Submission Deadline: 31 October 2018
IEEE Access invites manuscript submissions in the area of Applications of Big Data in Social Sciences.
What does Big Data mean for contemporary Social Sciences? How can velocity, variety and volume of Big Data streams be employed to gain a better understanding of complex socio-economical facts? Is Big Data a viable tool to address social problems? As data becomes more and more valuable, who will own and control access to it?
With the rapid increase of the sheer amount of social data produced and that is available, a particular recent trend for researchers from Social Sciences is to understand the potential of Big Data in complementing traditional research methods and their value in making decisions. Indeed, Big Data requires a revisit of data analysis techniques in fundamental ways at all stages from data acquisition and storage to data transformation and interpretation. In particular, the task of collecting and analyzing data — which is at the heart of the Big Data Analytics pipeline — underwent pressing (and somewhat daunting) challenges in the domain of Social Sciences. The types of available data fall into various categories: social data (e.g., Twitter feeds, Facebook likes), data about mobility and geospatial locations (e.g., sensor data collected through mobile phones or satellite images), data collected from government administrative sources and multi-lingual text datasets, only to name a few. In addition, data is often fragmented across many sources and often requires translation from one language (or specific format) to another and, in some extreme cases, a translation between different scientific disciplines is needed.
Several major issues have to be closely investigated around Big Data in Social Sciences. First, missing data is a main concern for Social Science researchers, especially for those who aim to study the effectiveness of data-driven approaches in the decision-making process. Second, social data generated from human interactions are often unreliable. Data collection processes should therefore incorporate mechanisms to spot potential inaccuracies and quantify to what extent inaccuracies are reflected in the outcomes of the data analysis tasks. Finally, the speed at which social data is generated from humans interacting through the increasing number of platforms and the myriad of interacting devices poses several challenges for effective real-time responses.
This Special Section in IEEE Access aims at presenting the latest developments, trends, and research solutions of Big Data in Social Sciences. The topics of interest include, but are not limited to:
Data Heterogeneity issues in Social Sciences
Big Data applications and methods in Sociology, Politics, Economics
Novel data collection techniques for reliable Social data
Novel Infrastructures and architectures for data-driven social applications
Big Data infrastructures for Social Sciences and Humanities
Big Data analytics for social sciences in IoT
Ethical frameworks about privacy and informed consent
Social Media in Social Sciences and Politics
Using Big Data to test social theories
Predictive modeling of social behaviors
Applications concerning Big Data in Humanities and Art
Investigations about the impact of Big Data analytics on human behaviors
Programming frameworks and middleware for “agile’’ Big Data analytics
Machine Learning techniques for Big Data analytics in social sciences
Cognitive technologies for Big Data in social sciences
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Associate Editor: Pasquale De Meo, University of Messina, Italy
Fabrizio Messina, University of Catania, Italy
Michael Sheng, Macquarie University, Australia
Jianguo Yao, Shanghai Jiao Tong University (SJTU), China