Scope

There are two main themes of the conference

A. 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.

B. 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.

Topics

Authors are encouraged to submit previously unpublished original papers in the following areas: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.

Publication

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

    Mobile Networks and Applications (MONET) Journal (IF: 2.497)

All accepted authors are eligible to submit an extended version in a fast track of:

    EAI Endorsed Transactions on Security and Safety