Exploring the effectiveness of geomasking techniques for protecting the geoprivacy of Twitter users

With the ubiquitous use of location-based services, large-scale individual-level location data has been widely collected through location-awareness devices. Geoprivacy concerns arise on the issues of user identity de-anonymization and location exposure. In this work, we investigate the effectiveness of geomasking techniques for protecting the geoprivacy of active Twitter users who frequently share geotagged tweets in their home and work locations. By analyzing over 38,000 geotagged tweets of 93 active Twitter users in three U.S. cities, the two-dimensional Gaussian masking technique with proper standard deviation settings is found to be more effective to protect user\u27s location privacy while sacrificing geospatial analytical resolution than the random perturbation masking method and the aggregation on traffic analysis zones. Furthermore, a three-dimensional theoretical framework considering privacy, analytics, and uncertainty factors simultaneously is proposed to assess geomasking techniques. Our research offers insights into geoprivacy concerns of social media users\u27 georeferenced data sharing for future development of location-based applications and services

Spatial and Temporal Sentiment Analysis of Twitter data

The public have used Twitter world wide for expressing opinions. This study focuses on spatio-temporal variation of georeferenced Tweets’ sentiment polarity, with a view to understanding how opinions evolve on Twitter over space and time and across communities of users. More specifically, the question this study tested is whether sentiment polarity on Twitter exhibits specific time-location patterns. The aim of the study is to investigate the spatial and temporal distribution of georeferenced Twitter sentiment polarity within the area of 1 km buffer around the Curtin Bentley campus boundary in Perth, Western Australia. Tweets posted in campus were assigned into six spatial zones and four time zones. A sentiment analysis was then conducted for each zone using the sentiment analyser tool in the Starlight Visual Information System software. The Feature Manipulation Engine was employed to convert non-spatial files into spatial and temporal feature class. The spatial and temporal distribution of Twitter sentiment polarity patterns over space and time was mapped using Geographic Information Systems (GIS). Some interesting results were identified. For example, the highest percentage of positive Tweets occurred in the social science area, while science and engineering and dormitory areas had the highest percentage of negative postings. The number of negative Tweets increases in the library and science and engineering areas as the end of the semester approaches, reaching a peak around an exam period, while the percentage of negative Tweets drops at the end of the semester in the entertainment and sport and dormitory area. This study will provide some insights into understanding students and staff ’s sentiment variation on Twitter, which could be useful for university teaching and learning management

European Handbook of Crowdsourced Geographic Information

"This book focuses on the study of the remarkable new source of geographic information that has become available in the form of user-generated content accessible over the Internet through mobile and Web applications. The exploitation, integration and application of these sources, termed volunteered geographic information (VGI) or crowdsourced geographic information (CGI), offer scientists an unprecedented opportunity to conduct research on a variety of topics at multiple scales and for diversified objectives. The Handbook is organized in five parts, addressing the fundamental questions: What motivates citizens to provide such information in the public domain, and what factors govern/predict its validity?What methods might be used to validate such information? Can VGI be framed within the larger domain of sensor networks, in which inert and static sensors are replaced or combined by intelligent and mobile humans equipped with sensing devices? What limitations are imposed on VGI by differential access to broadband Internet, mobile phones, and other communication technologies, and by concerns over privacy? How do VGI and crowdsourcing enable innovation applications to benefit human society? Chapters examine how crowdsourcing techniques and methods, and the VGI phenomenon, have motivated a multidisciplinary research community to identify both fields of applications and quality criteria depending on the use of VGI. Besides harvesting tools and storage of these data, research has paid remarkable attention to these information resources, in an age when information and participation is one of the most important drivers of development. The collection opens questions and points to new research directions in addition to the findings that each of the authors demonstrates. Despite rapid progress in VGI research, this Handbook also shows that there are technical, social, political and methodological challenges that require further studies and research.

Citizen Science and Geospatial Capacity Building

This book is a collection of the articles published the Special Issue of ISPRS International Journal of Geo-Information on “Citizen Science and Geospatial Capacity Building”. The articles cover a wide range of topics regarding the applications of citizen science from a geospatial technology perspective. Several applications show the importance of Citizen Science (CitSci) and volunteered geographic information (VGI) in various stages of geodata collection, processing, analysis and visualization; and for demonstrating the capabilities, which are covered in the book. Particular emphasis is given to various problems encountered in the CitSci and VGI projects with a geospatial aspect, such as platform, tool and interface design, ontology development, spatial analysis and data quality assessment. The book also points out the needs and future research directions in these subjects, such as; (a) data quality issues especially in the light of big data; (b) ontology studies for geospatial data suited for diverse user backgrounds, data integration, and sharing; (c) development of machine learning and artificial intelligence based online tools for pattern recognition and object identification using existing repositories of CitSci and VGI projects; and (d) open science and open data practices for increasing the efficiency, decreasing the redundancy, and acknowledgement of all stakeholders

Text-based document geolocation and its application to the digital humanities

This dissertation investigates automatic geolocation of documents (i.e. identification of their location, expressed as latitude/longitude coordinates), based on the text of those documents rather than metadata. I assert that such geolocation can be performed using text alone, at a sufficient accuracy for use in real-world applications. Although in some corpora metadata is found in abundance (e.g. home location, time zone, friends, followers, etc. in Twitter), it is lacking in others, such as many corpora of primary-source documents in the digital humanities, an area to which document geolocation has hardly been applied. To this end, I first develop methods for accurate text-based geolocation and then apply them to newly-annotated corpora in the digital humanities. The geolocation methods I develop use both uniform and adaptive (k-d tree) grids over the Earth’s surface, culminating in a hierarchical logistic-regression-based technique that achieves state of the art results on well-known corpora (Twitter user feeds, Wikipedia articles and Flickr image tags). In the second part of the dissertation I develop a new NLP task, text-based geolocation of historical corpora. Because there are no existing corpora to test on, I create and annotate two new corpora of significantly different natures (a 19th-century travel log and a large set of Civil War archives). I show how my methods produce good geolocation accuracy even given the relatively small amount of annotated data available, which can be further improved using domain adaptation. I then use the predictions on the much larger unannotated portion of the Civil War archives to generate and analyze geographic topic models, showing how they can be mined to produce interesting revelations concerning various Civil War-related subjects. Finally, I develop a new geolocation technique for text-only corpora involving co-training between document-geolocation and toponym- resolution models, using a gazetteer to inject additional information into the training process. To evaluate this technique I develop a new metric, the closest toponym error distance, on which I show improvements compared with a baseline geolocator.Linguistic

Geospatial data analysis in Russia’s geoweb

The chapter examines the role of geospatial data in Russia’s online ecosystem. Facilitated by the rise of geographic information systems and user-generated content, the distribution of geospatial data has blurred the line between physical spaces and their virtual representations. The chapter discusses different sources of these data available for Digital Russian Studies (e.g., social data and crowdsourced databases) together with the novel techniques for extracting geolocation from various data formats (e.g., textual documents and images). It also scrutinizes different ways of using these data, varying from mapping the spatial distribution of social and political phenomena to investigating the use of geotag data for cultural practices’ digitization to exploring the use of geoweb for narrating individual and collective identities online

Providing Information and Public Outreach Across Three U.S. State Archaeology Offices During the Age of Open Access

Archaeology in the United States has been transformed into a mainstream, practical science over the past fifty years by Cultural Resource Management (CRM) and the federal regulations imposed by the National Historic Preservation Act (NHPA) of 1966. However, this form of archaeology has been plagued with criticisms since the NHPA’s enactment including issues of access and use of data maintained by state site files. State archaeology is publicly funded yet state and federal legislation often exempts CRM data from freedom of information laws. To mitigate this contradiction and stem the growing body of “gray literature”, new open-access (OA) technologies are being developed to connect the general and academic publics with archaeological research. This thesis explores to what extent a consensus exists between state outreach and access features and how the implemented polices could be adapted with recent developments in OA information systems. Understanding these questions requires information directly from state archaeologists. As such, seven interviews were conducted with personnel from three state archaeology departments across the country. To establish a survey of state policy offerings, these professionals were asked a series of questions as they related to their state’s implementation of data access and public outreach outlets. Furthermore, each interviewee provided comments on their state’s investment in OA development. Over sixty pages of transcripts revealed a consensus on the concepts of data access and public outreach. This agreement was checked by considerable variation in state policy offerings. A grounded theory analysis was applied to the transcripts to uncover why this variation in policy existed. Initially it appeared that underfunding of programs was the primary factor. However, a comprehensive assessment though revealed that concerns with data security led states to implement strict yet largely unstandardized data access and public outreach policies. As such, I put forth that state archaeology departments across the nation look to implement Open-Access data management systems like the Digital Index of North American Archaeology (DINNA). Implementing such systems will provide a new platform for efficient researching and help in the fight against the growing body of gray literature. What is more, systems like DINAA will act as a clearinghouse of linked comprehensive data sets for state archaeologists, academic, and CRM researchers to utilize for broad geographic analyses needed to understand the threats posed to archaeological sites nationwide

From Signal to Social : Steps Towards Pervasive Social Context

The widespread adoption of smartphones with advanced sensing, computing and data transfer capabilities has made scientific studies of human social behavior possible at a previously unprecedented scale. It has also allowed context-awareness to become a natural feature in many applications using features such as activity recognition and location information. However, one of the most important aspects of context remains largely untapped at scale, i.e. social interactions and social context. Social interaction sensing has been explored using smartphones and specialized hardware for research purposes within computational social science and ubiquitous computing, but several obstacles remain to make it usable in practice by applications at industrial scale. In this thesis, I explore methods of physical proximity sensing and extraction of social context information from user-generated data for the purpose of context-aware applications. Furthermore, I explore the application space made possible through these methods, especially in the class of use cases that are characterized by embodied social agency, through field studies and a case study.A major concern when collecting context information is the impact on user privacy. I have performed a user study in which I have surveyed the user attitudes towards the privacy implications of proximity sensing. Finally, I present results from quantitatively estimating the sensitivity of a simple type of context information, i.e. application usage, in terms of risk of user re-identification