International Workshop on Mining Actionable Insights from Social Networks

International Workshop on Mining Actionable Insights from Social Networks
MAISoN 2017:

Co-located with Tenth ACM International Web Search and Data Mining (WSDM) Conference in Cambridge, UK February 6-10, 2017.

The wide adoption of social network churns out an ocean of data which presents an interesting opportunity for mining the data and discover new knowledge to predict real-world outcomes. The enormity and high variance of the information that propagates through large user communities influence 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 attributes and contents of social network provides an opportunity to discover social structure characteristics, analyze action patterns qualitatively and quantitatively, and 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. In particular, marketers aggregate the opinions of the collective population to dynamically calibrate, anticipate and offer products and services that meet perpetually shifting consumer demands in a hyper-competitive marketplace. The traditional research in social network mainly focus on theories and methodologies on community discovery, pattern detection and evolution, behavioural analysis and anomaly and misbehaviour detection. The main distinguishing focus of this workshop will be the use of social media data for building predictive models that can be used to uncover hidden and unexpected aspects of user-generated content in order to extract actionable insight from them. The objectives will be to transform the insight into effective actions which could help organizations improve and refine their strategies.

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

We solicit original, unpublished and innovative research work on all aspects around, but not limited to, the following themes:
• Applications of Social Network Analysis
• 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
• Social networks and information/knowledge dissemination
◦ Topic and trend prediction
◦ Prediction of information diffusion patterns
◦ Identification of causality and correlation between event/topics/communities
• Information diffusion modeling with social networks
◦ Sentiment diffusion in social network
◦ Competitive intelligence from social networks
• Merging internal (proprietary) data with social data
• Search Behaviour Analytics with Social Networks
• Feature Engineering from Social Networks
• Social Networks and Recommender Systems
• Sentiment Analysis and Prediction on Social Networks
• Datasets and Evaluation methodologies for predictive modeling in social networks

Format and Submission
We invite the submission of regular research papers (6-8 pages) as well as position papers (2-4 pages). We recommend papers to be formatted according to the standard double-column ACM Guidelines. All papers will be peer reviewed by three reviewers. 
All submissions must be submitted in PDF format according to the guidelines through the Easychair installation:

Important Dates
• Submission: November 10, 2016
• Decisions: December 5, 2016
Submissions will be due Midnight AoE time.

• Ebrahim Bagheri, Ryerson University
• Zeinab Noorian, Ryerson University
• Faezeh Ensan, Ferdowsi University of Mashhad