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Trajectory-based Methods to Predict ...

ProQuest Information and Learning Co.

Trajectory-based Methods to Predict User Churn in Online Health Communities.

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	Trajectory-based Methods to Predict User Churn in Online Health Communities./
Author:	Joshi, Apoorva.
Description:	1 online resource (45 pages)
Notes:	Source: Masters Abstracts International, Volume: 58-01.
Contained By:	Masters Abstracts International58-01(E).
Subject:	Computer science. -
Online resource:	click for full text (PQDT)
ISBN:	9780438153011

Trajectory-based Methods to Predict User Churn in Online Health Communities.
Joshi, Apoorva.

Trajectory-based Methods to Predict User Churn in Online Health Communities. - 1 online resource (45 pages)

Source: Masters Abstracts International, Volume: 58-01.

Thesis (M.S.)--The University of Iowa, 2018.

Includes bibliographical references

Online Health Communities (OHCs) have positively disrupted the modern global healthcare system as patients and caregivers are interacting online with similar peers to improve quality of their life. Social support is the pillar of OHCs and, hence, analyzing the different types of social support activities contributes to a better understanding and prediction of future user engagement in OHCs.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2018

Mode of access: World Wide Web

ISBN: 9780438153011Subjects--Topical Terms:

573171
Computer science.
Index Terms--Genre/Form:

554714
Electronic books.

Trajectory-based Methods to Predict User Churn in Online Health Communities.
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100 1 $a Joshi, Apoorva. $3 1190735
245 1 0 $a Trajectory-based Methods to Predict User Churn in Online Health Communities.
264 0 $c 2018
300 $a 1 online resource (45 pages)
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
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500 $a Source: Masters Abstracts International, Volume: 58-01.
500 $a Advisers: Kang Zhao; Sudhakar M. Reddy.
502 $a Thesis (M.S.)--The University of Iowa, 2018.
504 $a Includes bibliographical references
520 $a Online Health Communities (OHCs) have positively disrupted the modern global healthcare system as patients and caregivers are interacting online with similar peers to improve quality of their life. Social support is the pillar of OHCs and, hence, analyzing the different types of social support activities contributes to a better understanding and prediction of future user engagement in OHCs.
520 $a This thesis used data from a popular OHC, called Breastcancer.org, to first classify user posts in the community into the different categories of social support using Word2Vec for language processing and six different classifiers were explored, resulting in the conclusion that Random Forest was the best approach for classification of the user posts. This exercise helped identify the different types of social support activities that users participate in and also detect the most common type of social support activity among users in the community.
520 $a Thereafter, three trajectory-based methods were proposed and implemented to predict user churn (attrition) from the OHC. Comparison of the proposed trajectory-based methods with two non-trajectory-based benchmark methods helped establish that user trajectories, which represent the month-to-month change in the type of social support activity of users are effective pointers for user churn from the community.
520 $a The results and findings from this thesis could help OHC managers better understand the needs of users in the community and take necessary steps to improve user retention and community management.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2018
538 $a Mode of access: World Wide Web
650 4 $a Computer science. $3 573171
650 4 $a Artificial intelligence. $3 559380
650 4 $a Web studies. $3 1148502
655 7 $a Electronic books. $2 local $3 554714
690 $a 0984
690 $a 0800
690 $a 0646
710 2 $a ProQuest Information and Learning Co. $3 1178819
710 2 $a The University of Iowa. $b Electrical and Computer Engineering. $3 1179482
773 0 $t Masters Abstracts International $g 58-01(E).
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10752210 $z click for full text (PQDT)

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