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International Workshop on Mining Actionable Insights from Social Networks
(MAISoN 2019)
Co-located with The 5th ACM SIGIR International Conference on the Theory of
Information Retrieval - October 2, 2019 Santa Clara, California - 


In the last 10 years, the dissemination and use of social networks have
grown significantly worldwide. Online social networks have billions of
users and are able to record hundreds of data from each of its users. The
wide adoption of social networks resulted in an ocean of data which
presents an interesting opportunity for performing data mining and
knowledge discovery in a real-world context. The enormity and high variance
of the information that propagates through large user communities
influences the public discourse in society and sets trends and agendas in
topics that range from marketing, education, business and medicine to
politics, technology and the entertainment industry. Mining the contents of
social networks provides an opportunity to discover social structure
characteristics, analyze action patterns qualitatively and quantitatively,
and gives the ability to predict future events. In recent years, decision
makers have become savvy about how to translate social data into actionable
information in order to leverage them for a competitive edge. Moreover,
 social networks expose different aspects of the social behavior of its

Traditional research in social network mining mainly focuses on theories
and methodologies for community discovery, pattern detection and evolution,
behavioural analysis and anomaly (misbehaviour) detection. While
interesting and definitely worthwhile, the main distinguishing focus of
this joint workshop will be the use of social network data for building
predictive models that can be used to uncover hidden and unexpected aspects
of user-generated content in order to extract actionable insights from them
and for analyzing different aspects of social influence, such as influence
maximization and discovering influencers. Thus, the focus is on algorithms
and methods for (social) network analysis, data mining techniques to gain
actionable real-world insights, and models and approaches for understanding
influence dissemination and discovering influential users in social

In this workshop, we invite researchers and practitioners, both from
academia and industry, from different disciplines such as computer science,
data mining, machine learning, network science, social network analysis and
other related areas to share their ideas and research achievements in order
to deliver technology and solutions for mining actionable insights from
social network data.


We solicit original, unpublished and innovative research work on all
aspects around, but not limited to, the following themes:
• Predictive modeling based on social networks, such as
◦ Box office prediction
◦ Election prediction
◦ Flu prediction
• Product adaptation models with social networks, such as
◦ Sale price prediction
◦ New product popularity prediction
◦ Brand popularity
◦ Business downfall prediction
• User modeling and social networks, including
◦ Predict users daily activities including recurring events
◦ User churn prediction
◦ Determining user similarities, trustworthiness and reliability
◦ Recommender systems
• Social networks and information/knowledge dissemination
◦ Topic and trend prediction
◦ Prediction of information diffusion patterns
◦ Identification of causality and correlation between
• Social network analysis and measures
◦ Network topology
◦ Centrality measures
◦ Community detection
◦ Dynamic network models
◦ Diffusion models
• Information diffusion modeling with social networks
◦ Information propagation and assimilation in social networks
◦ Sentiment diffusion in social networks
◦ Competitive intelligence from social networks
• Social influence analysis on online social networks
◦ Systems and algorithms for discovering influential users
◦ Recommending influential users in online social networks
◦ Social influence maximization
◦ Modeling social networks and behavior for discovering influential users
◦ Discovering influencers for advertising and viral marketing in social
◦ Decision support systems and influencer discovering
• Trust and reputation in social networks
• Merging internal (proprietary) data with social data
• Feature engineering from social networks
• Datasets and evaluation methodologies for predictive modeling in social

    •    Submission deadline: July 15, 2019
    •    Acceptance notification: August 2, 2019
    •    Camera ready version: August 16, 2019

The authors of accepted papers will be invited to submit a substantial
extension of their manuscript (with at least 30% additional content) to a
special issue of the Information Retrieval journal ( ). Previous special issues of MAISoN
have appeared in Information Systems Journal and Information Processing and
Management Journal by Elsevier.

    •    Marcelo G. Armentano, ISISTAN (CONICET-UNICEN), Argentina
    •    Ebrahim Bagheri, Ryerson University, Canada
    •    Julia Kiseleva, Microsoft AI, Seattle, Washington, United States
    •    Frank Takes, University of Amsterdam, The Netherlands


We invite the submission of regular research papers as well as position
papers. All submissions must be in English, in PDF format, and in ACM
two-column format. Long paper submissions should not exceed 8 pages.
Position papers should not exceed 4 pages. The LaTeX and Word templates of
the ICTIR conference  available from the ACM website should also be used
for your submission to the MAISoN workshop. All papers will be
peer-reviewed by three reviewers. All submissions must be submitted through

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