SocInf+MAISoN 2018 : Joint International Workshop on Social Influence Analysis and Mining Actionable Insights from Social Networks

July 13~19, 2018, Stockholm, Sweden

Scope & Topics

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, behavioral analysis and anomaly (misbehavior) 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 :

• 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
• 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 networks
• Decision support systems and influencer discovering
• 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
• Trust and reputation
• Determining user similarities, trustworthiness and reliability

Important Dates

Submission Deadline	:	Apr 30, 2018
Authors Notification	:	Jun 3, 2018
Final Manuscript Due	:	Jun 17, 2018

User Name : advika
Posted 29-03-2018 on 16:10:41 AEDT

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