Special Issue "Advances in Social Network Analysis – Spatio-Temporal and Semantic Methods"

Special Issue Editors

Dr. Hartwig H. Hochmair
Website
Guest Editor
Fort Lauderdale Research and Education Center (FLREC), University of Florida, Fort Lauderdale, FL 32611, USA
Interests: crowdsourced geographic information; alternative transportation; location-based services; data quality
Special Issues and Collections in MDPI journals
Dr. Levente Juhász
Website
Co-Guest Editor
GIS Center, Florida International University, United States
Interests: GIScience; user-generated geodata; spatial analysis; geocomputation; citizen science; volunteered geographic information; geospatial web
Special Issues and Collections in MDPI journals
Assoc. Prof. Dr. Bernd Resch
Website
Co-Guest Editor
1. Department of Geoinformatics - Z_GIS, University of Salzburg, Schillerstraße 30, 5020 Salzburg, Austria
2. Center for Geographic Analysis, Harvard University, 1737 Cambridge Street, 02138 Cambridge MA, USA
Interests: human-centred geoinformatics; geospatial machine learning; urban geoinformatics; fusion of human and technical sensors; people as sensors and collective sensing (VGI); real-time and smart cities; crowdsourcing; digital health
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

Data from geospatial applications, such as social media, location-based service (LBS) and volunteered geographic information (VGI) platforms, has become a prominent source for modeling human behavior and for better understanding complex social dynamics in geographic spaces. The massive amount of multi-dimensional data (spatial, temporal, semantic) from these sources is typically unstructured and thus calls for an advance in data representation, modeling, analysis, and visualization for the successful transition from data to information. This Special Issue is inviting contributions that demonstrate integrated analysis of spatial, temporal, and semantic data from social networks, including their content, linkage, and structure, towards a better understanding of social behavior, human interaction patterns and the dynamic characteristics of real-world phenomena and events. This involves novel use of machine learning approaches, analysis frameworks, data mining, and (geo-)statistical methods to exploit unstructured content of social network data. This Special Issue also encourages the demonstration of new analytical tools, discussion of current data privacy and licensing issues, the exploration of data from lesser known social media, LBS, and VGI platforms, and the application of fusion methods of data across multiple platforms.

Assoc. Prof. Dr. Hartwig H. Hochmair
Dr. Levente Juhász
Assoc. Prof. Dr. Bernd Resch
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. ISPRS International Journal of Geo-Information is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1000 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Location-based social networks
  • Real time data exploration
  • Data mining
  • Data fusion
  • Scalable frameworks
  • Unstructured data
  • Artificial intelligence and machine learning
  • Spatio-temporal analytics
  • Semantic analysis
  • User behavior analysis

Published Papers (3 papers)

Order results
Result details
Select all
Export citation of selected articles as:

Research

Open AccessArticle
An Overview of Social Media Apps and their Potential Role in Geospatial Research
ISPRS Int. J. Geo-Inf. 2020, 9(9), 526; https://doi.org/10.3390/ijgi9090526 - 01 Sep 2020
Abstract
Social media apps provide analysts with a wide range of data to study behavioral aspects of our everyday lives and to answer societal questions. Although social media data analysis is booming, only a handful of prominent social media apps, such as Twitter, Foursquare/Swarm, [...] Read more.
Social media apps provide analysts with a wide range of data to study behavioral aspects of our everyday lives and to answer societal questions. Although social media data analysis is booming, only a handful of prominent social media apps, such as Twitter, Foursquare/Swarm, Facebook, or LinkedIn are typically used for this purpose. However, there is a large selection of less known social media apps that go unnoticed in the scientific community. This paper reviews 110 social media apps and assesses their potential usability in geospatial research through providing metrics on selected characteristics. About half of the apps (57 out of 110) offer an Application Programming Interface (API) for data access, where rate limits, fee models, and type of spatial data available for download vary strongly between the different apps. To determine the current role and relevance of social media platforms that offer an API in academic research, a search for scientific papers on Google Scholar, the Association for Computing Machinery (ACM) Digital Library, and the Science Core Collection of the Web of Science (WoS) is conducted. This search revealed that Google Scholar returns the highest number of documents (Mean = 183,512) compared to ACM (Mean = 1895) and WoS (Mean = 1495), and that data and usage patterns from prominent social media apps are more frequently analyzed in research studies than those of less known apps. The WoS citation database was also used to generate lists of themes covered in academic publications that analyze the 57 social media platforms that offer an API. Results show that among these 57 platforms, for 26 apps at least some papers evolve around a geospatial discipline, such as Geography, Remote Sensing, Transportation, or Urban Planning. This analysis, therefore, connects apps with commonly used research themes, and together with tabulated API characteristics can help researchers to identify potentially suitable social media apps for their research. Word clouds generated from titles and abstracts of papers associated with the 57 platforms, grouped into seven thematic categories, show further refinement of topics addressed in the analysis of social media platforms. Considering various evaluation criteria, such as provision of geospatial data or the number (i.e., absence) of currently published research papers in connection with a social media platform, the study concludes that among the numerous social media apps available today, 17 less known apps deserve closer examination since they might be used to investigate previously underexplored research topics. It is hoped that this study can serve as a reference for the analysis of the social media landscape in the future. Full article
Show Figures

Figure 1

Open AccessArticle
Geographical Structural Features of the WeChat Social Networks
ISPRS Int. J. Geo-Inf. 2020, 9(5), 290; https://doi.org/10.3390/ijgi9050290 - 01 May 2020
Abstract
Recently, spatial interaction analysis of online social networks has become a big concern. Early studies of geographical characteristics analysis and community detection in online social networks have shown that nodes within the same community might gather together geographically. However, the method of community [...] Read more.
Recently, spatial interaction analysis of online social networks has become a big concern. Early studies of geographical characteristics analysis and community detection in online social networks have shown that nodes within the same community might gather together geographically. However, the method of community detection is based on the idea that there are more links within the community than that connect nodes in different communities, and there is no analysis to explain the phenomenon. The statistical models for network analysis usually investigate the characteristics of a network based on the probability theory. This paper analyzes a series of statistical models and selects the MDND model to classify links and nodes in social networks. The model can achieve the same performance as the community detection algorithm when analyzing the structure in the online social network. The construction assumption of the model explains the reasons for the geographically aggregating of nodes in the same community to a degree. The research provides new ideas and methods for nodes classification and geographic characteristics analysis of online social networks and mobile communication networks and makes up for the shortcomings of community detection methods that do not explain the principle of network generation. A natural progression of this work is to geographically analyze the characteristics of social networks and provide assistance for advertising delivery and Internet management. Full article
Show Figures

Figure 1

Open AccessArticle
Spatial Reliability Assessment of Social Media Mining Techniques with Regard to Disaster Domain-Based Filtering
ISPRS Int. J. Geo-Inf. 2020, 9(4), 245; https://doi.org/10.3390/ijgi9040245 - 20 Apr 2020
Cited by 2
Abstract
The data generated by social media such as Twitter are classified as big data and the usability of those data can provide a wide range of resources to various study areas including disaster management, tourism, political science, and health. However, apart from the [...] Read more.
The data generated by social media such as Twitter are classified as big data and the usability of those data can provide a wide range of resources to various study areas including disaster management, tourism, political science, and health. However, apart from the acquisition of the data, the reliability and accuracy when it comes to using it concern scientists in terms of whether or not the use of social media data (SMD) can lead to incorrect and unreliable inferences. There have been many studies on the analyses of SMD in order to investigate their reliability, accuracy, or credibility, but that have not dealt with the filtering techniques applied to with the data before creating the results or after their acquisition. This study provides a methodology for detecting the accuracy and reliability of the filtering techniques for SMD and then a spatial similarity index that analyzes spatial intersections, proximity, and size, and compares them. Finally, we offer a comparison that shows the best combination of filtering techniques and similarity indices to create event maps of SMD by using the Getis-Ord Gi* technique. The steps of this study can be summarized as follows: an investigation of domain-based text filtering techniques for dealing with sentiment lexicons, machine learning-based sentiment analyses on reliability, and developing intermediate codes specific to domain-based studies; then, by using various similarity indices, the determination of the spatial reliability and accuracy of maps of the filtered social media data. The study offers the best combination of filtering, mapping, and spatial accuracy investigation methods for social media data, especially in the case of emergencies, where urgent spatial information is required. As a result, a new similarity index based on the spatial intersection, spatial size, and proximity relationships is introduced to determine the spatial accuracy of the fine-filtered SMD. The motivation for this research is to develop the ability to create an incidence map shortly after a disaster event such as a bombing. However, the proposed methodology can also be used for various domains such as concerts, elections, natural disasters, marketing, etc. Full article
Show Figures

Figure 1

Back to TopTop