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Identification of potential depression in social media posts

IAES International Journal of Artificial Intelligence · Vol. 14 No. 3 (2025) · hlm. 2096–2103

DOI
10.11591/ijai.v14.i3.pp2096-2103
ISSN
2252-8938, 2089-4872
Terbit
2025-06-01
Penerbit
Institute of Advanced Engineering and Science
Bahasa
eng
Lisensi
creativecommons.org/licenses/by-sa/4.0

Abstrak

The widespread use of social media to convey emotions (including depression) can be used to identify suspected depression in social media posts by examining the language that they have used on social media. This study aims to develop a system for detecting suspected depression in social media posts using sentiment analysis. This study collected data from X (Twitter) for three months using the keywords depression, mental health, and mental disorders. 1,502 data were generated due to the cleaning process of the 5,000 data collected. The findings of employing the validated by psychologist valence aware dictionary and sentiment reasoner (VADER) and Indonesian sentiment (InSet) lexicons demonstrate that VADER is more accurate (95.1%) than Inset (76.9%). The results of modeling with random forest, naive Bayes, and support vector machine (SVM) showed that random forest had the highest accuracy (83.3%), followed by naive Bayes (80.5%) and SVM (80.4%). Predicting social media data using lexicons and machine learning has limits that can be addressed by validation from clinical psychology. The frequency, timing, and idiom of posts on social media can reveal signs of depression. Depression seems to be best described by words like melancholy, stress, sadness, worthlessness, and depression.

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  • Universitas Esa Unggul
  • Universitas Islam Negeri Ar-Raniry
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Rekan penulisOrganisasiArtikel bersama
Puteri SejatiUniversitas Esa Unggul1
Marzuki PilliangUniversitas Esa Unggul1
Habibullah AkbarUniversitas Esa Unggul1
Arif MugionoUniversitas Esa Unggul1
Mira MaisuraUniversitas Islam Negeri Ar-Raniry1
Chairan M. NurUniversitas Islam Negeri Ar-Raniry1

Afiliasi berasal dari OpenAlex; metadata OAI-PMH jurnal tidak memuatnya. Penulis tanpa ID OpenAlex dicocokkan berdasarkan nama, sehingga dua orang bisa tergabung atau satu orang bisa terpisah.

Sumber metadata: OAI-PMH (metadata jurnal) http://ijai.iaescore.com/index.php/IJAI/oai, pertama di-harvest 8 Nov 2026, terakhir terlihat 8 Nov 2026.
Metadata asli (oai_dc) 
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	<dc:title xml:lang="en-US">Identification of potential depression in social media posts</dc:title>
	<dc:creator>Munawar, Munawar</dc:creator>
	<dc:creator>Yulhendri, Yulhendri</dc:creator>
	<dc:subject xml:lang="en-US">Machine learning; Random Forest; Naive Bayes; SVM</dc:subject>
	<dc:subject xml:lang="en-US">Depression; Naïve Bayes; Random forest; Sentiment analysis; Social media; Support vector machine</dc:subject>
	<dc:description xml:lang="en-US">The widespread use of social media to convey emotions (including depression) can be used to identify suspected depression in social media posts by examining the language that they have used on social media. This study aims to develop a system for detecting suspected depression in social media posts using sentiment analysis. This study collected data from X (Twitter) for three months using the keywords depression, mental health, and mental disorders. 1,502 data were generated due to the cleaning process of the 5,000 data collected. The findings of employing the validated by psychologist valence aware dictionary and sentiment reasoner (VADER) and Indonesian sentiment (InSet) lexicons demonstrate that VADER is more accurate (95.1%) than Inset (76.9%). The results of modeling with random forest, naive Bayes, and support vector machine (SVM) showed that random forest had the highest accuracy (83.3%), followed by naive Bayes (80.5%) and SVM (80.4%). Predicting social media data using lexicons and machine learning has limits that can be addressed by validation from clinical psychology. The frequency, timing, and idiom of posts on social media can reveal signs of depression. Depression seems to be best described by words like melancholy, stress, sadness, worthlessness, and depression.</dc:description>
	<dc:publisher xml:lang="en-US">Institute of Advanced Engineering and Science</dc:publisher>
	<dc:contributor xml:lang="en-US"></dc:contributor>
	<dc:date>2025-06-01</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:type xml:lang="en-US"></dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://ijai.iaescore.com/index.php/IJAI/article/view/24865</dc:identifier>
	<dc:identifier>10.11591/ijai.v14.i3.pp2096-2103</dc:identifier>
	<dc:source xml:lang="en-US">IAES International Journal of Artificial Intelligence (IJ-AI); Vol 14, No 3: June 2025; 2096-2103</dc:source>
	<dc:source>2252-8938</dc:source>
	<dc:source>2089-4872</dc:source>
	<dc:source>10.11591/ijai.v14.i3</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://ijai.iaescore.com/index.php/IJAI/article/view/24865/14908</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2025 Institute of Advanced Engineering and Science</dc:rights>
	<dc:rights xml:lang="en-US">http://creativecommons.org/licenses/by-sa/4.0</dc:rights>
</oai_dc:dc>