Special Issue "Applications and Implications in Geosocial Media Monitoring"

A special issue of ISPRS International Journal of Geo-Information (ISSN 2220-9964).

Deadline for manuscript submissions: closed (30 June 2021) | Viewed by 16764

Special Issue Editors

Institute of Geography and Regional Research, University of Vienna, Universitätsstraße 7, 1010 Vienna, Austria
Interests: location privacy; spatial confidentiality; geosocial media; spatio-temporal analytics; spatial modelling; crime analysis; urban safety
1. Department of Geoinformatics, University of Salzburg, 5020 Salzburg, Austria
2. Center for Geographic Analysis, Harvard University, Cambridge, MA 02138, 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
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Special Issue Information

Dear Colleagues,

The number of GIS applications and tools that analyze and infer information from raw geosocial media data has rapidly increased over the last few years. Their application fields are various, including such fields as consumer segmentation and market research, sentiment analysis & event detection, monitoring the public sector, modeling population and ambient population, drug abuse geospatial patterns, applications in law enforcement/police, detecting location spoofing, and surveillance and medial governance, just to name a few examples from the fast-growing list.

Geosocial media data are produced by individuals, are about individuals, and are also available at an individual level and of high spatiotemporal resolution. Hence their exploitation raises several concerns regarding their ethical use, the disclosure of personal information, the validity of the inferred information, and last but not least the transparency of their use and end purpose.  The scope of this Special Issue is to examine various geosocial media monitoring applications as well as/or their implications with regards to ethics, privacy, and the evolution towards a digital society.

Topics of interest include, but are not limited to, the following:

  1. Monitoring communities with the use of geosocial media
  2. Monitoring individuals with the use of geosocial media
  3. Monitoring by public organizations with the use of geosocial media
  4. Other monitoring applications with the use of geosocial media
  5. Implications of geosocial media monitoring
  6. Inference attacks from geosocial media
  7. Privacy protection for geosocial media
  8. Geoprivacy by Design
  9. Research ethics
Dr. Ourania Kounadi
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 submissions that pass pre-check are 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 1700 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

  • social media
  • big geodata
  • location privacy
  • location-based inferences
  • surveillance

Published Papers (6 papers)

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Research

19 pages, 3343 KiB  
Article
Assessing Place Type Similarities Based on Functional Signatures Extracted from Social Media Data
ISPRS Int. J. Geo-Inf. 2021, 10(9), 626; https://doi.org/10.3390/ijgi10090626 - 17 Sep 2021
Viewed by 1557
Abstract
Place types are often used to query places or retrieve data in gazetteers. Existing gazetteers do not use the same place type classification schemes, and the various typing schemes can cause difficulties in data alignment and matching. Different place types may share some [...] Read more.
Place types are often used to query places or retrieve data in gazetteers. Existing gazetteers do not use the same place type classification schemes, and the various typing schemes can cause difficulties in data alignment and matching. Different place types may share some level of similarities. However, previous studies have paid little attention to the place type similarities. This study proposes an analytical approach to measuring similarities between place types in multiple typing schemes based on functional signatures extracted from web-harvested place descriptions. In this study, a functional signature consists of three component signature factors: place affordance, events, and key-descriptors. The proposed approach has been tested in a case study using Twitter data. The case study finds high similarity scores between some pairs of types and summarizes the situations when high similarities could occur. The research makes two innovative contributions: First, it proposes a new analytical approach to measuring place type similarities. Second, it demonstrates the potential and benefits of using location-based social media data to better understand places. Full article
(This article belongs to the Special Issue Applications and Implications in Geosocial Media Monitoring)
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31 pages, 10853 KiB  
Article
Spatio-Temporal Machine Learning Analysis of Social Media Data and Refugee Movement Statistics
ISPRS Int. J. Geo-Inf. 2021, 10(8), 498; https://doi.org/10.3390/ijgi10080498 - 23 Jul 2021
Cited by 1 | Viewed by 3795
Abstract
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, [...] Read more.
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. Full article
(This article belongs to the Special Issue Applications and Implications in Geosocial Media Monitoring)
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16 pages, 953 KiB  
Article
Analysis of Geotagging Behavior: Do Geotagged Users Represent the Twitter Population?
ISPRS Int. J. Geo-Inf. 2021, 10(6), 373; https://doi.org/10.3390/ijgi10060373 - 02 Jun 2021
Cited by 18 | Viewed by 3284
Abstract
Twitter’s APIs are now the main data source for social media researchers. A large number of studies have utilized Twitter data for diverse research interests. Twitter users can share their precise real-time location, and Twitter APIs can provide this information as longitude and [...] Read more.
Twitter’s APIs are now the main data source for social media researchers. A large number of studies have utilized Twitter data for diverse research interests. Twitter users can share their precise real-time location, and Twitter APIs can provide this information as longitude and latitude. These geotagged Twitter data can help to study human activities and movements for different applications. Compared to the mostly small-scale data samples in different domains, such as social science, collecting geotagged data offers large samples. There is a fundamental question whether geotagged users can represent non-geotagged users. While some studies have investigated the question from different perspectives, they did not investigate profile information and the contents of tweets of geotagged and non-geotagged users. This empirical study addresses this limitation by applying text mining, statistical analysis, and machine learning techniques on Twitter data comprising more than 88,000 users and over 170 million tweets. Our findings show that there is a significant difference (p-value < 0.001) between geotagged and non-geotagged users based on 73% of the features obtained from the users’ profiles and tweets. The features can also help to distinguish between geotagged and non-geotagged users with around 80% accuracy. This research illustrates that geotagged users do not represent the Twitter population. Full article
(This article belongs to the Special Issue Applications and Implications in Geosocial Media Monitoring)
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18 pages, 1900 KiB  
Article
Evaluating the Representativeness of Socio-Demographic Variables over Time for Geo-Social Media Data
ISPRS Int. J. Geo-Inf. 2021, 10(5), 323; https://doi.org/10.3390/ijgi10050323 - 10 May 2021
Cited by 3 | Viewed by 2118
Abstract
Geo-social media data are widely used as a data source to model populations and processes in a variety of contexts. However, if the data do not adequately represent the population they are drawn from, analysis results will be biased. Unaddressed, these biases may [...] Read more.
Geo-social media data are widely used as a data source to model populations and processes in a variety of contexts. However, if the data do not adequately represent the population they are drawn from, analysis results will be biased. Unaddressed, these biases may lead to false interpretations and conclusions. In this paper, we propose a generic methodology for investigating the representativeness of geo-social media data for population groups of similar statistical predictive power based on reference data. The groups are designed to be spatially coherent regions with similar prediction errors. Based on these units, we investigate the influence of different socio-demographic covariates on the representativeness. We perform experiments based on over 1.6 billion tweets and 90 socio-demographic covariates. We demonstrate that Twitter data representativeness varies strongly over time and space. Our results show that densely populated areas tend to be underrepresented consistently in non-spatial models. Over time, some covariates like the number of people aged 20 years exhibit highly different effects on the prediction models, whereas others are much more stable. The spatial effects can most frequently be explained using spatial error models, indicating spatially related errors that indicate the necessity of additional covariates. Finally, we provide hints for interpreting the results of our approach for researchers using the concepts presented in this paper. Full article
(This article belongs to the Special Issue Applications and Implications in Geosocial Media Monitoring)
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26 pages, 9592 KiB  
Article
Information Detection for the Process of Typhoon Events in Microblog Text: A Spatio-Temporal Perspective
ISPRS Int. J. Geo-Inf. 2021, 10(3), 174; https://doi.org/10.3390/ijgi10030174 - 16 Mar 2021
Cited by 4 | Viewed by 1937
Abstract
Typhoon is one of the most destructive natural disasters in the world. Real-time information on the process of typhoon events serves as important reference for disaster emergency. In the era of big data, microblog text has been gradual applied to the prevention, preparation, [...] Read more.
Typhoon is one of the most destructive natural disasters in the world. Real-time information on the process of typhoon events serves as important reference for disaster emergency. In the era of big data, microblog text has been gradual applied to the prevention, preparation, response, and recovery of disaster management. However, previous studies mostly focused on the acquisition of different disaster information in microblog text, while ignoring the structural integration of this fragmented information, and thus cannot reflect the dynamic process of typhoon events. In this paper, a typhoon event information model (TEIM) considering the multi-granularity and dynamic characteristics of information is constructed from the spatio-temporal perspective. On the basis of extracting the information elements of typhoon events from microblog text, a process-oriented information aggregation method (TEPIA) is proposed to provide an ordered information resource for detecting the evolution process of typhoon events. Based on the case study of typhoon “Lekima” event using Sina Weibo, the results show that the method proposed in this paper can comprehensively detect the information of different objects on any spatio-temporal node during the process of typhoon events, which is beneficial to mining disaster emergencies in small scale from microblog text. Full article
(This article belongs to the Special Issue Applications and Implications in Geosocial Media Monitoring)
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20 pages, 8349 KiB  
Article
Twitter Use in Hurricane Isaac and Its Implications for Disaster Resilience
ISPRS Int. J. Geo-Inf. 2021, 10(3), 116; https://doi.org/10.3390/ijgi10030116 - 27 Feb 2021
Cited by 20 | Viewed by 2772
Abstract
Disaster resilience is the capacity of a community to “bounce back” from disastrous events. Most studies rely on traditional data such as census data to study community resilience. With increasing use of social media, new data sources such as Twitter could be utilized [...] Read more.
Disaster resilience is the capacity of a community to “bounce back” from disastrous events. Most studies rely on traditional data such as census data to study community resilience. With increasing use of social media, new data sources such as Twitter could be utilized to monitor human response during different phases of disasters to better understand resilience. An important research question is: Does Twitter use correlate with disaster resilience? Specifically, will communities with more disaster-related Twitter uses be more resilient to disasters, presumably because they have better situational awareness? The underlying issue is that if there are social and geographical disparities in Twitter use, how will such disparities affect communities’ resilience to disasters? This study examines the relationship between Twitter use and community resilience during Hurricane Isaac, which hit Louisiana and Mississippi in August 2012. First, we applied the resilience inference measurement (RIM) model to calculate the resilience indices of 146 affected counties. Second, we analyzed Twitter use and their sentiment patterns through the three phases of Hurricane Isaac—preparedness, response, and recovery. Third, we correlated Twitter use density and sentiment scores with the resilience scores and major social–environmental variables to test whether significant geographical and social disparities in Twitter use existed through the three phases of disaster management. Significant positive correlations were found between Twitter use density and resilience indicators, confirming that communities with higher resilience capacity, which are characterized by better social–environmental conditions, tend to have higher Twitter use. These results imply that Twitter use during disasters could be improved to increase the resilience of affected communities. On the other hand, no significant correlations were found between sentiment scores and resilience indicators, suggesting that further research on sentiment analysis may be needed. Full article
(This article belongs to the Special Issue Applications and Implications in Geosocial Media Monitoring)
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