Reducing Social Media Users' Biases to Predict the Outcome of Australian Federal Election 2019

Badhan Chandra Das; Md Musfique Anwar; Iqbal H. Sarker

doi:10.1109/CSDE50874.2020.9411633

Reducing Social Media Users' Biases to Predict the Outcome of Australian Federal Election 2019

Badhan Chandra Das
, Md Musfique Anwar
, Iqbal H. Sarker

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Scopus citations

Abstract

Several online social networking sites (OSNs) are being used as a medium of expressing any ideas, opinions, thoughts towards any regular or special issues and also for support or even oppose any social or political matters at the same time. At the age of this modern technology, in any country, people would like to post their views regarding any political party during the election period on such emerging OSNs in order to demonstrate their stands upon them. In this paper, we incorporated the tweets relevant to the Australian federal Election 2019, with a view to serve our primary purpose of predicting the outcome of it. We aggregated two efficacious techniques to extract the information from a large Twitter dataset to count a virtual support for each corresponding political group and propose an approach of reducing users' biases in OSNs to predict outcome of the election more efficiently. Our investigation finds close relevance with the original results of the election published by the Australian Electoral Commission.

Original language	English
Title of host publication	2020 IEEE Asia-Pacific Conference on Computer Science and Data Engineering, CSDE 2020
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781665419741
DOIs	https://doi.org/10.1109/CSDE50874.2020.9411633
State	Published - 16 Dec 2020
Externally published	Yes
Event	2020 IEEE Asia-Pacific Conference on Computer Science and Data Engineering, CSDE 2020 - Gold Coast, Australia Duration: 16 Dec 2020 → 18 Dec 2020

Publication series

Name	2020 IEEE Asia-Pacific Conference on Computer Science and Data Engineering, CSDE 2020

Conference

Conference	2020 IEEE Asia-Pacific Conference on Computer Science and Data Engineering, CSDE 2020
Country/Territory	Australia
City	Gold Coast
Period	16/12/20 → 18/12/20

Bibliographical note

Publisher Copyright:
© 2020 IEEE.

Keywords

Bias controlling function
Regular expression
User biases

ASJC Scopus subject areas

Artificial Intelligence
Computer Networks and Communications
Computer Science Applications
Information Systems and Management
Health Informatics
Communication

Access to Document

10.1109/CSDE50874.2020.9411633

Cite this

Das, B. C., Anwar, M. M., & Sarker, I. H. (2020). Reducing Social Media Users' Biases to Predict the Outcome of Australian Federal Election 2019. In 2020 IEEE Asia-Pacific Conference on Computer Science and Data Engineering, CSDE 2020 Article 9411633 (2020 IEEE Asia-Pacific Conference on Computer Science and Data Engineering, CSDE 2020). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CSDE50874.2020.9411633

Das, Badhan Chandra ; Anwar, Md Musfique ; Sarker, Iqbal H. / Reducing Social Media Users' Biases to Predict the Outcome of Australian Federal Election 2019. 2020 IEEE Asia-Pacific Conference on Computer Science and Data Engineering, CSDE 2020. Institute of Electrical and Electronics Engineers Inc., 2020. (2020 IEEE Asia-Pacific Conference on Computer Science and Data Engineering, CSDE 2020).

@inproceedings{767e9c7cbd644e79813530b2a81e4fab,

title = "Reducing Social Media Users' Biases to Predict the Outcome of Australian Federal Election 2019",

abstract = "Several online social networking sites (OSNs) are being used as a medium of expressing any ideas, opinions, thoughts towards any regular or special issues and also for support or even oppose any social or political matters at the same time. At the age of this modern technology, in any country, people would like to post their views regarding any political party during the election period on such emerging OSNs in order to demonstrate their stands upon them. In this paper, we incorporated the tweets relevant to the Australian federal Election 2019, with a view to serve our primary purpose of predicting the outcome of it. We aggregated two efficacious techniques to extract the information from a large Twitter dataset to count a virtual support for each corresponding political group and propose an approach of reducing users' biases in OSNs to predict outcome of the election more efficiently. Our investigation finds close relevance with the original results of the election published by the Australian Electoral Commission.",

keywords = "Bias controlling function, Regular expression, User biases",

author = "Das, \{Badhan Chandra\} and Anwar, \{Md Musfique\} and Sarker, \{Iqbal H.\}",

note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; 2020 IEEE Asia-Pacific Conference on Computer Science and Data Engineering, CSDE 2020 ; Conference date: 16-12-2020 Through 18-12-2020",

year = "2020",

month = dec,

day = "16",

doi = "10.1109/CSDE50874.2020.9411633",

language = "English",

series = "2020 IEEE Asia-Pacific Conference on Computer Science and Data Engineering, CSDE 2020",

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booktitle = "2020 IEEE Asia-Pacific Conference on Computer Science and Data Engineering, CSDE 2020",

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Das, BC, Anwar, MM & Sarker, IH 2020, Reducing Social Media Users' Biases to Predict the Outcome of Australian Federal Election 2019. in 2020 IEEE Asia-Pacific Conference on Computer Science and Data Engineering, CSDE 2020., 9411633, 2020 IEEE Asia-Pacific Conference on Computer Science and Data Engineering, CSDE 2020, Institute of Electrical and Electronics Engineers Inc., 2020 IEEE Asia-Pacific Conference on Computer Science and Data Engineering, CSDE 2020, Gold Coast, Australia, 16/12/20. https://doi.org/10.1109/CSDE50874.2020.9411633

Reducing Social Media Users' Biases to Predict the Outcome of Australian Federal Election 2019. / Das, Badhan Chandra; Anwar, Md Musfique; Sarker, Iqbal H.
2020 IEEE Asia-Pacific Conference on Computer Science and Data Engineering, CSDE 2020. Institute of Electrical and Electronics Engineers Inc., 2020. 9411633 (2020 IEEE Asia-Pacific Conference on Computer Science and Data Engineering, CSDE 2020).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Sarker, Iqbal H.

PY - 2020/12/16

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AB - Several online social networking sites (OSNs) are being used as a medium of expressing any ideas, opinions, thoughts towards any regular or special issues and also for support or even oppose any social or political matters at the same time. At the age of this modern technology, in any country, people would like to post their views regarding any political party during the election period on such emerging OSNs in order to demonstrate their stands upon them. In this paper, we incorporated the tweets relevant to the Australian federal Election 2019, with a view to serve our primary purpose of predicting the outcome of it. We aggregated two efficacious techniques to extract the information from a large Twitter dataset to count a virtual support for each corresponding political group and propose an approach of reducing users' biases in OSNs to predict outcome of the election more efficiently. Our investigation finds close relevance with the original results of the election published by the Australian Electoral Commission.

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Das BC, Anwar MM, Sarker IH. Reducing Social Media Users' Biases to Predict the Outcome of Australian Federal Election 2019. In 2020 IEEE Asia-Pacific Conference on Computer Science and Data Engineering, CSDE 2020. Institute of Electrical and Electronics Engineers Inc. 2020. 9411633. (2020 IEEE Asia-Pacific Conference on Computer Science and Data Engineering, CSDE 2020). doi: 10.1109/CSDE50874.2020.9411633

Reducing Social Media Users' Biases to Predict the Outcome of Australian Federal Election 2019

Abstract

Publication series

Conference

Bibliographical note

Keywords

ASJC Scopus subject areas

Access to Document

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Cite this