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*International Workshop on Mining Actionable Insights from Social
NetworksMAISoN 2017: http://ls3.rnet.ryerson.ca/MAISoN/2017/
<http://ls3.rnet.ryerson.ca/MAISoN/2017/>*
Co-located with Tenth ACM International Web Search and Data Mining (*WSDM*)
Conference in Cambridge, UK February 6-10, 2017.
http://www.wsdm-conference.org/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.


*Topics*
We solicit original, unpublished and innovative research work on all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:
https://easychair.org/conferences/?conf=maison2017


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


*Organizers*
    •    Ebrahim Bagheri, Ryerson University <bagheri at ryerson dot ca >
    •    Zeinab Noorian, Ryerson University
    •    Faezeh Ensan, Ferdowsi University of Mashhad

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