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Posted to dev@flagon.apache.org by lewis john mcgibbney <le...@apache.org> on 2019/08/09 23:50:31 UTC
Fwd: FW: [EXTERNAL] [SIG-IRList] CFP: Information Retrieval Journal -
SI on Mining Actionable Insights from Online User Generated Content
---------- Forwarded message ---------
From: Mcgibbney, Lewis J (398M) <le...@jpl.nasa.gov>
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 <le...@apache.org>
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: lewis.j.mcgibbney@jpl.nasa.gov
ORCID: orcid.org/0000-0003-2185-928X
[image: signature_1031575970]
Dare Mighty Things
*From: *ACM SIGIR Mailing List <SI...@LISTSERV.ACM.ORG> on behalf of
Ebrahim Bagheri <eb...@GMAIL.COM>
*Reply-To: *Ebrahim Bagheri <eb...@GMAIL.COM>
*Date: *Thursday, August 8, 2019 at 4:48 PM
*To: *"SIGIR@LISTSERV.ACM.ORG" <SI...@LISTSERV.ACM.ORG>
*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://people.apache.org/keys/committer/lewismc
Re: [EXTERNAL] [SIG-IRList] CFP: Information Retrieval Journal - SI
on Mining Actionable Insights from Online User Generated Content
Posted by Joshua Poore <po...@apache.org>.
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 <le...@apache.org> wrote:
>
>
>
> ---------- Forwarded message ---------
> From: Mcgibbney, Lewis J (398M) <lewis.j.mcgibbney@jpl.nasa.gov <ma...@jpl.nasa.gov>>
> 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 <lewismc@apache.org <ma...@apache.org>>
>
>
>
>
>
>
> 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: lewis.j.mcgibbney@jpl.nasa.gov <ma...@jpl.nasa.gov>
> ORCID: orcid.org/0000-0003-2185-928X <http://orcid.org/0000-0003-2185-928X>
>
>
>
>
>
>
> Dare Mighty Things
>
>
>
> From: ACM SIGIR Mailing List <SIGIR@LISTSERV.ACM.ORG <ma...@LISTSERV.ACM.ORG>> on behalf of Ebrahim Bagheri <ebrahim.bagheri@GMAIL.COM <ma...@GMAIL.COM>>
> Reply-To: Ebrahim Bagheri <ebrahim.bagheri@GMAIL.COM <ma...@GMAIL.COM>>
> Date: Thursday, August 8, 2019 at 4:48 PM
> To: "SIGIR@LISTSERV.ACM.ORG <ma...@LISTSERV.ACM.ORG>" <SIGIR@LISTSERV.ACM.ORG <ma...@LISTSERV.ACM.ORG>>
> 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>