CALL FOR PAPERS The 4th International Workshop on Mining Actionable Insights from Social Networks (MAISoN 2020) Co-located with The Web Conference - 20-24 April 2020 Taipei - https://www2020.thewebconf.org
Website: https://www.maisonworkshop.org AIM AND SCOPE 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 large amounts 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 users. Traditional research in social network mining mainly focuses on theories and methodologies for community discovery, pattern detection and evolution, behavioral 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 and aforementioned methods 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 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. TOPICS OF INTEREST We solicit original, unpublished and innovative research work on all aspects of the theme of this workshop. The topics of interest include but are not limited to: - Descriptive modeling and analysis of real-world (social) networks. - Social network analysis and measures (network topology, centrality measures, community detection, dynamic network models, diffusion models). - Analysis of social media, viral marketing and the spreading of fake news. - Predictive modeling based on social networks such as box office prediction, election prediction, and flu prediction. - Product adaptation models with social networks such as sale price prediction, new product popularity prediction, brand popularity, and business downfall prediction. - Information diffusion modeling with social networks such as sentiment diffusion in social networks and competitive intelligence. - User modeling and social networks including predicting daily user activities, recurring events, user churn prediction. - Social networks and information/knowledge dissemination such as topic and trend prediction, prediction of information diffusion patterns, and identification of causality and correlation between event/topics/communities. - 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 influencer for advertising and viral marketing in social networks, decision support systems and influencer discovering). - Merging internal (proprietary) data with social data. - Trust and reputation in social networks. - Feature engineering for and from social networks. - New datasets and evaluation methodologies for predictive modeling in social networks. IMPORTANT DATES • Submission deadline: Jan 10, 2019 • Acceptance notification: Jan 31, 2020 • Camera ready version: Feb 20, 2020 PROGRAM COMMITTEE CHAIRS • Marcelo G. Armentano, ISISTAN (CONICET-UNICEN), Argentina • Ebrahim Bagheri, Ryerson University, Canada • Zeinab Noorian, Trent University, Canada • Frank Takes, Leiden University and University of Amsterdam, The Netherlands SUBMISSION AND SELECTION PROCESS 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 (https://www.acm.org/publications/proceedings-template). Long paper submissions should not exceed 8 pages. Position papers should not exceed 4 pages. The LaTeX and Word templates of the The Web 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 Easychair: https://easychair.org/conferences/?conf=maison20
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