1st International Workshop on Social Media Analytics for Healthcare (SMA4H)

1st International Workshop on Social Media Analytics for Healthcare (SMA4H)

Held in conjunction with IEEE/WIC/ACM International Conference on Web Intelligence 2018 (WI’18)

The fast expansion of social media in the last few years is making available an enormous and continuous stream of user-generated contents containing invaluable information that can be used to understand, in near real time, human life dynamics. These massive quantities of data could support a wide range of medical and healthcare applications, including among others clinical trials and decision support, disease surveillance, personalized medicines and population health management.

In the public health area, especially, physicians could take a great advantage since the available huge data can be gathered faster and at a lower cost, compared to the traditional sources, mainly surveys. The pervasiveness and crowdsourcing power of social media data allow to model phenomena that was not possible before because either too expensive or outright impossible to answer, such as distribution of health information in a population, tracking health information trends over time and identifying gaps between health information supply and demand. Besides, social media combines textual, temporal, geographical and network data, opening up unique opportunities to study the interplay between human mobility, social structure and disease transmission.

Although social media analytics research for health applications is still very much its infancy, it received a great attention along recent years. Several research studies appeared including, influenza surveillance, pharmacovigilance, user behavioral patterns, drug abuse, depression, well-being, assisted living and tracking infectious/viral disease spread.

Aims and Topics

Big data analytics applications take advantage of such an explosion in social data to extract insights for improving healthcare. Social media data can be mined to obtain patterns and knowledge that can be leveraged in descriptive as well as predictive models of population health. By discovering associations and understanding patterns and trends within the data, big data analytics has the potential to improve the overall effectiveness of public health monitoring and analysis and significantly reduce its latency and costs.

The First International Workshop on Social Media Analytics for Healthcare (SMA4H) provides a venue to promote collaborations, present and exchange ideas, practices and advances specific to social media use in the particularly challenging area of health applications. It brings together individuals representing academia, public health researchers and practitioners and provides a forum for dialogue within and across different disciplines in the field of Web Intelligence: collective intelligence, data science, human-centric computing, knowledge management, network science, social network analysis, machine learning, statistical modelling, computational linguistics, epidemiology, sociology, and public health research. The workshop is an excellent opportunity to shape the future of health delivery, communication and practice by discussing novel approaches to big data analytics and mining methods that are applicable to social media data and may prove invaluable for health monitoring, surveillance, disease spreading and outbreaks prediction.

The workshop solicits empirical, experimental, methodological, and theoretical research reporting original and unpublished results on social media analysis and mining on topics in the realm of healthcare and health informatics along with applications to real life situations. This can mean new models, new datasets, new algorithms, or new applications.

Topics of interest include, but are not limited to:

Crowdsourcing of network health data
Methods for the automatic detection and extraction of health-related concepts
Data pre-processing and cleansing to deal with noise and missing data
Classifying and clustering of temporal health data in high dimensional spaces
Application of deep learning methods to health data
Novel architectures for scalable health data analysis and mining
Statistics and probability in large-scale health social data analysis
Community discovery and analysis
Large-scale graph algorithms for social network analysis
Social geography and spatial networks
Mobility mining
Spatio-temporal health data mining
Spatio-temporal prediction of pandemics
Methods for capturing outbreaks of infectious diseases
Modeling the health status and well-being of individuals
Models to predict the users’ moods from social posts
Real-time syndromic surveillance and early detection of emerging disease
Virus spread monitoring and modeling
Detect health-related topics of discussion and events
Drug abuse and alcoholism incidence monitoring
Methodologies and measures to understand patterns and trends for general public health research

Important dates

August 31, 2018: Paper Submission deadline
September 28, 2018: Paper Notification
October 12, 2018: Camera-Ready Paper Due
December 03, 2018: Workshop

Paper submission

Submitted papers must be unpublished and not considered elsewhere for publication. Submissions will undergo a rigorous review process handled by the Technical Program Committee. Papers will be selected based on their originality, significance, relevance, and clarity of presentation. Only electronic submissions in PDF format through the workshop submission site will be considered. Papers must be in English, up to 4 pages in the IEEE conference proceedings format, including references. The IEEE Proceedings Manuscript Formatting Guidelines can be found at: https://www.ieee.org/conferences/publishing/templates.html

Paper publication

At least one of the authors must register and present the paper, if accepted. Registered and presented papers will be included in the Workshop Proceedings published by IEEE. They will also be invited to be expanded and submitted for possible publication in an International Journal special issue.

Workshop Chairs

Carmela Comito, National Research Council, Institute of High Performance Computing and Networking (ICAR-CNR), Italy
Agostino Forestiero, National Research Council, Institute of High Performance Computing and Networking (ICAR-CNR), Italy
Clara Pizzuti, National Research Council, Institute of High Performance Computing and Networking (ICAR-CNR), Italy

Program Committee

Leopoldo Bertossi, Carleton University, Ottawa, Canada
Stefano Cagnoni, University of Parma, Italy
Andrea Calì, University of London, United Kingdom
Mario Ciampi, National Research Council (ICAR-CNR), Italy
Anne Laurent, LIRMM, University of Montpellier, France
Corrado Loglisci, University of Bari, Italy
David Manset, GNUBILA/MAAT, France
Monica Mordonini, University of Parma, Italy
Mehdi Sheikhalishahi, InnoTec21 GmbH, Germany
Paolo Trunfio, University of Calabria, Italy
Ester Zumpano, University of Calabria, Italy

Contacts

carmela.comito@icar.cnr.it
agostino.forestiero@icar.cnr.it
clara.pizzuti@icar.cnr.it

Workshop Website: http://sma4h.icar.cnr.it/
Conference Website: http://webintelligence2018.com/