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Detection of Americans’ Behavior toward Islam on Facebook

Journal of ICT Research and Applications (JICTRA) · Vol. 16 No. 3 (2022) · pp. 300–312

DOI
10.5614/itbj.ict.res.appl.2022.16.3.7
ISSN
2338-5499, 2337-5787
Published
2022-12-31
Publisher
DRI - ITB
Language
eng

Abstract

Social network websites have become a rich place for detecting and analyzing people’s attitudes, perceptions, and feelings towards news, products, and other real-world issues. Facebook is a popular platform among different age groups and countries and is generally used to convey ideas about certain topics based on likes, comments and sharing. In recent years, one of the most controversial topics were the idea behind Islamophobia and other ideas built in people’s minds about Islam around the world. This research studied the public opinion of American citizens about Islam during the presidency of Donald Trump, as that period was rich in diversity of opinion between his supporters and detractors. In this paper, sentiment analysis was used to analyze American citizens’ behavior towards posts about Islam during Trump’s presidency in various states across the United States. Sentiment analysis was performed on Facebook posts and comments extracted from American news channels from the year 2017. Several machine learning methods were used to detect the polarity in the dataset. The highest classification accuracy among the classifiers used in this research was achieved using a logistic regression classifier, reaching 84%.

Keywords

Collaboration network of the authors

5 authors · 6 co-authorship links

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  • Jordan University of Science and Technology
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
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Co-authorOrganisationJoint articles
Mohammad AlrousanAffiliation unknown1
Suzan AmareenJordan University of Science and Technology1

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://journals.itb.ac.id/index.php/jictra/oai, first harvested 26 Okt 2026, last seen 1 Nov 2026.
Original metadata (oai_dc) 
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	<dc:title xml:lang="en-US">Detection of Americans’ Behavior toward Islam on Facebook</dc:title>
	<dc:creator>Abuein, Qusai</dc:creator>
	<dc:creator>Shatnawi, Mohammed Q.</dc:creator>
	<dc:creator>Ghazalat, Lujain</dc:creator>
	<dc:subject xml:lang="en-US">analysis</dc:subject>
	<dc:subject xml:lang="en-US">behavior</dc:subject>
	<dc:subject xml:lang="en-US">document frequency</dc:subject>
	<dc:subject xml:lang="en-US">frequency-inverse</dc:subject>
	<dc:subject xml:lang="en-US">logistic regession</dc:subject>
	<dc:subject xml:lang="en-US">sentiment analysis</dc:subject>
	<dc:description xml:lang="en-US">Social network websites have become a rich place for detecting and analyzing people’s attitudes, perceptions, and feelings towards news, products,  and other real-world issues. Facebook is a popular platform among different age groups and countries and is generally used to convey ideas about certain topics based on likes, comments and sharing. In recent years, one of the most controversial topics were the idea behind Islamophobia and other ideas built in people’s minds about Islam around the world. This research studied the public opinion of American citizens about Islam during the presidency of Donald Trump, as that period was rich in diversity of opinion between his supporters and detractors. In this paper, sentiment analysis was used to analyze American citizens’ behavior towards posts about Islam during Trump’s presidency in various states across the United States. Sentiment analysis was performed on Facebook posts and comments extracted from American news channels from the year 2017. Several machine learning methods were used to detect the polarity in the dataset. The highest classification accuracy among the classifiers used in this research was achieved using a logistic regression classifier, reaching 84%.</dc:description>
	<dc:publisher xml:lang="en-US">DRI - ITB</dc:publisher>
	<dc:date>2022-12-31</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
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	<dc:type xml:lang="en-US">Peer-reviewed Article</dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://journals.itb.ac.id/index.php/jictra/article/view/18292</dc:identifier>
	<dc:identifier>10.5614/itbj.ict.res.appl.2022.16.3.7</dc:identifier>
	<dc:source xml:lang="en-US">Journal of ICT Research and Applications; Vol. 16 No. 3 (2022); 300-312</dc:source>
	<dc:source>2338-5499</dc:source>
	<dc:source>2337-5787</dc:source>
	<dc:source>10.5614/itbj.ict.res.appl.2022.16.3</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://journals.itb.ac.id/index.php/jictra/article/view/18292/6081</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2023 Journal of ICT Research and Applications</dc:rights>
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