Special Issue on Mining Social Influence and Actionable Insights from Social Networks
Elsevier’s Information Processing and Management Journal http://ls3.rnet.ryerson.ca/?p=949 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 hundreds 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. In this respect, many users of the 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 companies and organizations are interested in discovering influencers on social networks to increase the promotion and sale of their products and services. However, the discovering of influencers on social networks is a really 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 special issue, we solicit manuscripts from 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: - Predictive modeling based on social networks such as o Box office prediction o Election prediction o Flu prediction - Product adaptation models with social networks such as o Sale price prediction o New product popularity prediction o Brand popularity o Business downfall prediction - User modeling and social networks including o Predict users daily activities including recurring events o User churn prediction o Determining user similarities, trustworthiness and reliability - Social networks and information/knowledge dissemination o Topic and trend prediction o Prediction of information diffusion patterns o Identification of causality and correlation between event/topics/communities - Social network analysis and measures o Network topology o Centrality measures o Community detection o Dynamic network models o Diffusion models - Information diffusion modeling with social networks o Information propagation and assimilation in social networks o Sentiment diffusion in social networks o Competitive intelligence from social networks - Social influence analysis on online social networks o Systems and algorithms for discovering influential users o Recommending influential users in online social networks o Social influence maximization o Modeling social networks and behavior for discovering influential users o Discovering influencers for advertising and viral marketing in social networks o Decision support systems and influencer discovering - Trust and reputation in social networks - Merging internal (proprietary) data with social data - Feature Engineering from Social Networks - Datasets and Evaluation methodologies for predictive modeling in social networks IMPORTANT DATES * Submission deadline: October 15, 2018 * First Notification: Jan 1, 2019 * Revisions Due: Feb 1, 2019 * Final Notification: April 1, 2019 GUEST EDITORS • Marcelo G. Armentano, ISISTAN Research Institute (CONICET- UNICEN), Argentina • Ebrahim Bagheri, Ryerson University, Canada • Frank Takes, University of Amsterdam, The Netherlands • Virginia D. Yannibelli, ISISTAN Research Institute (CONICET- UNICEN), Argentina Paper Submission Details Papers submitted to this special issue for possible publication must be original and must not be under consideration for publication in any other journal or conference. Previously published or accepted conference papers must contain at least 30% new material to be considered for the special issue. All papers are to be submitted through the journal editorial submission system (https://www.evise.com/profile/#/IPM/login). At the beginning of the submission process in the submission system, authors need to select “Mining Social Influence and Actionable Insights from Social Networks” as the article type. All manuscripts must be prepared according to the journal publication guidelines which can also be found on the website provided above. Papers will be evaluated following the journal’s standard review process.
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