Gender Prediction of Indonesian Twitter Users Using Tweet and Profile Features

Mahendra, Rahmad; Putra, Hadi Syah; Faisal, Douglas Raevan; Rizki, Fadzil

doi:10.21609/jiki.v15i2.1079

Jurnal SINTA 2

Gender Prediction of Indonesian Twitter Users Using Tweet and Profile Features

Jurnal Ilmu Komputer dan Informasi (JIKI) · Vol. 15 No. 2 (2022) · pp. 131–141

DOI: 10.21609/jiki.v15i2.1079
ISSN: 2502-9274, 2088-7051
Published: 2022-07-02
Publisher: Faculty of Computer Science - Universitas Indonesia
Language: eng

Abstract

The increasing use of social media generates huge amounts of data which in turn triggers research into social media analytics. Social media contents can be analyzed to explore public opinion on an issue or provide the insights reflecting proxy indicators towards real-world events. Understanding the demographics of social media users can increase the potential for applications of sentiment analysis, topic modeling, and other analytical tasks. To map demographics, we need to know the latent attributes of users, such as age, gender, occupation and location of residence. Since this attribute is not directly available, we need to do some inference from the social media data. This study aims to predict the gender attribute given a Twitter user account. We conducted experiments with several supervised classifiers with feature extraction, including the use of word embedding representations. The results of this study indicate that the combination of features extracted from Tweet contents and user profile structured data can predict the gender of Twitter users in Indonesia with accuracy above 80%.

Keywords

Collaboration network of the authors

10 authors · 27 co-authorship links

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University of Indonesia
Affiliation unknown

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Co-author	Organisation	Joint articles
Fariz Darari	University of Indonesia	1
Muhammad Ilham Al Ghifari	University of Indonesia	1
Muhammad Zuhdi Zamrud	University of Indonesia	1
Marcellino Chris O'Vara	University of Indonesia	1
Berty Chrismartin Lumban Tobing	Affiliation unknown	1
On Lee	Affiliation unknown	1

Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.

Metadata source: OAI-PMH (metadata jurnal) https://jiki.cs.ui.ac.id/index.php/jiki/oai, first harvested 26 Okt 2026, last seen 26 Okt 2026.

Original metadata (oai_dc)

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	<dc:title xml:lang="en-US">Gender Prediction of Indonesian Twitter Users Using Tweet and Profile Features</dc:title>
	<dc:creator>Mahendra, Rahmad</dc:creator>
	<dc:creator>Putra, Hadi Syah</dc:creator>
	<dc:creator>Faisal, Douglas Raevan</dc:creator>
	<dc:creator>Rizki, Fadzil</dc:creator>
	<dc:subject xml:lang="en-US">gender</dc:subject>
	<dc:subject xml:lang="en-US">Twitter</dc:subject>
	<dc:subject xml:lang="en-US">user</dc:subject>
	<dc:subject xml:lang="en-US">classification</dc:subject>
	<dc:subject xml:lang="en-US">feature extraction</dc:subject>
	<dc:subject xml:lang="en-US">demography</dc:subject>
	<dc:description xml:lang="en-US">The increasing use of social media generates huge amounts of data which in turn triggers research into social media analytics. Social media contents can be analyzed to explore public opinion on an issue or provide the insights reflecting proxy indicators towards real-world events. Understanding the demographics of social media users can increase the potential for applications of sentiment analysis, topic modeling, and other analytical tasks. To map demographics, we need to know the latent attributes of users, such as age, gender, occupation and location of residence. Since this attribute is not directly available, we need to do some inference from the social media data. This study aims to predict the gender attribute given a Twitter user account. We conducted experiments with several supervised classifiers with feature extraction, including the use of word embedding representations. The results of this study indicate that the combination of features extracted from Tweet contents and user profile structured data can predict the gender of Twitter users in Indonesia with accuracy above 80%.</dc:description>
	<dc:publisher xml:lang="en-US">Faculty of Computer Science  - Universitas Indonesia</dc:publisher>
	<dc:date>2022-07-02</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
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	<dc:identifier>https://jiki.cs.ui.ac.id/index.php/jiki/article/view/1079</dc:identifier>
	<dc:identifier>10.21609/jiki.v15i2.1079</dc:identifier>
	<dc:source xml:lang="en-US">Jurnal Ilmu Komputer dan Informasi; Vol. 15 No. 2 (2022): Jurnal Ilmu Komputer dan Informasi (Journal of Computer Science and Information); 131-141</dc:source>
	<dc:source>2502-9274</dc:source>
	<dc:source>2088-7051</dc:source>
	<dc:source>10.21609/jiki.v15i2</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://jiki.cs.ui.ac.id/index.php/jiki/article/view/1079/464</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2022 Jurnal Ilmu Komputer dan Informasi</dc:rights>
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