Urbanization is one of the most impactful human activities across the world today affecting the quality of urban life and its sustainable development.Urbanization in Africa is occurring at an unprecedented rate and it...Urbanization is one of the most impactful human activities across the world today affecting the quality of urban life and its sustainable development.Urbanization in Africa is occurring at an unprecedented rate and it threatens the attainment of Sustainable Development Goals(SDGs).Urban sprawl has resulted in unsustainable urban development patterns from social,environmental,and economic perspectives.This study is among the first examples of research in Africa to combine remote sensing data with social media data to determine urban sprawl from 2011 to 2017 in Morogoro urban municipality,Tanzania.Random Forest(RF)method was applied to accomplish imagery classification and location-based social media(Twitter usage)data were obtained through a Twitter Application Programming Interface(API).Morogoro urban municipality was classified into built-up,vegetation,agriculture,and water land cover classes while the classification results were validated by the generation of 480 random points.Using the Kernel function,the study measured the location of Twitter users within a 1 km buffer from the center of the city.The results indicate that,expansion of the city(built-up land use),which is primarily driven by population expansion,has negative impacts on ecosystem services because pristine grasslands and forests which provide essential ecosystem services such as carbon sequestration and support for biodiversity have been replaced by built-up land cover.In addition,social media usage data suggest that there is the concentration of Twitter usage within the city center while Twitter usage declines away from the city center with significant spatial and numerical increase in Twitter usage in the study area.The outcome of the study suggests that the combination of remote sensing,social sensing,and population data were useful as a proxy/inference for interpreting urban sprawl and status of access to urban services and infrastructure in Morogoro,and Africa city where data for urban planning is often unavailable,inaccurate,or stale.展开更多
As the pivotal green space,urban parks play an important role in urban residents’daily activities.Thy can not only bring people physical health,but also can be more likely to elicit positive sentiment to those who vi...As the pivotal green space,urban parks play an important role in urban residents’daily activities.Thy can not only bring people physical health,but also can be more likely to elicit positive sentiment to those who visit them.Recently,social media big data has provided new data sources for sentiment analysis.However,there was limited researches that explored the connection between urban parks and individual’s sentiments.Therefore,this study firstly employed a pre-trained language model(BERT,Bidirectional Encoder Representations from Transformers)to calculate sentiment scores based on social media data.Secondly,this study analysed the relationship between urban parks and individual’s sentiment from both spatial and temporal perspectives.Finally,by utilizing structural equation model(SEM),we identified 13 factors and analyzed its degree of the influence.The research findings are listed as below:①It confirmed that individuals generally experienced positive sentiment with high sentiment scores in the majority of urban parks;②The urban park type showed an influence on sentiment scores.In this study,higher sentiment scores observed in Eco-parks,comprehensive parks,and historical parks;③The urban parks level showed low impact on sentiment scores.With distinctions observed mainly at level-3 and level-4;④Compared to internal factors in parks,the external infrastructure surround them exerted more significant impact on sentiment scores.For instance,number of bus and subway stations around urban parks led to higher sentiment scores,while scenic spots and restaurants had inverse result.This study provided a novel method to quantify the services of various urban parks,which can be served as inspiration for similar studies in other cities and countries,enhancing their park planning and management strategies.展开更多
Social media plays a crucial role in the organization of massive social movements. However, the sheer quantity of data generated by the events as well as the data collection restrictions that researchers encounter, le...Social media plays a crucial role in the organization of massive social movements. However, the sheer quantity of data generated by the events as well as the data collection restrictions that researchers encounter, leads to a series of challenges for researchers who want to analyze dynamic public discourse and opinion in response to and in the creation of world events. In this paper we present gatherTweet, a Python package that helps researchers efficiently collect social media data for events that are composed of many decentralized actions (across both space and time). The package is useful for studies that require analysis of the organizational or baseline messaging before an action, the action itself, and the effects of the action on subsequent public discourse. By capturing these aspects of world events gatherTweet enables the study of events and actions like protests, natural disasters, and elections.展开更多
This study aims to conduct an in-depth analysis of social media data using causal inference methods to explore the underlying mechanisms driving user behavior patterns.By leveraging large-scale social media datasets,t...This study aims to conduct an in-depth analysis of social media data using causal inference methods to explore the underlying mechanisms driving user behavior patterns.By leveraging large-scale social media datasets,this research develops a systematic analytical framework that integrates techniques such as propensity score matching,regression analysis,and regression discontinuity design to identify the causal effects of content characteristics,user attributes,and social network structures on user interactions,including clicks,shares,comments,and likes.The empirical findings indicate that factors such as sentiment,topical relevance,and network centrality have significant causal impacts on user behavior,with notable differences observed among various user groups.This study not only enriches the theoretical understanding of social media data analysis but also provides data-driven decision support and practical guidance for fields such as digital marketing,public opinion management,and digital governance.展开更多
This paper is concerned with anisotropic effects on seismic data and signal analysis for transversely isotropic rock media with vertical anisotropy. It is understood that these effects are significant in many practica...This paper is concerned with anisotropic effects on seismic data and signal analysis for transversely isotropic rock media with vertical anisotropy. It is understood that these effects are significant in many practical applications, e.g. earthquake forecasting, materials exploration inside the Earth’s crust, as well as various practical works in oil industry. Under the framework of the most accepted anisotropic media model (i.e. VTI media, transverse isotropy with a vertical axis symmetry), with applications of a set of available anisotropic rock parameters for sandstone and shale, we have performed numerical calculations of the anisotropic effects. We show that for rocks with strong anisotropy, the induced relative depth error can be significantly large. Nevertheless, with an improved understanding of the seismic-signal propagation and proper data processing, the error can be reduced, which in turn may enhance the probability of forecasting accurately the various wave propagations inside the Earth’s crust, e.g. correctly forecasting the incoming earthquakes from the center of the Earth.展开更多
COVID-19 posed challenges for global tourism management.Changes in visitor temporal and spatial patterns and their associated determinants pre-and peri-pandemic in Canadian Rocky Mountain National Parks are analyzed.D...COVID-19 posed challenges for global tourism management.Changes in visitor temporal and spatial patterns and their associated determinants pre-and peri-pandemic in Canadian Rocky Mountain National Parks are analyzed.Data was collected through social media programming and analyzed using spatiotemporal analysis and a geographically weighted regression(GWR)model.Results highlight that COVID-19 significantly changed park visitation patterns.Visitors tended to explore more remote areas peri-pandemic.The GWR model also indicated distance to nearby trails was a significant influence on visitor density.Our results indicate that the pandemic influenced tourism temporal and spatial imbalance.This research presents a novel approach using combined social media big data which can be extended to the field of tourism management,and has important implications to manage visitor patterns and to allocate resources efficiently to satisfy multiple objectives of park management.展开更多
In marine seismic exploration, ocean-bottom cable techniques accurately record the multicomponent seismic wavefield; however, the seismic wave propagation in fluid–solid media cannot be simulated by a single wave equ...In marine seismic exploration, ocean-bottom cable techniques accurately record the multicomponent seismic wavefield; however, the seismic wave propagation in fluid–solid media cannot be simulated by a single wave equation. In addition, when the seabed interface is irregular, traditional finite-difference schemes cannot simulate the seismic wave propagation across the irregular seabed interface. Therefore, an acoustic–elastic forward modeling and vector-based P-and S-wave separation method is proposed. In this method, we divide the fluid–solid elastic media with irregular interface into orthogonal grids and map the irregular interface in the Cartesian coordinates system into a horizontal interface in the curvilinear coordinates system of the computational domain using coordinates transformation. The acoustic and elastic wave equations in the curvilinear coordinates system are applied to the fluid and solid medium, respectively. At the irregular interface, the two equations are combined into an acoustic–elastic equation in the curvilinear coordinates system. We next introduce a full staggered-grid scheme to improve the stability of the numerical simulation. Thus, separate P-and S-wave equations in the curvilinear coordinates system are derived to realize the P-and S-wave separation method.展开更多
The united front work is an important part of the general pattern of united front work, and it is an important part of the party building in colleges and universities. It involves broad, comprehensive, democratic, non...The united front work is an important part of the general pattern of united front work, and it is an important part of the party building in colleges and universities. It involves broad, comprehensive, democratic, non-party intellectuals, Religion, returned overseas Chinese, Taiwan compatriots and Taiwan overseas students and other aspects of the task, is the microcosm of the united front work and the window. Colleges and universities is an important base for cultivating talents. Teachers ‘teaching level and ideology and morality all directly affect young students’ outlook on life and values. Fully mobilize the enthusiasm and creativity of non-party people, is conducive to schools to complete the dissemination of knowledge, training the sacred mission of talent, help to promote the work of the school center to complete. The wide application of new media has provided new opportunities and new challenges for the united front work in colleges and universities.展开更多
This article introduces a novel low rank approximation (LRA)-based model to detect the functional regions with the data from about 15 million social media check-in records during a year-long period in Shanghai, China....This article introduces a novel low rank approximation (LRA)-based model to detect the functional regions with the data from about 15 million social media check-in records during a year-long period in Shanghai, China. We identified a series of latent structures, named latent spatio-temporal activity structures. While interpreting these structures, we can obtain a series of underlying associations between the spatial and temporal activity patterns. Moreover, we can not only reproduce the observed data with a lower dimensional representative, but also project spatio-temporal activity patterns in the same coordinate system. With the K-means clustering algorithm, five significant types of clusters that are directly annotated with a combination of temporal activities can be obtained, providing a clear picture of the correlation between the groups of regions and different activities at different times during a day. Besides the commercial and transportation dominant areas, we also detected two kinds of residential areas, the developed residential areas and the developing residential areas.We further interpret the spatial distribution of these clusters using urban form analytics. The results are highly consistent with the government planning in the same periods, indicating that our model is applicable to infer the functional regions from social media check-in data and can benefit a wide range of fields, such as urban planning, public services, and location-based recommender systems.展开更多
In light of the rapid growth and development of social media, it has become the focus of interest in many different scientific fields. They seek to extract useful information from it, and this is called (knowledge), s...In light of the rapid growth and development of social media, it has become the focus of interest in many different scientific fields. They seek to extract useful information from it, and this is called (knowledge), such as extracting information related to people’s behaviors and interactions to analyze feelings or understand the behavior of users or groups, and many others. This extracted knowledge has a very important role in decision-making, creating and improving marketing objectives and competitive advantage, monitoring events, whether political or economic, and development in all fields. Therefore, to extract this knowledge, we need to analyze the vast amount of data found within social media using the most popular data mining techniques and applications related to social media sites.展开更多
This research is about the nuisances of social media applications on a Wi-Fi network at a university campus in Ghana. The aim was to access the security risk on the network, the speed of the network, and the data cons...This research is about the nuisances of social media applications on a Wi-Fi network at a university campus in Ghana. The aim was to access the security risk on the network, the speed of the network, and the data consumption of those platforms on the network. Network Mapper (Nmap Zenmap) Graphical User Interface 7.80 application was used to scan the various social media platforms to identify the protocols, ports, services, etc. to enable in accessing the vulnerability of the network. Data consumption of users’ mobile devices was collected and analyzed. Device Accounting (DA) based on the various social media applications was used. The results of the analysis revealed that the network is prone to attacks due to the nature of the protocols, ports, and services on social media applications. The numerous users with average monthly data consumption per user of 4 gigabytes, 300 megabytes on social media alone are a clear indication of high traffic as well as the cost of maintaining the network. A URL filtering of the social media websites was proposed on Rockus Outdoor AP to help curb the nuisance.展开更多
The World Wide Web provides a wealth of information about everything, including contemporary audio and visual art events, which are discussed on media outlets, blogs, and specialized websites alike. This information m...The World Wide Web provides a wealth of information about everything, including contemporary audio and visual art events, which are discussed on media outlets, blogs, and specialized websites alike. This information may become a robust source of real-world data, which may form the basis of an objective data-driven analysis. In this study, a methodology for collecting information about audio and visual art events in an automated manner from a large array of websites is presented in detail. This process uses cutting edge Semantic Web, Web Search and Generative AI technologies to convert website documents into a collection of structured data. The value of the methodology is demonstrated by creating a large dataset concerning audiovisual events in Greece. The collected information includes event characteristics, estimated metrics based on their text descriptions, outreach metrics based on the media that reported them, and a multi-layered classification of these events based on their type, subjects and methods used. This dataset is openly provided to the general and academic public through a Web application. Moreover, each event’s outreach is evaluated using these quantitative metrics, the results are analyzed with an emphasis on classification popularity and useful conclusions are drawn concerning the importance of artistic subjects, methods, and media.展开更多
With the emergence of new types of data(e.g.social media data)and cutting-edge computer technology(e.g.Natural Language Processing),the shortcomings of traditional methods(subjective and objective ways)for de-tecting ...With the emergence of new types of data(e.g.social media data)and cutting-edge computer technology(e.g.Natural Language Processing),the shortcomings of traditional methods(subjective and objective ways)for de-tecting urban livability can be overcome by an integrated approach.This study aims to develop a comprehensive approach to measure urban livability based on statistic data,geo-data(e.g.points of interest),questionnaires survey,and social media data(Instagram),from both objective and subjective angles.Hong Kong,as a city with a high level of urbanization and contrasting urban environments,is chosen as the study area in this research.Through this study,the question“which area of Hong Kong is more suitable for living”is answered by the visu-alization of GIS-based analysis.Also,the correlation between livability scores and individuals’sentiment scores are explored.Specifically,the results show that central areas of Hong Kong with a higher level of urbanization are relatively more livable than suburban regions.However,through sentiment analysis,individuals who post Instagram in suburban areas of Hong Kong usually express more positive content and happier emotion than those who post Instagram in central urban areas.The study could offer useful information for the policy action of authorities as well as the residential location choices of citizens.展开更多
Objective To discuss how to use social media data for post-marketing drug safety monitoring in China as soon as possible by systematically combing the text mining applications,and to provide new ideas and methods for ...Objective To discuss how to use social media data for post-marketing drug safety monitoring in China as soon as possible by systematically combing the text mining applications,and to provide new ideas and methods for pharmacovigilance.Methods Relevant domestic and foreign literature was used to explore text classification based on machine learning,text mining based on deep learning(neural networks)and adverse drug reaction(ADR)terminology.Results and Conclusion Text classification based on traditional machine learning mainly include support vector machine(SVM)algorithm,naive Bayesian(NB)classifier,decision tree,hidden Markov model(HMM)and bidirectional en-coder representations from transformers(BERT).The main neural network text mining based on deep learning are convolution neural network(CNN),recurrent neural network(RNN)and long short-term memory(LSTM).ADR terminology standardization tools mainly include“Medical Dictionary for Regulatory Activities”(MedDRA),“WHODrug”and“Systematized Nomenclature of Medicine-Clinical Terms”(SNOMED CT).展开更多
The evolution of social media in the recent years promoted the appearance of a new category: social media based on check-in. It enables the user to define their identity through information sharing. This paper aims to...The evolution of social media in the recent years promoted the appearance of a new category: social media based on check-in. It enables the user to define their identity through information sharing. This paper aims to show the evolution of these media highlighting the effects and changes they cause in society through Super Bowl XLVI scenario, besides indicating the important role they have for the companies and marketing.展开更多
Digitalization is transforming governments across the globe. At the national level, down to regional and multiple departments in the public institutions, unprecedented change is occurring exponentially as a result of ...Digitalization is transforming governments across the globe. At the national level, down to regional and multiple departments in the public institutions, unprecedented change is occurring exponentially as a result of massive digitalization. Digitalization is compelling governments at all levels to embrace voluminous data and institute appropriate multi-channel platforms to support digital transformation. While this is the case, most governments have been caught unprepared thwarting maximum benefits spurred by digitalization. Inherently, the social media and e-participation tools for generating huge amount of data have convoluted most governments’ appetite in Big Data management. This situation is further compounded with the slow pace of adoption of these technological tools by citizens and the public sectors. For enhanced e-citizen satisfaction and engagement, as well as e-participation processes, public institutions need to promote engagement and collaboration. In view of advancing benefits to their citizens, public institutions need to institute appropriate measures to collect citizen’s data. The information collected is vital for public institutions in actualizing what services the citizens want. Using literature reviews and cases, the authors examine Big Data benefits in counties and propose a Big Data model to improve efficiency of e-governance services and productivity in county governments. The authors demonstrate Big Data framework has the aptitude of molding citizen’s opinion in county decision making process. Better use of e-technologies is shown in the proposed model which illustrates sharing resources among various data analytics sources. Our proposed framework based on Big Data analytics is a viable initiative to progress effectiveness and productivity, strengthen citizen engagement and participation and encourage decision-making in e-governance services delivery in the counties.展开更多
基金This work is supported by the National Natural Science Foundation of China[Grants Number 41771452,41771454 and 41890820]the Natural Science Fund of Hubei Province in China[Grant Number 2018CFA007].
文摘Urbanization is one of the most impactful human activities across the world today affecting the quality of urban life and its sustainable development.Urbanization in Africa is occurring at an unprecedented rate and it threatens the attainment of Sustainable Development Goals(SDGs).Urban sprawl has resulted in unsustainable urban development patterns from social,environmental,and economic perspectives.This study is among the first examples of research in Africa to combine remote sensing data with social media data to determine urban sprawl from 2011 to 2017 in Morogoro urban municipality,Tanzania.Random Forest(RF)method was applied to accomplish imagery classification and location-based social media(Twitter usage)data were obtained through a Twitter Application Programming Interface(API).Morogoro urban municipality was classified into built-up,vegetation,agriculture,and water land cover classes while the classification results were validated by the generation of 480 random points.Using the Kernel function,the study measured the location of Twitter users within a 1 km buffer from the center of the city.The results indicate that,expansion of the city(built-up land use),which is primarily driven by population expansion,has negative impacts on ecosystem services because pristine grasslands and forests which provide essential ecosystem services such as carbon sequestration and support for biodiversity have been replaced by built-up land cover.In addition,social media usage data suggest that there is the concentration of Twitter usage within the city center while Twitter usage declines away from the city center with significant spatial and numerical increase in Twitter usage in the study area.The outcome of the study suggests that the combination of remote sensing,social sensing,and population data were useful as a proxy/inference for interpreting urban sprawl and status of access to urban services and infrastructure in Morogoro,and Africa city where data for urban planning is often unavailable,inaccurate,or stale.
基金R&D Program of Beijing Municipal Education Commission(No.KM202211417015)Academic Research Projects of Beijing Union University(No.ZK10202209)+1 种基金The team-building subsidy of“Xuezhi Professorship”of the College of Applied Arts and Science of Beijing Union University(No.BUUCAS-XZJSTD-2024005)Academic Research Projects of Beijing Union University(No.ZKZD202305).
文摘As the pivotal green space,urban parks play an important role in urban residents’daily activities.Thy can not only bring people physical health,but also can be more likely to elicit positive sentiment to those who visit them.Recently,social media big data has provided new data sources for sentiment analysis.However,there was limited researches that explored the connection between urban parks and individual’s sentiments.Therefore,this study firstly employed a pre-trained language model(BERT,Bidirectional Encoder Representations from Transformers)to calculate sentiment scores based on social media data.Secondly,this study analysed the relationship between urban parks and individual’s sentiment from both spatial and temporal perspectives.Finally,by utilizing structural equation model(SEM),we identified 13 factors and analyzed its degree of the influence.The research findings are listed as below:①It confirmed that individuals generally experienced positive sentiment with high sentiment scores in the majority of urban parks;②The urban park type showed an influence on sentiment scores.In this study,higher sentiment scores observed in Eco-parks,comprehensive parks,and historical parks;③The urban parks level showed low impact on sentiment scores.With distinctions observed mainly at level-3 and level-4;④Compared to internal factors in parks,the external infrastructure surround them exerted more significant impact on sentiment scores.For instance,number of bus and subway stations around urban parks led to higher sentiment scores,while scenic spots and restaurants had inverse result.This study provided a novel method to quantify the services of various urban parks,which can be served as inspiration for similar studies in other cities and countries,enhancing their park planning and management strategies.
文摘Social media plays a crucial role in the organization of massive social movements. However, the sheer quantity of data generated by the events as well as the data collection restrictions that researchers encounter, leads to a series of challenges for researchers who want to analyze dynamic public discourse and opinion in response to and in the creation of world events. In this paper we present gatherTweet, a Python package that helps researchers efficiently collect social media data for events that are composed of many decentralized actions (across both space and time). The package is useful for studies that require analysis of the organizational or baseline messaging before an action, the action itself, and the effects of the action on subsequent public discourse. By capturing these aspects of world events gatherTweet enables the study of events and actions like protests, natural disasters, and elections.
文摘This study aims to conduct an in-depth analysis of social media data using causal inference methods to explore the underlying mechanisms driving user behavior patterns.By leveraging large-scale social media datasets,this research develops a systematic analytical framework that integrates techniques such as propensity score matching,regression analysis,and regression discontinuity design to identify the causal effects of content characteristics,user attributes,and social network structures on user interactions,including clicks,shares,comments,and likes.The empirical findings indicate that factors such as sentiment,topical relevance,and network centrality have significant causal impacts on user behavior,with notable differences observed among various user groups.This study not only enriches the theoretical understanding of social media data analysis but also provides data-driven decision support and practical guidance for fields such as digital marketing,public opinion management,and digital governance.
文摘This paper is concerned with anisotropic effects on seismic data and signal analysis for transversely isotropic rock media with vertical anisotropy. It is understood that these effects are significant in many practical applications, e.g. earthquake forecasting, materials exploration inside the Earth’s crust, as well as various practical works in oil industry. Under the framework of the most accepted anisotropic media model (i.e. VTI media, transverse isotropy with a vertical axis symmetry), with applications of a set of available anisotropic rock parameters for sandstone and shale, we have performed numerical calculations of the anisotropic effects. We show that for rocks with strong anisotropy, the induced relative depth error can be significantly large. Nevertheless, with an improved understanding of the seismic-signal propagation and proper data processing, the error can be reduced, which in turn may enhance the probability of forecasting accurately the various wave propagations inside the Earth’s crust, e.g. correctly forecasting the incoming earthquakes from the center of the Earth.
基金This research was supported by the UBC APFNet Grant(Project ID:2022sp2 CAN).
文摘COVID-19 posed challenges for global tourism management.Changes in visitor temporal and spatial patterns and their associated determinants pre-and peri-pandemic in Canadian Rocky Mountain National Parks are analyzed.Data was collected through social media programming and analyzed using spatiotemporal analysis and a geographically weighted regression(GWR)model.Results highlight that COVID-19 significantly changed park visitation patterns.Visitors tended to explore more remote areas peri-pandemic.The GWR model also indicated distance to nearby trails was a significant influence on visitor density.Our results indicate that the pandemic influenced tourism temporal and spatial imbalance.This research presents a novel approach using combined social media big data which can be extended to the field of tourism management,and has important implications to manage visitor patterns and to allocate resources efficiently to satisfy multiple objectives of park management.
基金financially supported by the Natural Science Foundation of China(No.41774133)the Open Funds of SINOPEC Key Laboratory of Geophysics(No.wtyjy-wx2017-01-04)National Science and Technology Major Project of the Ministry of Science and Technology of China(No.2016ZX05024-003-011)
文摘In marine seismic exploration, ocean-bottom cable techniques accurately record the multicomponent seismic wavefield; however, the seismic wave propagation in fluid–solid media cannot be simulated by a single wave equation. In addition, when the seabed interface is irregular, traditional finite-difference schemes cannot simulate the seismic wave propagation across the irregular seabed interface. Therefore, an acoustic–elastic forward modeling and vector-based P-and S-wave separation method is proposed. In this method, we divide the fluid–solid elastic media with irregular interface into orthogonal grids and map the irregular interface in the Cartesian coordinates system into a horizontal interface in the curvilinear coordinates system of the computational domain using coordinates transformation. The acoustic and elastic wave equations in the curvilinear coordinates system are applied to the fluid and solid medium, respectively. At the irregular interface, the two equations are combined into an acoustic–elastic equation in the curvilinear coordinates system. We next introduce a full staggered-grid scheme to improve the stability of the numerical simulation. Thus, separate P-and S-wave equations in the curvilinear coordinates system are derived to realize the P-and S-wave separation method.
文摘The united front work is an important part of the general pattern of united front work, and it is an important part of the party building in colleges and universities. It involves broad, comprehensive, democratic, non-party intellectuals, Religion, returned overseas Chinese, Taiwan compatriots and Taiwan overseas students and other aspects of the task, is the microcosm of the united front work and the window. Colleges and universities is an important base for cultivating talents. Teachers ‘teaching level and ideology and morality all directly affect young students’ outlook on life and values. Fully mobilize the enthusiasm and creativity of non-party people, is conducive to schools to complete the dissemination of knowledge, training the sacred mission of talent, help to promote the work of the school center to complete. The wide application of new media has provided new opportunities and new challenges for the united front work in colleges and universities.
基金the Open Research Fund Program of Shenzhen Key Laboratory of Spatial Smart Sensing and Services%sponsored by the Scientific Research Foundation for the Returned Overseas Chinese Scholars, State Education Ministry(grant number 50-20150618)%National Natural Science Foundation of China (grant numbers 41001220, 51378512, 41571397, and 41501442)This work was also supported by the Special Program for Applied Research on Super Computation of the NSFC-Guangdong Joint Fund
文摘This article introduces a novel low rank approximation (LRA)-based model to detect the functional regions with the data from about 15 million social media check-in records during a year-long period in Shanghai, China. We identified a series of latent structures, named latent spatio-temporal activity structures. While interpreting these structures, we can obtain a series of underlying associations between the spatial and temporal activity patterns. Moreover, we can not only reproduce the observed data with a lower dimensional representative, but also project spatio-temporal activity patterns in the same coordinate system. With the K-means clustering algorithm, five significant types of clusters that are directly annotated with a combination of temporal activities can be obtained, providing a clear picture of the correlation between the groups of regions and different activities at different times during a day. Besides the commercial and transportation dominant areas, we also detected two kinds of residential areas, the developed residential areas and the developing residential areas.We further interpret the spatial distribution of these clusters using urban form analytics. The results are highly consistent with the government planning in the same periods, indicating that our model is applicable to infer the functional regions from social media check-in data and can benefit a wide range of fields, such as urban planning, public services, and location-based recommender systems.
文摘In light of the rapid growth and development of social media, it has become the focus of interest in many different scientific fields. They seek to extract useful information from it, and this is called (knowledge), such as extracting information related to people’s behaviors and interactions to analyze feelings or understand the behavior of users or groups, and many others. This extracted knowledge has a very important role in decision-making, creating and improving marketing objectives and competitive advantage, monitoring events, whether political or economic, and development in all fields. Therefore, to extract this knowledge, we need to analyze the vast amount of data found within social media using the most popular data mining techniques and applications related to social media sites.
文摘This research is about the nuisances of social media applications on a Wi-Fi network at a university campus in Ghana. The aim was to access the security risk on the network, the speed of the network, and the data consumption of those platforms on the network. Network Mapper (Nmap Zenmap) Graphical User Interface 7.80 application was used to scan the various social media platforms to identify the protocols, ports, services, etc. to enable in accessing the vulnerability of the network. Data consumption of users’ mobile devices was collected and analyzed. Device Accounting (DA) based on the various social media applications was used. The results of the analysis revealed that the network is prone to attacks due to the nature of the protocols, ports, and services on social media applications. The numerous users with average monthly data consumption per user of 4 gigabytes, 300 megabytes on social media alone are a clear indication of high traffic as well as the cost of maintaining the network. A URL filtering of the social media websites was proposed on Rockus Outdoor AP to help curb the nuisance.
文摘The World Wide Web provides a wealth of information about everything, including contemporary audio and visual art events, which are discussed on media outlets, blogs, and specialized websites alike. This information may become a robust source of real-world data, which may form the basis of an objective data-driven analysis. In this study, a methodology for collecting information about audio and visual art events in an automated manner from a large array of websites is presented in detail. This process uses cutting edge Semantic Web, Web Search and Generative AI technologies to convert website documents into a collection of structured data. The value of the methodology is demonstrated by creating a large dataset concerning audiovisual events in Greece. The collected information includes event characteristics, estimated metrics based on their text descriptions, outreach metrics based on the media that reported them, and a multi-layered classification of these events based on their type, subjects and methods used. This dataset is openly provided to the general and academic public through a Web application. Moreover, each event’s outreach is evaluated using these quantitative metrics, the results are analyzed with an emphasis on classification popularity and useful conclusions are drawn concerning the importance of artistic subjects, methods, and media.
文摘With the emergence of new types of data(e.g.social media data)and cutting-edge computer technology(e.g.Natural Language Processing),the shortcomings of traditional methods(subjective and objective ways)for de-tecting urban livability can be overcome by an integrated approach.This study aims to develop a comprehensive approach to measure urban livability based on statistic data,geo-data(e.g.points of interest),questionnaires survey,and social media data(Instagram),from both objective and subjective angles.Hong Kong,as a city with a high level of urbanization and contrasting urban environments,is chosen as the study area in this research.Through this study,the question“which area of Hong Kong is more suitable for living”is answered by the visu-alization of GIS-based analysis.Also,the correlation between livability scores and individuals’sentiment scores are explored.Specifically,the results show that central areas of Hong Kong with a higher level of urbanization are relatively more livable than suburban regions.However,through sentiment analysis,individuals who post Instagram in suburban areas of Hong Kong usually express more positive content and happier emotion than those who post Instagram in central urban areas.The study could offer useful information for the policy action of authorities as well as the residential location choices of citizens.
文摘Objective To discuss how to use social media data for post-marketing drug safety monitoring in China as soon as possible by systematically combing the text mining applications,and to provide new ideas and methods for pharmacovigilance.Methods Relevant domestic and foreign literature was used to explore text classification based on machine learning,text mining based on deep learning(neural networks)and adverse drug reaction(ADR)terminology.Results and Conclusion Text classification based on traditional machine learning mainly include support vector machine(SVM)algorithm,naive Bayesian(NB)classifier,decision tree,hidden Markov model(HMM)and bidirectional en-coder representations from transformers(BERT).The main neural network text mining based on deep learning are convolution neural network(CNN),recurrent neural network(RNN)and long short-term memory(LSTM).ADR terminology standardization tools mainly include“Medical Dictionary for Regulatory Activities”(MedDRA),“WHODrug”and“Systematized Nomenclature of Medicine-Clinical Terms”(SNOMED CT).
文摘The evolution of social media in the recent years promoted the appearance of a new category: social media based on check-in. It enables the user to define their identity through information sharing. This paper aims to show the evolution of these media highlighting the effects and changes they cause in society through Super Bowl XLVI scenario, besides indicating the important role they have for the companies and marketing.
文摘Digitalization is transforming governments across the globe. At the national level, down to regional and multiple departments in the public institutions, unprecedented change is occurring exponentially as a result of massive digitalization. Digitalization is compelling governments at all levels to embrace voluminous data and institute appropriate multi-channel platforms to support digital transformation. While this is the case, most governments have been caught unprepared thwarting maximum benefits spurred by digitalization. Inherently, the social media and e-participation tools for generating huge amount of data have convoluted most governments’ appetite in Big Data management. This situation is further compounded with the slow pace of adoption of these technological tools by citizens and the public sectors. For enhanced e-citizen satisfaction and engagement, as well as e-participation processes, public institutions need to promote engagement and collaboration. In view of advancing benefits to their citizens, public institutions need to institute appropriate measures to collect citizen’s data. The information collected is vital for public institutions in actualizing what services the citizens want. Using literature reviews and cases, the authors examine Big Data benefits in counties and propose a Big Data model to improve efficiency of e-governance services and productivity in county governments. The authors demonstrate Big Data framework has the aptitude of molding citizen’s opinion in county decision making process. Better use of e-technologies is shown in the proposed model which illustrates sharing resources among various data analytics sources. Our proposed framework based on Big Data analytics is a viable initiative to progress effectiveness and productivity, strengthen citizen engagement and participation and encourage decision-making in e-governance services delivery in the counties.
