scholarly journals Spatio-Temporal Patterns of Fitness Behavior In Beijing Based on Social Media Data

2021 ◽  
Author(s):  
Tian Bin ◽  
Meng Bin ◽  
Zhi Guoqing ◽  
Qi Zhenyu ◽  
Chen Siyu ◽  
...  
Keyword(s):  
Social Media ◽  
Spatial Distribution ◽  
Latent Dirichlet Allocation ◽  
Temporal Distribution ◽  
Temporal Patterns ◽  
Social Media Data ◽  
Periodic Distribution ◽  
Spatio Temporal ◽  
Southern City ◽  
Media Data

Abstract Using social media data, this paper employs FastAI, Latent Dirichlet Allocation (LDA) and other text mining techniques coupled with GIS spatial analysis methods to study temporal and spatial patterns of fitness behavior of residents in Beijing, China, from the perspective of residents’ daily behavior. Using LDA theme model technology, it is found that fitness activities can be divided into four types: running-based fitness; riding-based fitness; fitness in sports venue; and fitness under professional guidance. Emotional analysis revealed that, residents can get a better fitness experience in sports venues. There are also obvious differences in the spatio-temporal distribution of the different fitness behaviors. Fitness behavior of Beijing residents has a multi-center spatial distribution pattern, with a wide coverage in northern city areas but obvious aggregation areas in southern city areas. In terms of temporal patterns, the residents' fitness frequency shows an obvious periodic distribution (weekly and 24 hours). And there are obvious differences in the time distribution of fitness behaviors for each theme. Additionally, based on the attribution analysis of a geodetector, it is found that the spatial distribution of fitness behavior of residents is mainly affected by factors such as catering services, education and culture, companies and public facilities.

scholarly journals Real-time spatio-temporal event detection on geotagged social media

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Yasmeen George ◽  
Shanika Karunasekera ◽  
Aaron Harwood ◽  
Kwan Hui Lim
Keyword(s):  
New York ◽  
Social Media ◽  
Event Detection ◽  
Detection System ◽  
Time And Space ◽  
Social Media Data ◽  
Event Time ◽  
Spatio Temporal ◽  
Geographical Space ◽  
Media Data

AbstractA key challenge in mining social media data streams is to identify events which are actively discussed by a group of people in a specific local or global area. Such events are useful for early warning for accident, protest, election or breaking news. However, neither the list of events nor the resolution of both event time and space is fixed or known beforehand. In this work, we propose an online spatio-temporal event detection system using social media that is able to detect events at different time and space resolutions. First, to address the challenge related to the unknown spatial resolution of events, a quad-tree method is exploited in order to split the geographical space into multiscale regions based on the density of social media data. Then, a statistical unsupervised approach is performed that involves Poisson distribution and a smoothing method for highlighting regions with unexpected density of social posts. Further, event duration is precisely estimated by merging events happening in the same region at consecutive time intervals. A post processing stage is introduced to filter out events that are spam, fake or wrong. Finally, we incorporate simple semantics by using social media entities to assess the integrity, and accuracy of detected events. The proposed method is evaluated using different social media datasets: Twitter and Flickr for different cities: Melbourne, London, Paris and New York. To verify the effectiveness of the proposed method, we compare our results with two baseline algorithms based on fixed split of geographical space and clustering method. For performance evaluation, we manually compute recall and precision. We also propose a new quality measure named strength index, which automatically measures how accurate the reported event is.

scholarly journals EXTRACTING AND COMPARING PLACES USING GEO-SOCIAL MEDIA

2015 ◽  
Vol II-3/W5 ◽  
pp. 311-316
Author(s):  
F. O. Ostermann ◽  
H. Huang ◽  
G. Andrienko ◽  
N. Andrienko ◽  
C. Capineri ◽  
...  
Keyword(s):  
Social Media ◽  
Semantic Similarity ◽  
Data Set ◽  
Social Media Data ◽  
Temporal Clustering ◽  
Depth Study ◽  
Data Source ◽  
Spatio Temporal ◽  
Media Data

Increasing availability of Geo-Social Media (e.g. Facebook, Foursquare and Flickr) has led to the accumulation of large volumes of social media data. These data, especially geotagged ones, contain information about perception of and experiences in various environments. Harnessing these data can be used to provide a better understanding of the semantics of places. We are interested in the similarities or differences between different Geo-Social Media in the description of places. This extended abstract presents the results of a first step towards a more in-depth study of semantic similarity of places. Particularly, we took places extracted through spatio-temporal clustering from one data source (Twitter) and examined whether their structure is reflected semantically in another data set (Flickr). Based on that, we analyse how the semantic similarity between places varies over space and scale, and how Tobler's first law of geography holds with regards to scale and places.

scholarly journals Spatio-Temporal Machine Learning Analysis of Social Media Data and Refugee Movement Statistics

2021 ◽  
Vol 10 (8) ◽  
pp. 498
Author(s):  
Clemens Havas ◽  
Lorenz Wendlinger ◽  
Julian Stier ◽  
Sahib Julka ◽  
Veronika Krieger ◽  
...  
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Public Authorities ◽  
Social Media Data ◽  
Spatial Features ◽  
Syrian Civil War ◽  
Information Gap ◽  
The Status ◽  
Spatio Temporal ◽  
Media Data

In 2015, within the timespan of only a few months, more than a million people made their way from Turkey to Central Europe in the wake of the Syrian civil war. At the time, public authorities and relief organisations struggled with the admission, transfer, care, and accommodation of refugees due to the information gap about ongoing refugee movements. Therefore, we propose an approach utilising machine learning methods and publicly available data to provide more information about refugee movements. The approach combines methods to analyse the textual, temporal and spatial features of social media data and the number of arriving refugees of historical refugee movement statistics to provide relevant and up to date information about refugee movements and expected numbers. The results include spatial patterns and factual information about collective refugee movements extracted from social media data that match actual movement patterns. Furthermore, our approach enables us to forecast and simulate refugee movements to forecast an increase or decrease in the number of incoming refugees and to analyse potential future scenarios. We demonstrate that the approach proposed in this article benefits refugee management and vastly improves the status quo.

scholarly journals Identifying Public Perceptions toward Emerging Transportation Trends through Social Media-Based Interactions

2021 ◽  
Vol 1 (3) ◽  
pp. 794-813
Author(s):  
Md Rakibul Alam ◽  
Arif Mohaimin Sadri ◽  
Xia Jin
Keyword(s):  
Social Media ◽  
Built Environment ◽  
Large Scale ◽  
User Fees ◽  
Data Driven ◽  
Social Media Data ◽  
Shared Mobility ◽  
Vehicle Technology ◽  
Spatio Temporal ◽  
Media Data

The objective of this study is to mine and analyze large-scale social media data (rich spatio-temporal data unlike traditional surveys) and develop comparative infographics of emerging transportation trends and mobility indicators by adopting natural language processing and data-driven techniques. As such, first, around 13 million tweets for about 20 days (16 December 2019–4 January 2020) from North America were collected, and tweets closely aligned with emerging transportation and mobility trends (such as shared mobility, vehicle technology, built environment, user fees, telecommuting, and e-commerce) were identified. Data analytics captured spatio-temporal differences in social media user interactions and concerns about such trends, as well as topics of discussions formed through such interactions. California, Florida, Georgia, Illinois, New York are among the highly visible cities discussing such trends. Being positive overall, people carried more positive views on shared mobility, vehicle technology, telecommuting, and e-commerce, while being more negative on user fees, and the built environment. Ride-hailing, fuel efficiency, trip navigation, daily as well as shopping and recreational activities, gas price, tax, and product delivery were among the emergent topics. The social media data-driven framework would allow real-time monitoring of transportation trends by agencies, researchers, and professionals.

scholarly journals Normalization Strategies for Enhancing Spatio-Temporal Analysis of Social Media Responses during Extreme Events: A Case Study based on Analysis of Four Extreme Events using Socio-Environmental Data Explorer (SEDE)

2017 ◽  
Vol IV-4/W2 ◽  
pp. 139-146
Author(s):  
J. Ajayakumar ◽  
E. Shook ◽  
V. K. Turner
Keyword(s):  
Social Media ◽  
Extreme Events ◽  
The United States ◽  
Environmental Data ◽  
Data Sources ◽  
Social Media Data ◽  
Public Response ◽  
Spatio Temporal ◽  
Media Data ◽  
Media Responses

With social media becoming increasingly location-based, there has been a greater push from researchers across various domains including social science, public health, and disaster management, to tap in the spatial, temporal, and textual data available from these sources to analyze public response during extreme events such as an epidemic outbreak or a natural disaster. Studies based on demographics and other socio-economic factors suggests that social media data could be highly skewed based on the variations of population density with respect to place. To capture the spatio-temporal variations in public response during extreme events we have developed the Socio-Environmental Data Explorer (SEDE). SEDE collects and integrates social media, news and environmental data to support exploration and assessment of public response to extreme events. For this study, using SEDE, we conduct spatio-temporal social media response analysis on four major extreme events in the United States including the “North American storm complex” in December 2015, the “snowstorm Jonas” in January 2016, the “West Virginia floods” in June 2016, and the “Hurricane Matthew” in October 2016. Analysis is conducted on geo-tagged social media data from Twitter and warnings from the storm events database provided by National Centers For Environmental Information (NCEI) for analysis. Results demonstrate that, to support complex social media analyses, spatial and population-based normalization and filtering is necessary. The implications of these results suggests that, while developing software solutions to support analysis of non-conventional data sources such as social media, it is quintessential to identify the inherent biases associated with the data sources, and adapt techniques and enhance capabilities to mitigate the bias. The normalization strategies that we have developed and incorporated to SEDE will be helpful in reducing the population bias associated with social media data and will be useful for researchers and decision makers to enhance their analysis on spatio-temporal social media responses during extreme events.

scholarly journals A Novel Framework for Mining Social Media Data Based on Text Mining, Topic Modeling, Random Forest, and DANP Methods

Mathematics ◽  
2021 ◽  
Vol 9 (17) ◽  
pp. 2041
Author(s):  
Chi-Yo Huang ◽  
Chia-Lee Yang ◽  
Yi-Hao Hsiao
Keyword(s):  
Decision Making ◽  
Social Media ◽  
Random Forest ◽  
Social Theory ◽  
Latent Dirichlet Allocation ◽  
Theoretical Framework ◽  
Causal Relationships ◽  
Social Media Data ◽  
Feature Importance ◽  
Media Data

The huge volume of user-generated data on social media is the result of the aggregation of users’ personal backgrounds, past experiences, and daily activities. This huge size of the generated data, the so-called “big data,” has been studied and investigated intensively during the past few years. In spite of the impression one may get from the media, a great deal of data processing has not been uncovered by existing techniques of data engineering and processing. However, very few scholars have tried to do so, especially from the perspective of multiple-criteria decision-making (MCDM). These MCDM methods can derive influence relationships and weights associated with aspects and criteria, which can hardly be achieved by traditional data analytics and statistical approaches. Therefore, in this paper, we aim to propose an analytic framework to mine social networks, feed the meaningful information via MCDM methods based on a theoretical framework, derive causal relationships among the aspects of the theoretical framework, and finally compare the causal relationships with a social theory. Latent Dirichlet allocation (LDA) will be adopted to derive topic models based on the data retrieved from social media. By clustering the topics into aspects of the social theory, the probability associated with each aspect will be normalized and then transformed to a Likert-type 5-point scale. Afterwards, for every topic, the feature importance of all other topics will be derived using the random forest (RF) algorithm. The feature importance matrix will be transformed to the initial influence matrix of the decision-making trial and evaluation laboratory (DEMATEL). The influence relationships among the aspects and criteria and influence weights can then be derived by using the DEMATEL-based analytic network process (DANP). The influence weight versus each criterion can be derived by using DANP. To verify the feasibility of the proposed framework, Taiwanese users’ attitudes toward air pollution will be analyzed based on the value–belief–norm (VBN) theory by using social media data retrieved from Dcard (dcard.tw). Based on the analytic results, the causal relationships are fully consistent with the VBN framework. Further, the mutual influences derived in this work that were seldom discussed by earlier works, i.e., the mutual influences between altruistic concerns and egoistic concerns, as well as those between altruistic concerns and biosphere concerns, are worth further investigation in future.

scholarly journals Topic modeling to mind illegal compensation for occupational injuries

2019 ◽  
Vol 29 (Supplement_4) ◽  
Author(s):  
S H Song ◽  
J Y Min ◽  
H J Kim ◽  
K B Min
Keyword(s):  
Social Media ◽  
Topic Modeling ◽  
Social Insurance ◽  
Latent Dirichlet Allocation ◽  
Occupational Injuries ◽  
Workplace Safety ◽  
Body Parts ◽  
Insurance Claims ◽  
Social Media Data ◽  
Media Data

Abstract Background Accurate reports of occupational injuries are important to monitor workplace safety and health initiatives. In South Korea, media reports, experts, and workers have been constantly raising the issue of underreporting. Supposedly it is because employers have strong market “incentives” by underreporting their employees’ injuries. A critical way to underreport or cover-up is illegal compensation (in Korean called “gong-sang”). Unfortunately, “gong-sang” is not counted as official occupational injury statistics. The aim of this study was to analyze the social media data using topic modeling and to explore issues surrounding “gong-sang”. Methods We used web scraping technology and collected 2,210 social media data from Web search engines. Data was processed to transform unstructured textual documents into structured data using the Python and applied Latent Dirichlet allocation (LDA) in the Python library, Gensim, for topic modeling. Results Based on the LDA method from “gong-sang”- related documentation, 10 topics were identified. Topic 1 was the greatest concern (60.5%), with keywords implying the choice between illegal compensation (“gong-sang”) and legal insurance claims. The next concern was Topic 2 including keywords associated with claims for industrial accident insurance benefits. The rest topics (topic 3-10) showed the monetary issue, precarious employment, and vulnerable body parts to “gong-sang”. Conclusions We explored web-based data and identified the salient issues surrounding “gong-sang”. LDA topics may be helpful to ensure efficient occupational health and safety scheme to protect vulnerable employees from “gong-sang” practices. Key messages The topics formulated by LDA included queries about legal insurance claims. Legal insurance claims including private or social insurance, monetary compensation, injured body parts, and the type of jobs vulnerable to “gong-sang”.

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