*Special Issue on Mining Actionable Insights from Social Networks* *Elsevier’s Information Systems Journal*
*http://ls3.rnet.ryerson.ca/?page_id=870 <http://ls3.rnet.ryerson.ca/?page_id=870>* Ebrahim Bagheri, Ryerson University, bagh...@ryerson.ca Faezeh Ensan, Ferdowsi University of Mashhad, en...@um.ac.ir Ioannis Katakis*, National and Kapodistrian University of Athens, ka...@di.uoa.gr Zeinab Noorian, Ryerson University, znoor...@ryerson.ca * Area Editor, Information Systems *Scope* In recent years, the industry has developed techniques that utilize social data to gain competitive advantage. Marketing tools for example can aggregate and analyze a large number of opinions published online in order to extract insights that can aid the interested parties to improve their services, products, and profile as perceived by the digital communities. Research on online social networks has led to theories and methodologies to understand communities and the phenomena that are linked with them (user interactions, community discovery, information dissemination within the network). This special issue focuses on techniques that enable the extraction of actionable insights from online social networks. In particular, this issue will publish papers on the theories, methodologies, techniques and tools that, in practice, can aid: organizations to improve their workflows, cities to provide better services, companies to refine their strategies, health institutions to react to an outbreak, among other use cases. This issue encourages the submission of high quality papers from multiple disciplines as well as inter-disciplinary research with a strong application element that gives evidence of the discovery of actionable (and useful) insights. Papers that study open data or make their data available online are more than welcome. This special issue is open to papers that use social data sources that have not been thoroughly discussed in the literature. A partial list of topics can be found below: ● Predictive modeling based on social networks (e.g. box office prediction, election prediction, virus spread tracking) ● Marketing management (e.g. sale price suggestion, new product sales prediction, brand popularity, forecasting business downfall) ● User modeling and social networks (e.g. predicting users’ daily activities including recurring actions, customer churn prediction, customer grouping, determining user’s trustworthiness, reliability and credibility) ● Social networks and information/knowledge dissemination (e.g. topic and trend prediction, modeling information diffusion, identification of causality and correlation among event/topics/communities) ● Information diffusion modeling within social networks (e.g. sentiment diffusion, competitive intelligence) ● Integrating and analyzing internal (proprietary) data with social data ● Feature engineering and extraction from Social Networks (e.g. linguistic features, social structure features, knowledge-based features) ● Datasets and Evaluation methodologies for predictive modeling in social networks *Timeline* Submission Deadline: 15 April 2017 First Notification: 15 July 2017 Revisions Due: 15 Sept 2017 Final Decision: 15 October 2017 *Paper Submission Details * Authors are invited to submit original research contributions to https://www.evise.com/profile/#/IS/login. Authors should state in their cover letter to the EiC that "This manuscript is submitted for the Special Issue on Mining Actionable Insights from Social Networks". *Furthermore, each paper has to be submitted to the separate track dedicated to this special issue in the journal submission site.* Submission instructions can be found under "Guide for Authors" at: https://www.journals.elsevier. com/information-systems/
_______________________________________________ uai mailing list uai@ENGR.ORST.EDU https://secure.engr.oregonstate.edu/mailman/listinfo/uai
