Fwd: FW: [EXTERNAL] [SIG-IRList] CFP: Information Retrieval Journal - SI on Mining Actionable Insights from Online User Generated Content

[lewis john mcgibbney]

-- Forwarded message -
From: Mcgibbney, Lewis J (172B) 
Date: Tue, Oct 22, 2019 at 12:10
Subject: FW: [EXTERNAL] [SIG-IRList] CFP: Information Retrieval Journal -
SI on Mining Actionable Insights from Online User Generated Content
To: lewis john mcgibbney 






Dr. Lewis John McGibbney Ph.D., B.Sc.(Hons)

Enterprise Search Technologist

Web and Mobile Application Development Group (172B)

Application, Consulting, Development and Engineering Section (1722)

Info & Engineering Technology Planning and Development Division (1720)


Jet Propulsion Laboratory



California Institute of Technology

4800 Oak Grove Drive


Pasadena, California 91109

-8099

Mail Stop : 600-172A

Tel:  (+1) (818)-393-7402

Cell: (+1) (626)-487-3476

Fax:  (+1) (818)-393-1190

Email: lewis.j.mcgibb...@jpl.nasa.gov

ORCID: orcid.org/-0003-2185-928X



   [image: signature_2065373744]



 Dare Mighty Things



*From: *ACM SIGIR Mailing List  on behalf of
Ebrahim Bagheri 
*Reply-To: *Ebrahim Bagheri 
*Date: *Friday, October 11, 2019 at 1:08 AM
*To: *"si...@listserv.acm.org" 
*Subject: *[EXTERNAL] [SIG-IRList] CFP: Information Retrieval Journal - SI
on Mining Actionable Insights from Online User Generated Content



*I**nformation 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

oPredict users daily activities including recurring events

oUser churn prediction

oDetermining user similarities, trustworthiness and reliability

· Information/knowledge dissemination

oTopic and trend prediction

oPrediction of information diffusion patterns

oIdentification of causality and correlation between
event/topics/communities

· Product adaptation models such as

oSale price prediction

oNew product popularity prediction

oBrand popularity

oBusiness downfall prediction

· Information diffusion modeling

oInformation propagation and assimilation

oSentiment diffusion

oCom

Fwd: FW: [EXTERNAL] [SIG-IRList] CFP: Information Retrieval Journal - SI on Mining Actionable Insights from Online User Generated Content

[lewis john mcgibbney]

FYI folks

-- Forwarded message -
From: Mcgibbney, Lewis J (172B) 
Date: Fri, Sep 20, 2019 at 12:52 PM
Subject: FW: [EXTERNAL] [SIG-IRList] CFP: Information Retrieval Journal -
SI on Mining Actionable Insights from Online User Generated Content
To: lewis john mcgibbney 






Dr. Lewis John McGibbney Ph.D., B.Sc.(Hons)

Enterprise Search Technologist

Web and Mobile Application Development Group (172B)

Application, Consulting, Development and Engineering Section (1722)

Info & Engineering Technology Planning and Development Division (1720)

Jet Propulsion Laboratory

California Institute of Technology

4800 Oak Grove Drive

Pasadena, California 91109-8099

Mail Stop : 600-172A

Tel:  (+1) (818)-393-7402

Cell: (+1) (626)-487-3476

Fax:  (+1) (818)-393-1190

Email: lewis.j.mcgibb...@jpl.nasa.gov

ORCID: orcid.org/-0003-2185-928X



   [image: signature_1300440255]



 Dare Mighty Things



*From: *ACM SIGIR Mailing List  on behalf of
Ebrahim Bagheri 
*Reply-To: *Ebrahim Bagheri 
*Date: *Saturday, September 14, 2019 at 11:34 PM
*To: *"si...@listserv.acm.org" 
*Subject: *[EXTERNAL] [SIG-IRList] CFP: Information Retrieval Journal - SI
on Mining Actionable Insights from Online User Generated Content





*Inf**ormation 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

oPredict users daily activities including recurring events

oUser churn prediction

oDetermining user similarities, trustworthiness and reliability

· Information/knowledge dissemination

oTopic and trend prediction

oPrediction of information diffusion patterns

oIdentification of causality and correlation between
event/topics/communities

· Product adaptation models such as

oSale price prediction

oNew product popularity prediction

oBrand popularity

oBusiness downfall prediction

· Information diffusion modeling

oInformation propagation and assimilation

oSentiment diffusion

oCompetitive intelligence

· Social influence analysis

oSystems and algorithms for discovering influential users

oRecommending influential users

oInfluence maximization

oModeling social networks and behavior for discovering influential
users

oDiscovering influencers for advertising and viral marketing

oDecision support systems and influencer discovering

· Analysis of Emerging User-Generated Content Platforms such as:

oEmail Analytics

oChatbots and Analysis of Automated Conversation Agents

oDialogue Systems

oWeblogs and Wikis

· Feature En

Fwd: FW: [EXTERNAL] [SIG-IRList] CFP: Information Retrieval Journal - SI on Mining Actionable Insights from Online User Generated Content

[lewis john mcgibbney]

-- Forwarded message -
From: Mcgibbney, Lewis J (398M) 
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 






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)


Jet Propulsion Laboratory



California Institute of Technology

4800 Oak Grove Drive


Pasadena, California 91109

-8099

Mail Stop : 158-256C

Tel:  (+1) (818)-393-7402

Cell: (+1) (626)-487-3476

Fax:  (+1) (818)-393-1190

Email: lewis.j.mcgibb...@jpl.nasa.gov

ORCID: orcid.org/-0003-2185-928X



   [image: signature_1031575970]



 Dare Mighty Things



*From: *ACM SIGIR Mailing List  on behalf of
Ebrahim Bagheri 
*Reply-To: *Ebrahim Bagheri 
*Date: *Thursday, August 8, 2019 at 4:48 PM
*To: *"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

oPredict users daily activities including recurring events

oUser churn prediction

oDetermining user similarities, trustworthiness and reliability

· Information/knowledge dissemination

oTopic and trend prediction

oPrediction of information diffusion patterns

oIdentification of causality and correlation between
event/topics/communities

· Product adaptation models such as

oSale price prediction

oNew product popularity prediction

oBrand popularity

oBusiness downfall prediction

· Information diffusion modeling

oInformation propagation and assimilation

oSentiment diffusion

oCompetitive intelligence

· Social influence analysis

oSystems and algorithm