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New insights gained from location-based social media data: VSI Preface for the special issue on New insights gained from location-based social media data
University of Gävle, Faculty of Engineering and Sustainable Development, Department of Industrial Development, IT and Land Management.ORCID iD: 0000-0002-2337-2486
2016 (English)In: Computers, Environment and Urban Systems, ISSN 0198-9715, E-ISSN 1873-7587, Vol. 58Article in journal, Editorial material (Refereed) Published
Abstract [en]

In the era of big data, increasingly sizeable datasets come from social media, particularly location-based social media, in the form that is widely known as user-generated contents. Many social media datasets are made available at the finest spatial and temporal scales. The availability of such data creates unprecedented opportunities for researchers to uncover what were previously hidden in the era of small data. What kinds of new research questions may be addressed with the available social media data? What are the social, ethical, and political implications of the wide use of social media platforms and the availability of such data? This special issue responds to the unique research opportunities and challenges from two broad perspectives. First, it looks at the need to develop new theories and data models for the management and analysis of social media data. Secondly, it advocates innovative acquisition and employment of social media data to enhance our understanding of human activities, social and spatial interactions, or the society as a whole. The inspiration for this special issue was the first ever International Conference on Location-based Social Media (ICLSM) held March 5-7, 2015 in Athens, Georgia, USA that brought together researchers from around the globe to discuss geosocial analysis and modeling of social media data. Geographers, GIScientists and social scientists gathered to report on the unique opportunities of collaboration and insights that can be gained from the analysis of location-based social media data collected from sources such as Facebook and Twitter. Participants shared innovative methods for social media data mining, big data analytics, social network analysis, social media data models and representations, human mobility and patterns of interaction.

Place, publisher, year, edition, pages
Elsevier, 2016. Vol. 58
National Category
Human Geography
URN: urn:nbn:se:hig:diva-22400OAI: oai:DiVA.org:hig-22400DiVA: diva2:971099

Edited By Dr. Xiaobai Yao, Prof. Bin Jiang, Prof. Yu Liu and Prof. Marguerite Madden

Available from: 2016-09-15 Created: 2016-09-15 Last updated: 2016-10-13Bibliographically approved

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