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Social Computing for Geographic Information Science
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Social Computing for Geographic Information Science

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

Deadline for manuscript submissions: closed (31 October 2021) | Viewed by 21646

Special Issue Editors

Dr. Arianna D'Ulizia

E-Mail Website
Guest Editor
National Research Council of Italy, 10135 Rome, Italy
Interests: social computing; human-computer interaction; multimodal and natural language processing; user-centered interaction design
Special Issues, Collections and Topics in MDPI journals
Dr. Patrizia Grifoni

E-Mail Website
Guest Editor
National Research Council, Institute of Research on Population and Social Policies (CNR-IRPPS), 00185 Rome, Italy
Interests: social informatics; social computing; human-machine interaction; multimodal interaction; sketch-based interfaces; multimedia applications; user modelling; knowledge bases; spatial data; geographic information systems; responsible research and innovation
Special Issues, Collections and Topics in MDPI journals
Dr. Fernando Ferri

E-Mail Website
Guest Editor
National Research Council, Institute of Research on Population and Social Policies (CNR-IRPPS), 00185 Rome, Italy
Interests: social informatics; social computing; data and knowledge bases; human-machine interaction; user-machine natural interaction; user modelling; visual interaction; sketch-based interfaces; geographic information systems; medical informatics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Geographic information science is experiencing new and unprecedented challenges linked to the increasing use of location-based social networks and the generation of huge amounts of social geo-located data from mobile sensing devices. Since geo-social technologies are rapidly advancing, there is an urgent need to understand and analyze the interplay between geographic information science and social computing in order to design new approaches for collecting, representing, understanding, managing, learning, interacting, and reasoning about social geographic information.

This Special Issue is dedicated to exploring current trends, research challenges, and opportunities related to geographic information science through the lens of social computing, from a threefold perspective: (1) theoretical, by exploring how to develop new conceptual frameworks for effectively fusing geographic and social information for representing and understanding geo-social relationships networks; (2) computational, by investigating how to innovatively use social computing techniques for geographic information science issues, including making spatial data more accessible, creating user-friendly environments for interacting with this data, and analyzing and reasoning on big social sensing data; (3) applicative, by understanding how the analysis of social geo-located data can be applied to various human activity domains, including languages, political elections, emotion recognition, disaster management, smart cities, and the spreading of disease.

We call for original contributions from a wide range of cross-disciplinary and collaborative domain knowledge related to geographic information science and social computing, such as cognitive science, information science, computer science, linguistics, and social science.

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

  • Social media geographic information analytics
  • Social sensing and spatiotemporal data
  • Collecting, processing, interpreting, and visualizing social sensing data
  • Volunteered geographic information (VGI) management
  • Location-based social networks
  • Modeling geo-social interactions
  • Collaborative interactions with geographic information
  • Sentiment analysis approaches for social sensing data
  • Geographic human–computer interaction
Dr. Arianna D'Ulizia
Dr. Patrizia Grifoni
Dr. Fernando Ferri
Guest Editor

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.

Published Papers (6 papers)

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Research

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20 pages, 535 KiB  
Article
Language Modeling on Location-Based Social Networks
by Juglar Diaz, Felipe Bravo-Marquez and Barbara Poblete
ISPRS Int. J. Geo-Inf. 2022, 11(2), 147; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi11020147 - 18 Feb 2022
Cited by 1 | Viewed by 2296
Abstract
The popularity of mobile devices with GPS capabilities, along with the worldwide adoption of social media, have created a rich source of text data combined with spatio-temporal information. Text data collected from location-based social networks can be used to gain space–time insights into [...] Read more.
The popularity of mobile devices with GPS capabilities, along with the worldwide adoption of social media, have created a rich source of text data combined with spatio-temporal information. Text data collected from location-based social networks can be used to gain space–time insights into human behavior and provide a view of time and space from the social media lens. From a data modeling perspective, text, time, and space have different scales and representation approaches; hence, it is not trivial to jointly represent them in a unified model. Existing approaches do not capture the sequential structure present in texts or the patterns that drive how text is generated considering the spatio-temporal context at different levels of granularity. In this work, we present a neural language model architecture that allows us to represent time and space as context for text generation at different granularities. We define the task of modeling text, timestamps, and geo-coordinates as a spatio-temporal conditioned language model task. This task definition allows us to employ the same evaluation methodology used in language modeling, which is a traditional natural language processing task that considers the sequential structure of texts. We conduct experiments over two datasets collected from location-based social networks, Twitter and Foursquare. Our experimental results show that each dataset has particular patterns for language generation under spatio-temporal conditions at different granularities. In addition, we present qualitative analyses to show how the proposed model can be used to characterize urban places. Full article
(This article belongs to the Special Issue Social Computing for Geographic Information Science)
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22 pages, 3897 KiB  
Article
#AllforJan: How Twitter Users in Europe Reacted to the Murder of Ján Kuciak—Revealing Spatiotemporal Patterns through Sentiment Analysis and Topic Modeling
by Tamás Kovács, Anna Kovács-Győri and Bernd Resch
ISPRS Int. J. Geo-Inf. 2021, 10(9), 585; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10090585 - 31 Aug 2021
Cited by 6 | Viewed by 3667
Abstract
Social media platforms such as Twitter are considered a new mediator of collective action, in which various forms of civil movements unite around public posts, often using a common hashtag, thereby strengthening the movements. After 26 February 2018, the #AllforJan hashtag spread across [...] Read more.
Social media platforms such as Twitter are considered a new mediator of collective action, in which various forms of civil movements unite around public posts, often using a common hashtag, thereby strengthening the movements. After 26 February 2018, the #AllforJan hashtag spread across the web when Ján Kuciak, a young journalist investigating corruption in Slovakia, and his fiancée were killed. The murder caused moral shock and mass protests in Slovakia and in several other European countries, as well. This paper investigates how this murder, and its follow-up events, were discussed on Twitter, in Europe, from 26 February to 15 March 2018. Our investigations, including spatiotemporal and sentiment analyses, combined with topic modeling, were conducted to comprehensively understand the trends and identify potential underlying factors in the escalation of the events. After a thorough data pre-processing including the extraction of spatial information from the users’ profile and the translation of non-English tweets, we clustered European countries based on the temporal patterns of tweeting activity in the analysis period and investigated how the sentiments of the tweets and the discussed topics varied over time in these clusters. Using this approach, we found that tweeting activity resonates not only with specific follow-up events, such as the funeral or the resignation of the Prime Minister, but in some cases, also with the political narrative of a given country affecting the course of discussions. Therefore, we argue that Twitter data serves as a unique and useful source of information for the analysis of such civil movements, as the analysis can reveal important patterns in terms of spatiotemporal and sentimental aspects, which may also help to understand protest escalation over space and time. Full article
(This article belongs to the Special Issue Social Computing for Geographic Information Science)
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20 pages, 9736 KiB  
Article
Emojis as Contextual Indicants in Location-Based Social Media Posts
by Eva Hauthal, Alexander Dunkel and Dirk Burghardt
ISPRS Int. J. Geo-Inf. 2021, 10(6), 407; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10060407 - 12 Jun 2021
Cited by 3 | Viewed by 3207
Abstract
The presented study aims to investigate the relationship between the use of emojis in location-based social media and the location of the corresponding post in terms of perceived objects and conducted activities connected to this place. The basis for this is not a [...] Read more.
The presented study aims to investigate the relationship between the use of emojis in location-based social media and the location of the corresponding post in terms of perceived objects and conducted activities connected to this place. The basis for this is not a purely frequency-based assessment, but a specifically introduced measure called typicality. To evaluate the typicality measure and examine the assumption that emojis are contextual indicants, a dataset of worldwide geotagged posts from Instagram relating to sunset and sunrise events is used, converted to a privacy-aware version based on a Hyperloglog approach. Results suggest that emojis can often provide more nuanced information about user activities and the surrounding environment than is possible with hashtags. Thus, emojis may be suitable for identifying less obvious characteristics and the sense of a place. Emojis are already explored in research, but mainly for sentiment analysis, for semantic studies or as part of emoji prediction. In contrast, this work provides novel insights into the user’s spatial or activity context by applying the typicality measure and therefore considers emojis contextual indicants. Full article
(This article belongs to the Special Issue Social Computing for Geographic Information Science)
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13 pages, 315 KiB  
Article
Case Study on Privacy-Aware Social Media Data Processing in Disaster Management
by Marc Löchner, Ramian Fathi, David ‘-1’ Schmid, Alexander Dunkel, Dirk Burghardt, Frank Fiedrich and Steffen Koch
ISPRS Int. J. Geo-Inf. 2020, 9(12), 709; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi9120709 - 26 Nov 2020
Cited by 3 | Viewed by 4033
Abstract
Social media data is heavily used to analyze and evaluate situations in times of disasters, and derive decisions for action from it. In these critical situations, it is not surprising that privacy is often considered a secondary problem. In order to prevent subsequent [...] Read more.
Social media data is heavily used to analyze and evaluate situations in times of disasters, and derive decisions for action from it. In these critical situations, it is not surprising that privacy is often considered a secondary problem. In order to prevent subsequent abuse, theft or public exposure of collected datasets, however, protecting the privacy of social media users is crucial. Avoiding unnecessary data retention is an important question that is currently largely unsolved. There are a number of technical approaches available, but their deployment in disaster management is either impractical or requires special adaption, limiting its utility. In this case study, we explore the deployment of a cardinality estimation algorithm called HyperLogLog into disaster management processes. It is particularly suited for this field, because it allows to stream data in a format that cannot be used for purposes other than the originally intended. We develop and conduct a focus group discussion with teams of social media analysts. We identify challenges and opportunities of working with such a privacy-enhanced social media data format and compare the process with conventional techniques. Our findings show that, with the exception of training scenarios, deploying HyperLogLog in the data acquisition process will not distract the data analysis process. Instead, several benefits, such as improved working with huge datasets, may contribute to a more widespread use and adoption of the presented technique, which provides a basis for a better integration of privacy considerations in disaster management. Full article
(This article belongs to the Special Issue Social Computing for Geographic Information Science)
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23 pages, 4865 KiB  
Article
BITOUR: A Business Intelligence Platform for Tourism Analysis
by Alexander Bustamante, Laura Sebastia and Eva Onaindia
ISPRS Int. J. Geo-Inf. 2020, 9(11), 671; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi9110671 - 12 Nov 2020
Cited by 6 | Viewed by 3596
Abstract
Integrating collaborative data in data-driven Business Intelligence (BI) system brings an opportunity to foster the decision-making process towards improving tourism competitiveness. This article presents BITOUR, a BI platform that integrates four collaborative data sources (Twitter, Openstreetmap, Tripadvisor and Airbnb). [...] Read more.
Integrating collaborative data in data-driven Business Intelligence (BI) system brings an opportunity to foster the decision-making process towards improving tourism competitiveness. This article presents BITOUR, a BI platform that integrates four collaborative data sources (Twitter, Openstreetmap, Tripadvisor and Airbnb). BITOUR follows a classical BI architecture and provides functionalities for data transformation, data processing, data analysis and data visualization. At the core of the data processing, BITOUR offers mechanisms to identify tourists in Twitter, assign tweets to attractions and accommodation sites from Tripadvisor and Airbnb, analyze sentiments in opinions issued by tourists, and all this using geolocation objects in Openstreetmap. With all these ingredients, BITOUR enables data analysis and visualization to answer questions like the most frequented places by tourists, the average stay length or the view of visitors of some particular destination. Full article
(This article belongs to the Special Issue Social Computing for Geographic Information Science)
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Review

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33 pages, 3148 KiB  
Review
Query Processing of Geosocial Data in Location-Based Social Networks
by Arianna D’Ulizia, Patrizia Grifoni and Fernando Ferri
ISPRS Int. J. Geo-Inf. 2022, 11(1), 19; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi11010019 - 30 Dec 2021
Cited by 1 | Viewed by 2722
Abstract
The increasing use of social media and the recent advances in geo-positioning technologies have produced a great amount of geosocial data, consisting of spatial, textual, and social information, to be managed and queried. In this paper, we focus on the issue of query [...] Read more.
The increasing use of social media and the recent advances in geo-positioning technologies have produced a great amount of geosocial data, consisting of spatial, textual, and social information, to be managed and queried. In this paper, we focus on the issue of query processing by providing a systematic literature review of geosocial data representations, query processing methods, and evaluation approaches published over the last two decades (2000–2020). The result of our analysis shows the categories of geosocial queries proposed by the surveyed studies, the query primitives and the kind of access method used to retrieve the result of the queries, the common evaluation metrics and datasets used to evaluate the performance of the query processing methods, and the main open challenges that should be faced in the near future. Due to the ongoing interest in this research topic, the results of this survey are valuable to many researchers and practitioners by gaining an in-depth understanding of the geosocial querying process and its applications and possible future perspectives. Full article
(This article belongs to the Special Issue Social Computing for Geographic Information Science)
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