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* ([[2012_CommunityDiscoveryandProfilingw|Zhou & al, 2012]]) ⇒ [[author::Wenjun Zhou]], [[author::Hongxia Jin]], and [[author::Yan Liu]]. ([[year::2012]]). "Community Discovery and Profiling with Social Messages." In: [[proceedings::Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining]] ([[conference::KDD-2012]]). ISBN:978-1-4503-1462-6 [http://dx.doi.org/10.1145/2339530.2339593 doi:10.1145/2339530.2339593]  
* ([[2012_CommunityDiscoveryandProfilingw|Zhou et al., 2012]]) [[author::Wenjun Zhou]], [[author::Hongxia Jin]], and [[author::Yan Liu]]. ([[year::2012]]). “Community Discovery and Profiling with Social Messages.In: [[proceedings::Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining]] ([[conference::KDD-2012]]). ISBN:978-1-4503-1462-6 [http://dx.doi.org/10.1145/2339530.2339593 doi:10.1145/2339530.2339593]


<B>Subject Headings:</B>  
<B>Subject Headings:</B>


==Notes==
== Notes ==


==Cited By==
==Cited By==
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* http://dl.acm.org/citation.cfm?id=2339530.2339593&preflayout=flat#citedby
* http://dl.acm.org/citation.cfm?id=2339530.2339593&preflayout=flat#citedby


==Quotes==
== Quotes ==
===Author Keywords===
 
=== Author Keywords ===
* [[Collaboration]]; [[community discovery]]; [[database application]]s; [[email]]; [[generative model]]s; [[social media]]
* [[Collaboration]]; [[community discovery]]; [[database application]]s; [[email]]; [[generative model]]s; [[social media]]


===Abstract===
=== Abstract ===


[[Discovering communiti]]es from [[social media]] and [[collaboration system]]s has been of great interest in recent years. </s>
[[Discovering communiti]]es from [[social media]] and [[collaboration system]]s has been of great interest in recent years. </s>
[[Existing work]] show prospects of [[modeling content]]s and [[social link]]s, aiming at [[discovering]] [[social communiti]]es, whose [[definition]] varies by [[application]]. </s>
[[Existing work]] show prospects of [[modeling content]]s and [[social link]]s, aiming at [[discovering social communiti]]es, whose [[definition]] varies by [[application]]. </s>
[[We]] believe that a [[community]] depends not only on the group of [[people]] who [[actively participate]], but also the topics they [[communicate]] about or [[collaborate]] on. </s>
[[We]] believe that a [[community]] depends not only on the [[group of people]] who [[actively participate]], but also the [[topic]]s they [[communicate]] about or [[collaborate]] on. </s>
This is especially true for workplace [[email communication]]s. </s>
This is especially true for [[workplace email communication]]s. </s>
Within an organization, it is not uncommon that [[employees multifunction]], and [[group]]s of employees collaborate on multiple projects at the same [[time]]. </s>
Within an [[organization]], it is not uncommon that [[employees multifunction]], and [[group]]s of [[employee]]s collaborate on multiple [[project]]s at the same time. </s>
In [[this paper]], we aim to automatically [[discovering and profiling users' communiti]]es by taking into account both the [[contact]]s and the [[topic]]s. </s>
[[In this paper, we]] aim to [[automatically discovering]] and [[profiling users'communiti]]es by taking into account both the [[contact]]s and the [[topic]]s. </s>
More specifically, we propose a [[community profiling model]] called [[COCOMP]], where the [[communities label]]s are [[latent]], and each [[social document]] corresponds to an [[information sharing activity]] among the most [[probable community]] members regarding the most [[relevant community issue]]s. </s>
More specifically, [[we]] propose a [[community profiling model]] called [[COCOMP]], where the [[communities label]]s are [[latent]], and each [[social document]] corresponds to an [[information sharing activity]] among the most [[probable]] [[community member]]s regarding the most relevant [[community issue]]s. </s>
[[Experiment result]]s on several [[social communication dataset]]s, including [[email]]s and [[Twitter message]]s, demonstrate that the [[model]] can [[discover]] [[users' communities effectively]], and provide concrete [[semantic]]s. </s>
[[Experiment result]]s on several [[social communication dataset]]s, including [[email]]s and [[Twitter message]]s, demonstrate that [[the model]] can [[discover]] [[users' communiti]]es effectively, and provide concrete [[semantics]]. </s>


==References==
== References ==
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{{#ifanon:|
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Latest revision as of 19:37, 20 December 2023

Subject Headings:

Notes

Cited By

Quotes

Author Keywords

Abstract

Discovering communities from social media and collaboration systems has been of great interest in recent years. Existing work show prospects of modeling contents and social links, aiming at discovering social communities, whose definition varies by application. We believe that a community depends not only on the group of people who actively participate, but also the topics they communicate about or collaborate on. This is especially true for workplace email communications. Within an organization, it is not uncommon that employees multifunction, and groups of employees collaborate on multiple projects at the same time. In this paper, we aim to automatically discovering and profiling users'communities by taking into account both the contacts and the topics. More specifically, we propose a community profiling model called COCOMP, where the communities labels are latent, and each social document corresponds to an information sharing activity among the most probable community members regarding the most relevant community issues. Experiment results on several social communication datasets, including emails and Twitter messages, demonstrate that the model can discover users' communities effectively, and provide concrete semantics.

References

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 AuthorvolumeDate ValuetitletypejournaltitleUrldoinoteyear
2012 CommunityDiscoveryandProfilingwYan Liu
Hongxia Jin
Wenjun Zhou
Community Discovery and Profiling with Social Messages10.1145/2339530.23395932012