Analytics and Big Data for Social Goods and Collaboration in Digital Spaces
Special issue call for papers from Data Technologies and Applications
Social or common good is generally a result of an action or application that benefits society broadly. In the past, it was usually driven by governments and non-profit organizations following particular policies and campaigns. With the advancement of social media via computer-mediated technologies like WeChat, WhatsApp, Weibo, Twitter, Instagram, Facebook or YouTube, along with emerging decentralized applications, billions of registered users could create social media and interact globally and instantly. As a result, everyone has the opportunity to contribute to social networks and collaborative on-line communities and eventually contribute to social good and/or emerging collaborative efforts.
Tremendous growth of digital information (from granular data to aggregated data) is available for numerous applications with a social impact, for instance, environmental protection, clean energy, healthcare and education. Data analytics are ubiquitous and purpose-oriented in different forms: descriptive analytics, diagnostic analytics, predictive analytics and prescriptive analytics. Typical challenges for adopting big data technologies for social applications are data handling and storage, data quality, computational power, algorithm customization for special applications and security. Also importantly, applications on social sharing and collaboration require scaling and long-term sustainability.
This special issue aims to consolidate recent advances in big data for social good and collaboration in digital spaces. Pilot studies with an strong empirical basis and projects that are data-intensive or data-driven are especially welcome.
Topics of interest for the special issue include (but are not limited to):
- Innovative applications of data analytics to social issues like energy, healthcare, education, food, poverty, injustice, inequalities in society.
- Machine learning for applications for social good and collaboration.
- Advanced techniques for handling unstructured, unlabeled and/or missing data in social collaboration.
- Data quality control for the specifics of social and computer-mediated interaction.
- Application of data technologies and data science to social sciences studies in the digital realm.
- Emerging business for social evolution through data research.
- Social good frameworks and mechanisms enabled by data science research.
- Smart clusters for international and global social integration
- Multi-objective optimization, machine learning and intelligent techniques for social sharing, media and interaction.
- Meta-analysis of applications related to the topics.
- Security and privacy in analytics in social media.
- Co-simulation and other applications of simulation for social interaction.
- Decentralized approaches to data sharing applications, e.g. social media over blockchains or decentralized file systems.
Submissions to Data Technologies and Applications are made using ScholarOne Manuscripts, the online submission and peer review system. Registration and access is available at https://mc.manuscriptcentral.com/dtaa. If you are unable to find the information you need in the author guidelines or our author resources (http://emeraldgrouppublishing.com/authors/index.htm) section, please email [email protected] for assistance. Please quote the journal name, your contact details and the information your require.
Submission deadline: September 30th 2018
Author notification: November 2018
Final approval by Editor-in-chief: January 2019
Expected publication date: Q1 2019
Miltiadis D. Lytras, The American College of Greece, Greece, [email protected] (corresponding GE)
Anna Visvizi, The American College of Greece, Greece, [email protected] (corresponding GE)
Peiquan Jin, University of Science and Technology China, PR China, [email protected]
Naif Aljohani, King Abdulaziz University, Jeddah, Saudi Arabia, [email protected]