Summary

This conference aims to address the challenges and opportunities that the "data revolution & AI" presents to social research, statistics, and computational sciences within the field of data science.

Description

As digital technologies, the internet and social media become increasingly integrated into society, a proliferation of digital footprints of human and societal behaviours are generated in our daily lives. All these data provide opportunities to study complex social systems, by the empirical observation of patterns in large-scale data, quantitative modelling and experiments. 

The social data revolution enables not only new business models but it also provides policy makers with better instruments to support their decisions.

The conference focuses on, but is not limited to, the following aspects:

• Methodological and Algorithmic Issues in Social Data Science

• Big data issues

• Data Security

• Decision Support Systems

• Delphi methodologies

• Empiricism and Knowledge

• Ensemble methods

• Features of data quality

• Knowledge-based methods and algorithms

• Knowledge coding and graphs

• Machine and Deep Learning

• Multivariate analysis

• Pattern recognition

• Resampling techniques

• Social data sources integration

• Sentiment analysis

• Social simulation models

• Structural Equation Modeling

• Symbolic data analysis

• Textual data analysis

• Technical solutions for ethical issues in Data Science and AI

Social Data Science application areas:

• Birth rate decline and population aging

• Communication

• Customer Journey

• Data Journalism

• Digital Humanities

• Education in social data science

• E-Politics

• Ethical aspects of Data Science and AI

• Future studies

• Game Studies

• Healthcare 4.0

• Open data for the social sciences

• Quality of life

• Quantitative methods for policy impact evaluation

• Smart cities

• Social inequalities

• Social networks

• Social Science ontologies

• Sustainability studies