Lewis! Where have you been!? I’d love to pull something together with the community, but I’ve got my next kid coming next month…
J > On Aug 9, 2019, at 7:50 PM, lewis john mcgibbney <[email protected]> wrote: > > > > ---------- Forwarded message --------- > From: Mcgibbney, Lewis J (398M) <[email protected] > <mailto:[email protected]>> > Date: Fri, Aug 9, 2019 at 14:20 > Subject: FW: [EXTERNAL] [SIG-IRList] CFP: Information Retrieval Journal - SI > on Mining Actionable Insights from Online User Generated Content > To: lewis john mcgibbney <[email protected] <mailto:[email protected]>> > > > > > > > Dr. Lewis John McGibbney Ph.D., B.Sc.(Hons) > > Data Scientist III > > Computer Science for Data Intensive Applications Group (398M) > > Instrument Software and Science Data Systems Section (398) > > > <https://www.google.com/maps/search/4800+Oak+Grove+Drive+%0D%0A+%0D%0A+%0D%0A+Pasadena,+California+91109?entry=gmail&source=g>Jet > Propulsion Laboratory > > > <https://www.google.com/maps/search/4800+Oak+Grove+Drive+%0D%0A+%0D%0A+%0D%0A+Pasadena,+California+91109?entry=gmail&source=g> > > <https://www.google.com/maps/search/4800+Oak+Grove+Drive+%0D%0A+%0D%0A+%0D%0A+Pasadena,+California+91109?entry=gmail&source=g>California > Institute of Technology > > 4800 Oak Grove Drive > <https://www.google.com/maps/search/4800+Oak+Grove+Drive+%0D%0A+%0D%0A+%0D%0A+Pasadena,+California+91109?entry=gmail&source=g> > Pasadena, California 91109 > <https://www.google.com/maps/search/4800+Oak+Grove+Drive+%0D%0A+%0D%0A+%0D%0A+Pasadena,+California+91109?entry=gmail&source=g>-8099 > > Mail Stop : 158-256C > > Tel: (+1) (818)-393-7402 > > Cell: (+1) (626)-487-3476 > > Fax: (+1) (818)-393-1190 > > Email: [email protected] <mailto:[email protected]> > ORCID: orcid.org/0000-0003-2185-928X <http://orcid.org/0000-0003-2185-928X> > > > > > > > Dare Mighty Things > > > > From: ACM SIGIR Mailing List <[email protected] > <mailto:[email protected]>> on behalf of Ebrahim Bagheri > <[email protected] <mailto:[email protected]>> > Reply-To: Ebrahim Bagheri <[email protected] > <mailto:[email protected]>> > Date: Thursday, August 8, 2019 at 4:48 PM > To: "[email protected] <mailto:[email protected]>" > <[email protected] <mailto:[email protected]>> > Subject: [EXTERNAL] [SIG-IRList] CFP: Information Retrieval Journal - SI on > Mining Actionable Insights from Online User Generated Content > > > > 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 > > o Predict users daily activities including recurring events > > o User churn prediction > > o Determining user similarities, trustworthiness and reliability > > · Information/knowledge dissemination > > o Topic and trend prediction > > o Prediction of information diffusion patterns > > o Identification of causality and correlation between > event/topics/communities > > · Product adaptation models such as > > o Sale price prediction > > o New product popularity prediction > > o Brand popularity > > o Business downfall prediction > > · Information diffusion modeling > > o Information propagation and assimilation > > o Sentiment diffusion > > o Competitive intelligence > > · Social influence analysis > > o Systems and algorithms for discovering influential users > > o Recommending influential users > > o Influence maximization > > o Modeling social networks and behavior for discovering influential users > > o Discovering influencers for advertising and viral marketing > > o Decision support systems and influencer discovering > > · Analysis of Emerging User-Generated Content Platforms such as: > > o Email Analytics > > o Chatbots and Analysis of Automated Conversation Agents > > o Dialogue Systems > > o Weblogs and Wikis > > · Feature Engineering from User-Generated Content > > > > > > > > GUEST EDITORS > > · Marcelo G. Armentano > <http://marcelo.armentano.isistan.unicen.edu.ar/>, ISISTAN Research Institute > (CONICET- UNICEN), Argentina > > · Ebrahim Bagheri <https://www.ee.ryerson.ca/people/Bagheri.html>, > Ryerson University, Canada > > · Julia Kiseleva <http://juliakiseleva.com/>, Microsoft Research AI, > USA > > · Frank Takes <https://www.franktakes.nl/>, 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. > > -- > http://home.apache.org/~lewismc/ <http://home.apache.org/~lewismc/> > http://people.apache.org/keys/committer/lewismc > <http://people.apache.org/keys/committer/lewismc>
