Predicting short interval tracking polls with online social media

Ho Leung Li; Vincent To Yee Ng; Chi Keung Simon Shiu

doi:10.1109/CSCWD.2013.6581027

Predicting short interval tracking polls with online social media

Ho Leung Li, Vincent To Yee Ng, Chi Keung Simon Shiu

Research output: Chapter in book / Conference proceeding › Conference article published in proceeding or book › Academic research › peer-review

2 Citations (Scopus)

Abstract

The of behavioral patterns in online social media are often reflecting the happenings in our society. These patterns, which can be considered as opinions, are often correlated with public opinion polling. However, many correlation analyses done previously were for subsequent discoveries and not being able to handle short interval polling opinions. For opinions obtained from tracking polling with short opinion collection interval, like rolling polling, it cannot perform well in tracing the latest trends. This paper describes an extended correlation model for such kind of polling in examining the correlation between opinion in online social media and the public opinion from tracking poll. It has been tested with a recent rolling polling and it outperformed the previous correlation models.

Original language	English
Title of host publication	Proceedings of the 2013 IEEE 17th International Conference on Computer Supported Cooperative Work in Design, CSCWD 2013
Pages	587-592
Number of pages	6
DOIs	https://doi.org/10.1109/CSCWD.2013.6581027
Publication status	Published - 19 Sept 2013
Event	2013 IEEE 17th International Conference on Computer Supported Cooperative Work in Design, CSCWD 2013 - Whistler, BC, Canada Duration: 27 Jun 2013 → 29 Jun 2013

Conference

Conference	2013 IEEE 17th International Conference on Computer Supported Cooperative Work in Design, CSCWD 2013
Country/Territory	Canada
City	Whistler, BC
Period	27/06/13 → 29/06/13

Keywords

online social media
opinion tracking
prediction
public polling

ASJC Scopus subject areas

Computer Graphics and Computer-Aided Design
Software

More information

10.1109/CSCWD.2013.6581027

Cite this

@inproceedings{0408a0376a564da08572cc7dfc5a899c,

title = "Predicting short interval tracking polls with online social media",

abstract = "The of behavioral patterns in online social media are often reflecting the happenings in our society. These patterns, which can be considered as opinions, are often correlated with public opinion polling. However, many correlation analyses done previously were for subsequent discoveries and not being able to handle short interval polling opinions. For opinions obtained from tracking polling with short opinion collection interval, like rolling polling, it cannot perform well in tracing the latest trends. This paper describes an extended correlation model for such kind of polling in examining the correlation between opinion in online social media and the public opinion from tracking poll. It has been tested with a recent rolling polling and it outperformed the previous correlation models.",

keywords = "online social media, opinion tracking, prediction, public polling",

author = "Li, {Ho Leung} and Ng, {Vincent To Yee} and Shiu, {Chi Keung Simon}",

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doi = "10.1109/CSCWD.2013.6581027",

language = "English",

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pages = "587--592",

booktitle = "Proceedings of the 2013 IEEE 17th International Conference on Computer Supported Cooperative Work in Design, CSCWD 2013",

note = "2013 IEEE 17th International Conference on Computer Supported Cooperative Work in Design, CSCWD 2013 ; Conference date: 27-06-2013 Through 29-06-2013",

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Li, HL, Ng, VTY & Shiu, CKS 2013, Predicting short interval tracking polls with online social media. in Proceedings of the 2013 IEEE 17th International Conference on Computer Supported Cooperative Work in Design, CSCWD 2013., 6581027, pp. 587-592, 2013 IEEE 17th International Conference on Computer Supported Cooperative Work in Design, CSCWD 2013, Whistler, BC, Canada, 27/06/13. https://doi.org/10.1109/CSCWD.2013.6581027

Predicting short interval tracking polls with online social media. / Li, Ho Leung; Ng, Vincent To Yee; Shiu, Chi Keung Simon.
Proceedings of the 2013 IEEE 17th International Conference on Computer Supported Cooperative Work in Design, CSCWD 2013. 2013. p. 587-592 6581027.

Research output: Chapter in book / Conference proceeding › Conference article published in proceeding or book › Academic research › peer-review

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N2 - The of behavioral patterns in online social media are often reflecting the happenings in our society. These patterns, which can be considered as opinions, are often correlated with public opinion polling. However, many correlation analyses done previously were for subsequent discoveries and not being able to handle short interval polling opinions. For opinions obtained from tracking polling with short opinion collection interval, like rolling polling, it cannot perform well in tracing the latest trends. This paper describes an extended correlation model for such kind of polling in examining the correlation between opinion in online social media and the public opinion from tracking poll. It has been tested with a recent rolling polling and it outperformed the previous correlation models.

AB - The of behavioral patterns in online social media are often reflecting the happenings in our society. These patterns, which can be considered as opinions, are often correlated with public opinion polling. However, many correlation analyses done previously were for subsequent discoveries and not being able to handle short interval polling opinions. For opinions obtained from tracking polling with short opinion collection interval, like rolling polling, it cannot perform well in tracing the latest trends. This paper describes an extended correlation model for such kind of polling in examining the correlation between opinion in online social media and the public opinion from tracking poll. It has been tested with a recent rolling polling and it outperformed the previous correlation models.

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KW - opinion tracking

KW - prediction

KW - public polling

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ER -

Predicting short interval tracking polls with online social media

Abstract

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Keywords

ASJC Scopus subject areas

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