There are two main themes of the conference:
A) Social Media Data Analysis in Health: Research suggests that more and more people rely on online sources for health information including symptoms, treatments and general health-related advice. Moreover, the user behaviour currently active on social media demonstrates an openness to share facts related to their current health status. Such data could be used to provide real-time tracking and prediction of the spread of disease and other health concerns, or provide vital information about the effectiveness of the public health awareness strategies of health agencies such as Health Canada, the Centre for Disease Control (CDC) or the World Health Organization (WHO). However, the current understanding of online health data produced through social networks is limited in important ways: (a) existing databases are project specific and data gathering mechanisms are time-constrained; (b) existing health-tracking tools depend on single interfaces such as Facebook, Twitter or Instagram, and (c) there is a lack of capacity for real-time mapping of health issues.
B) Social Data and Artificial Intelligence: One of the themes of this conference is to analyze and formulate new perspective of social theories and develop computational algorithms using large scale data sets. The research articles should incorporate different computational algorithms and data analysis techniques to address the problem of inconsistencies, inaccuracies and inadequacies of social data for policy making. The use of advanced artificial intelligence algorithms and technologies is encouraged.
Authors are encouraged to submit previously unpublished original papers in the following areas:
- Social Data inadequacies and inconsistencies
- Predictive models of social behaviours
- Infrastructure and architecture for testing social theories
- Data collection and analysis platforms
- Relevance of IoT for social science theories
- Building capacity to continuously collect data across a range of social media networks
- Real-time tracking and filtering of noisy social media datasets
- Designing decision-making tools to empower organizations in harnessing social media and AI for their campaigns
- Cross-validating the predictive models of social media datasets with ground truth data
- Developing frameworks and algorithms to perform real-time analysis of social media datasets.
All registered papers will be submitted for publication by Springer and made available through SpringerLink Digital Library.
Proceedings will be submitted for inclusion in leading indexing services, Ei Compendex, ISI Web of Science, Scopus, CrossRef, Google Scholar, DBLP, as well as EAI’s own EU Digital Library (EUDL).
Authors of selected best accepted and presented papers will be invited to submit an extended version to
- Big Data Driven IoT for Smart Cities (IF:3.031) (with additional article processing fee)
- Mobile Networks and Applications (MONET) Journal (IF: 2.390)
All accepted authors are eligible to submit an extended version in a fast track of: