Engagingness and responsiveness behavior models on the Enron email network and its application to email reply order prediction

In email networks, user behaviors affect the way emails are sent and replied. While knowing these user behaviors can help to create more intelligent email services, there has not been much research into mining these behaviors. In this paper, we investigate user engagingness and responsiveness as two...

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Main Authors: ON, Byung-Won, LIM, Ee Peng, JIANG, Jing, TEOW, Loo Nin
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Language:English
Published: Institutional Knowledge at Singapore Management University 2013
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Online Access:https://ink.library.smu.edu.sg/sis_research/1980
https://ink.library.smu.edu.sg/context/sis_research/article/2979/viewcontent/Engagingness_and_Responsiveness_Behavior_Models_on_the_Enron_Emai.pdf
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spelling sg-smu-ink.sis_research-29792018-07-13T03:32:49Z Engagingness and responsiveness behavior models on the Enron email network and its application to email reply order prediction ON, Byung-Won LIM, Ee Peng JIANG, Jing TEOW, Loo Nin In email networks, user behaviors affect the way emails are sent and replied. While knowing these user behaviors can help to create more intelligent email services, there has not been much research into mining these behaviors. In this paper, we investigate user engagingness and responsiveness as two interaction behaviors that give us useful insights into how users email one another. Engaging users are those who can effectively solicit responses from other users. Responsive users are those who are willing to respond to other users. By modeling such behaviors, we are able to mine them and to identify engaging or responsive users. This paper proposes four types of models to quantify engagingness and responsiveness of users. These behaviors can be used as features in email reply order prediction, which predicts the email reply order given an email pair. Our experiments show that engagingness and responsiveness behavior features are more useful than other non-behavioral features in building a classifier for the email reply order prediction task. When combining behavior and non-behavior features, our classifier is also shown to predict the email reply order with good accuracy. This work was extended from the earlier conference paper that appeared in [9]. 2013-01-01T08:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/1980 info:doi/10.1007/978-3-7091-1346-2_10 https://ink.library.smu.edu.sg/context/sis_research/article/2979/viewcontent/Engagingness_and_Responsiveness_Behavior_Models_on_the_Enron_Emai.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University Communication Technology and New Media Databases and Information Systems
institution Singapore Management University
building SMU Libraries
continent Asia
country Singapore
Singapore
content_provider SMU Libraries
collection InK@SMU
language English
topic Communication Technology and New Media
Databases and Information Systems
spellingShingle Communication Technology and New Media
Databases and Information Systems
ON, Byung-Won
LIM, Ee Peng
JIANG, Jing
TEOW, Loo Nin
Engagingness and responsiveness behavior models on the Enron email network and its application to email reply order prediction
description In email networks, user behaviors affect the way emails are sent and replied. While knowing these user behaviors can help to create more intelligent email services, there has not been much research into mining these behaviors. In this paper, we investigate user engagingness and responsiveness as two interaction behaviors that give us useful insights into how users email one another. Engaging users are those who can effectively solicit responses from other users. Responsive users are those who are willing to respond to other users. By modeling such behaviors, we are able to mine them and to identify engaging or responsive users. This paper proposes four types of models to quantify engagingness and responsiveness of users. These behaviors can be used as features in email reply order prediction, which predicts the email reply order given an email pair. Our experiments show that engagingness and responsiveness behavior features are more useful than other non-behavioral features in building a classifier for the email reply order prediction task. When combining behavior and non-behavior features, our classifier is also shown to predict the email reply order with good accuracy. This work was extended from the earlier conference paper that appeared in [9].
format text
author ON, Byung-Won
LIM, Ee Peng
JIANG, Jing
TEOW, Loo Nin
author_facet ON, Byung-Won
LIM, Ee Peng
JIANG, Jing
TEOW, Loo Nin
author_sort ON, Byung-Won
title Engagingness and responsiveness behavior models on the Enron email network and its application to email reply order prediction
title_short Engagingness and responsiveness behavior models on the Enron email network and its application to email reply order prediction
title_full Engagingness and responsiveness behavior models on the Enron email network and its application to email reply order prediction
title_fullStr Engagingness and responsiveness behavior models on the Enron email network and its application to email reply order prediction
title_full_unstemmed Engagingness and responsiveness behavior models on the Enron email network and its application to email reply order prediction
title_sort engagingness and responsiveness behavior models on the enron email network and its application to email reply order prediction
publisher Institutional Knowledge at Singapore Management University
publishDate 2013
url https://ink.library.smu.edu.sg/sis_research/1980
https://ink.library.smu.edu.sg/context/sis_research/article/2979/viewcontent/Engagingness_and_Responsiveness_Behavior_Models_on_the_Enron_Emai.pdf
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