***** To join INSNA, visit http://www.insna.org ***** *Information Retrieval Journal* *Special Issue on Mining Actionable Insights from Online User Generated Content * *IMPORTANT DATES* * Submission deadline: Nov 1, 2019 * First Notification: Feb 1, 2020 * Revisions Due: April 1, 2020 * Final Notification: May 1, 2020 *AIM AND SCOPE * In the last 10 years, the dissemination and use of online platforms have grown significantly worldwide. For instance, online social networks have billions of users and are able to record hundreds of data from each of its users. The wide adoption of online content sharing platforms 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 user generated content provides an opportunity to discover user 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 user generated content into actionable information in order to leverage them for a competitive edge. Traditional research 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 special issue will be the use of user generated content for building predictive models that can be used to uncover hidden and unexpected aspects in order to extract actionable insights from them. 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 online user-generated content. *TOPICS OF INTEREST* We solicit original, unpublished and innovative research work on all aspects around, but not limited to, the following themes: - User modeling including - Predict users daily activities including recurring events - User churn prediction - Determining user similarities, trustworthiness and reliability - Information/knowledge dissemination - Topic and trend prediction - Prediction of information diffusion patterns - Identification of causality and correlation between event/topics/communities - Product adaptation models such as - Sale price prediction - New product popularity prediction - Brand popularity - Business downfall prediction - Information diffusion modeling - Information propagation and assimilation - Sentiment diffusion - Competitive intelligence - Social influence analysis - Systems and algorithms for discovering influential users - Recommending influential users - Influence maximization - Modeling social networks and behavior for discovering influential users - Discovering influencers for advertising and viral marketing - Decision support systems and influencer discovering - Analysis of Emerging User-Generated Content Platforms such as: - Email Analytics - Chatbots and Analysis of Automated Conversation Agents - Dialogue Systems - Weblogs and Wikis - Feature Engineering from User-Generated Content *GUEST EDITORS* - Marcelo G. Armentano <https://urldefense.proofpoint.com/v2/url?u=http-3A__marcelo.armentano.isistan.unicen.edu.ar_&d=DwIBaQ&c=sJ6xIWYx-zLMB3EPkvcnVg&r=yQQsvTNAnbvDXGM4nDrXAje4pr0qHX2qIOcCQtJ5k3w&m=mLKMA0lxRdgKGYbY42htC9k3TY3RsHS3Vuq6xtfRShQ&s=oFmNZNyWJyjlfDvmuCfxLA6BlBjrRpunGHGPlaujcLY&e= >, ISISTAN Research Institute (CONICET- UNICEN), Argentina - Ebrahim Bagheri <https://urldefense.proofpoint.com/v2/url?u=https-3A__www.ee.ryerson.ca_people_Bagheri.html&d=DwIBaQ&c=sJ6xIWYx-zLMB3EPkvcnVg&r=yQQsvTNAnbvDXGM4nDrXAje4pr0qHX2qIOcCQtJ5k3w&m=mLKMA0lxRdgKGYbY42htC9k3TY3RsHS3Vuq6xtfRShQ&s=gk1XpjWpGU56IOjhp9Pq596WsSygF3E0rBeiHEgTu-k&e= >, Ryerson University, Canada - Julia Kiseleva <https://urldefense.proofpoint.com/v2/url?u=http-3A__juliakiseleva.com_&d=DwIBaQ&c=sJ6xIWYx-zLMB3EPkvcnVg&r=yQQsvTNAnbvDXGM4nDrXAje4pr0qHX2qIOcCQtJ5k3w&m=mLKMA0lxRdgKGYbY42htC9k3TY3RsHS3Vuq6xtfRShQ&s=p8KQqi9L55zAiyVLZ5QIYurTXUj7evK4WnBjzhKbIHY&e= >, Microsoft Research AI, USA - Frank Takes <https://urldefense.proofpoint.com/v2/url?u=https-3A__www.franktakes.nl&d=DwIBaQ&c=sJ6xIWYx-zLMB3EPkvcnVg&r=yQQsvTNAnbvDXGM4nDrXAje4pr0qHX2qIOcCQtJ5k3w&m=mLKMA0lxRdgKGYbY42htC9k3TY3RsHS3Vuq6xtfRShQ&s=GXQaGyUTHIAbCzaPPOS23vmB5aKl0OfQAMlaCBwIBXI&e= >, University of Amsterdam, The Netherlands *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. At the beginning of the submission process in the submission system, authors need to select "*Mining Actionable Insights from Online User Generated Content*" 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. _____________________________________________________________________ SOCNET is a service of INSNA, the professional association for social network researchers (http://www.insna.org). To unsubscribe, send an email message to [log in to unmask] containing the line UNSUBSCRIBE SOCNET in the body of the message.