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In conjunction with the Federated AI Meeting (FAIM) / IJCAI-ECAI 2018
July 13-19, 2018
The wide adoption of social networks over the past years has 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, to 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 users. In this respect, many users of social networks are known as influencers. The influencers are users that usually publish their opinions about different topics, products and services on the social networks, and then affect intentionally or unintentionally the opinions, emotions, or behaviors of other users on the social networks. Because of the high impact of influencers on the opinions and behaviors of other users, many organizations are interested in discovering influencers on social networks to increase the promotion and sale of their products and services. However, the discovery of influencers on social networks is a very complex problem that requires developing models, techniques and algorithms for an appropriate analysis.
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 networks.
In this joint 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 insight from social network data.
TOPICS OF INTEREST
We solicit original, unpublished and innovative research work on all aspects around, but not limited to, the following themes:
Social networks and information/knowledge dissemination
Topic and trend prediction
Prediction of information diffusion patterns
Identification of causality and correlation between event/topics/communities
Social network analysis and measures
Dynamic network 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 networks
Decision support systems and influencer discovering
Predictive modeling based on social networks such as
Box office prediction
Product adaptation models with social networks such as
Sale price prediction
New product popularity prediction
Business downfall prediction
User modeling and social networks including
Predict users daily activities including recurring events
User churn prediction
Trust and reputation
Determining user similarities, trustworthiness and reliability
* Submission deadline: April 30, 2018
* Notification date: June 3, 2018
* Final version submission date : June 17, 2018
* Workshop: July 13-19, 2018 (exact date to be announced by IJCAI organization)
PROGRAM COMMITTEE CHAIRS
Marcelo G. Armentano, ISISTAN Research Institute (CONICET- UNICEN), Argentina
Ebrahim Bagheri, Ryerson University, Canada
Jérôme Kunegis, University of Namur, Belgium
Frank Takes, University of Amsterdam, The Netherlands
Jie Tang, Tsinghua University, China
Michalis Vazirgiannis, École Polytechnique, France
Virginia D. Yannibelli, ISISTAN Research Institute (CONICET- UNICEN), Argentina
SUBMISSION AND SELECTION PROCESS
Submitted papers must be no longer than seven pages (long papers, the last page may only contain references) or three pages (position papers), including all figures, tables etc., and should be formatted according to the style guide of IJCAI-ECAI 2018 Formatting Guidelines, LaTeX style or Word Template: (http://www.ijcai.org/authors_kit).
Papers should be submitted in PDF format, with no information about authors or affiliations (double blind review), through the EasyChair Conference System (https://easychair.org/conferences/?conf=socinfmaison18).
Each submitted paper to SocInf+MAISoN 2018 will be refereed by at least three members of the Workshop Program Committee, based on its originality, significance, technical soundness, and clarity of expression. Submissions must be in English, and can present mature research or experimental results as well as promising work in progress.
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 Elsevier Information Processing and Management journal.
*Please feel free to circulate this CFP among your colleagues and students.