Effective ◽  
Optimal Location ◽  
Location Based Services ◽  
Location Problems ◽  
Competitive Location ◽  
Test Cases ◽  
Social Media Data ◽  
Location Data ◽  
Media Data

Competitive location problems (CLPs) are a crucial business concern. Evaluating customers’ sensitivities to different facility attractions (such as distance and business area) is the premise for solving a CLP. Currently, the development of location-based services facilitates the use of location data for sensitivity evaluations. Most studies based on location data assumed the customers’ sensitivities to be global and constant over space. In this paper, we proposed a new method of using social media data to solve competitive location problems based on the evaluation of customers’ local sensitivities. Regular units were first designed to spatially aggregate social media data to extract samples with uniform spatial distribution. Then, geographically weighted regression (GWR) and the Huff model were combined to evaluate local sensitivities. By applying the evaluation results, the captures for different feasible locations were calculated, and the optimal location for a new retail facility could be determined. In our study, the five largest retail agglomerations in Beijing were taken as test cases, and a possible new retail agglomeration was located. The results of our study can help people have a better understanding of the spatial variation of customers’ local sensitivities. In addition, our results indicate that our method can solve competitive location problems in a cost-effective way.

Geo-social media data analytic for user modeling and location-based services

2016 ◽  
Vol 7 (3) ◽  
pp. 11-18 ◽  
Author(s):  
Jie Bao ◽  
Defu Lian ◽  
Fuzheng Zhang ◽  
Nicholas Jing Yuan
Keyword(s):  
Social Media ◽  
User Modeling ◽  
Location Based Services ◽  
Social Media Data ◽  
Media Data ◽  
Data Analytic

scholarly journals A three-layer model on users’ interests mining

2017 ◽  
Vol 44 (1) ◽  
pp. 136-144 ◽  
Author(s):  
Renfeng Yang ◽  
Wenbo Xie ◽  
Duanbing Chen
Keyword(s):  
Social Media ◽  
Big Data ◽  
Layer Model ◽  
Short Term ◽  
Social Media Data ◽  
Sina Weibo ◽  
Hit Rate ◽  
The Social ◽  
Media Data

With the advent of big data era, social media plays an important role in many areas such as security and finance. Researchers pay more attention on mining users’ interests through the social media data. A three-layer model (TLM) based on keyword extracting is proposed to mine users’ interests, which includes candidate words extracting, semantic structures analysing and interest words ranking. The TLM mainly focuses on both self-importance and semantic-importance of interest words. In addition, the TLM also considers the interest drifting to track long-term and short-term interests of users. Experiments conducted on 10 SINA Weibo datasets show that TLM is more efficient than existing methods to identify users’ interests based on hit rate.

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

2021 ◽  
Vol 1 (1) ◽  
Author(s):  
Mengtong Wu ◽  
Chao Jiang ◽  
Yi Zhang ◽  
Jingjing Cao ◽  
Ying Cheng ◽  
...  
Keyword(s):  
Social Media ◽  
Empirical Studies ◽  
Chinese Mainland ◽  
Destination Image ◽  
Social Media Data ◽  
Sina Weibo ◽  
Tourism Marketing ◽  
Major Factors ◽  
Q Statistic ◽  
Media Data

AbstractCulture 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.

scholarly journals Analyzing Social-Geographic Human Mobility Patterns Using Large-Scale Social Media Data

2020 ◽  
Vol 9 (2) ◽  
pp. 125 ◽  
Author(s):  
Zeinab Ebrahimpour ◽  
Wanggen Wan ◽  
José Luis Velázquez García ◽  
Ofelia Cervantes ◽  
Li Hou
Keyword(s):  
Social Media ◽  
Large Scale ◽  
City Planning ◽  
Smart Cities ◽  
Human Mobility ◽  
Location Based Services ◽  
Mobility Patterns ◽  
Social Media Data ◽  
Planning Decisions ◽  
Media Data

Social media data analytics is the art of extracting valuable hidden insights from vast amounts of semi-structured and unstructured social media data to enable informed and insightful decision-making. Analysis of social media data has been applied for discovering patterns that may support urban planning decisions in smart cities. In this paper, Weibo social media data are used to analyze social-geographic human mobility in the CBD area of Shanghai to track citizen’s behavior. Our main motivation is to test the validity of geo-located Weibo data as a source for discovering human mobility and activity patterns. In addition, our goal is to identify important locations in people’s lives with the support of location-based services. The algorithms used are described and the results produced are presented using adequate visualization techniques to illustrate the detected human mobility patterns obtained by the large-scale social media data in order to support smart city planning decisions. The outcome of this research is helpful not only for city planners, but also for business developers who hope to extend their services to citizens.

scholarly journals How Location-Based Social Network (LBSN) Data Contribute to Contemporary Urban Development

2021 ◽  
Vol 3 (1) ◽  
Author(s):  
Yanbo Wu ◽  
Xiaoxiang Zhu
Keyword(s):  
Social Media ◽  
Social Network ◽  
Urban Development ◽  
Social Media Data ◽  
Internet Technologies ◽  
New Ideas ◽  
Data Source ◽  
Location Based Social Network ◽  
Privacy Issues ◽  
Media Data

<div>In recent years, social media has created a large amount of new data due to the development of Internet technologies. Scholars in related fields focus a lot on the location-based social network (LBSN) and data generated from LBSN to provide new ideas for urban development. This research analyses LBSN data advantages, including the advanced data source, diversity of LBSN platforms, and LBSN data contents. Challenges of using social media data like deviation in data samples, privacy issues and technical barrier are also covered. Last but not least, this essay will discuss the applications of LBSN data in urban design.</div>

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