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OpenAlex Citation Counts

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OpenAlex is a bibliographic catalogue of scientific papers, authors and institutions accessible in open access mode, named after the Library of Alexandria. It's citation coverage is excellent and I hope you will find utility in this listing of citing articles!

If you click the article title, you'll navigate to the article, as listed in CrossRef. If you click the Open Access links, you'll navigate to the "best Open Access location". Clicking the citation count will open this listing for that article. Lastly at the bottom of the page, you'll find basic pagination options.

Requested Article:

Detecting and Measuring Depression on Social Media Using a Machine Learning Approach: Systematic Review
Danxia Liu, Xing Lin Feng, Farooq Ahmed, et al.
JMIR Mental Health (2021) Vol. 9, Iss. 3, pp. e27244-e27244
Open Access | Times Cited: 59

Showing 1-25 of 59 citing articles:

Pathways for Design Research on Artificial Intelligence
Ahmed Abbasi, Jeffrey Parsons, Gautam Pant, et al.
Information Systems Research (2024) Vol. 35, Iss. 2, pp. 441-459
Closed Access | Times Cited: 19
Deep learning techniques for suicide and depression detection from online social media: A scoping review
Anshu Malhotra, Rajni Jindal
Applied Soft Computing (2022) Vol. 130, pp. 109713-109713
Closed Access | Times Cited: 48
IIFDD: Intra and inter-modal fusion for depression detection with multi-modal information from Internet of Medical Things
Jian Chen, Yuzhu Hu, Qifeng Lai, et al.
Information Fusion (2023) Vol. 102, pp. 102017-102017
Closed Access | Times Cited: 26
Detecting depression of Chinese microblog users via text analysis: Combining Linguistic Inquiry Word Count (LIWC) with culture and suicide related lexicons
Sihua Lyu, Xiaopeng Ren, Yihua Du, et al.
Frontiers in Psychiatry (2023) Vol. 14
Open Access | Times Cited: 25
An optimized deep learning approach for suicide detection through Arabic tweets
Nadiah A. Baghdadi, Amer Malki, Hossam Magdy Balaha, et al.
PeerJ Computer Science (2022) Vol. 8, pp. e1070-e1070
Open Access | Times Cited: 26
Artificial intelligence assisted tools for the detection of anxiety and depression leading to suicidal ideation in adolescents: a review
Prabal Datta Barua, Jahmunah Vicnesh, Oh Shu Lih, et al.
Cognitive Neurodynamics (2022) Vol. 18, Iss. 1, pp. 1-22
Open Access | Times Cited: 20
A systematic review on automated clinical depression diagnosis
Kaining Mao, Yuqi Wu, Jie Chen
npj Mental Health Research (2023) Vol. 2, Iss. 1
Open Access | Times Cited: 12
Machine Learning Approaches for Detecting Signs of Depression from Social Media
Sarin Jickson, V. S. Anoop, S. Asharaf
Lecture notes in networks and systems (2023), pp. 201-214
Closed Access | Times Cited: 11
Identifying discernible indications of psychological well-being using ML: explainable AI in reddit social media interactions
Pahalage Dona Thushari, Nitisha Aggarwal, Vajratiya Vajrobol, et al.
Social Network Analysis and Mining (2023) Vol. 13, Iss. 1
Closed Access | Times Cited: 11
DECEN: A deep learning model enhanced by depressive emotions for depression detection from social media content
Zhijun Yan, Fei Peng, Dongsong Zhang
Decision Support Systems (2025), pp. 114421-114421
Closed Access
Mental health evaluation during internet blackouts: A case study of Bangladesh Quota Movement
Mohammad Rafi, Tahidul Islam
ITM Web of Conferences (2025) Vol. 72, pp. 02004-02004
Open Access
Artificial Intelligence in Diagnosing Depression Through Behavioural Cues: A Diagnostic Accuracy Systematic Review and Meta‐Analysis
Yong Shian Goh, Qi Rui See, Nopporn Vongsirimas, et al.
Journal of Clinical Nursing (2025)
Closed Access
Predicting depression levels on social media engagement: A machine learning approach
A. D. Dileep, Thyba Zakkir, Sangeetha Jayamohan
AIP conference proceedings (2025) Vol. 3281, pp. 020018-020018
Closed Access
Early Depression Detection from Social Media: State-of-the-Art Approaches
A. Alsaedi, Wael M. S. Yafooz
Studies in computational intelligence (2025), pp. 61-75
Closed Access
One shot intervention reduces online engagement with distorted content
Eeshan Hasan, Gunnar Paul Epping, Lorenzo Lorenzo‐Luaces, et al.
PNAS Nexus (2025) Vol. 4, Iss. 3
Open Access
Assisting Screening of Pediatric Depression with Large Language Models as Symptom Extractors: Pilot Study (Preprint)
Mariia Ignashina, Paulina Bondaronek, Dan Santel, et al.
(2025)
Closed Access
Mental Health in the Digital Era-NLP Models for Depression and Suicidal Tendency Detection
C. Rohan, V. M. Sapna
Lecture notes in electrical engineering (2025), pp. 349-359
Closed Access
Text-Based Depression Prediction on Social Media Using Machine Learning: Systematic Review and Meta-Analysis
Doreen Phiri, Frank Makowa, Vivi Leona Amelia, et al.
Journal of Medical Internet Research (2025) Vol. 27, pp. e59002-e59002
Open Access
Machine Learning-Based Detection of Stress Levels in Students with Social Media Addiction
Anuradha Parasar, N. Sujatha, S. Jeba Priya, et al.
(2025), pp. 1-6
Closed Access
A comprehensive survey on online social networks security and privacy issues: Threats, machine learning‐based solutions, and open challenges
Munmun Bhattacharya, Sandip Roy, Samiran Chattopadhyay, et al.
Security and Privacy (2022) Vol. 6, Iss. 1
Closed Access | Times Cited: 16
Mental Health Monitoring in the Digital Age
Mrignainy Kansal, Pancham Singh, Prashant Srivastava, et al.
Advances in medical technologies and clinical practice book series (2024), pp. 168-183
Closed Access | Times Cited: 3
Unraveling minds in the digital era: a review on mapping mental health disorders through machine learning techniques using online social media
Aysha Khan, Rashid Ali
Social Network Analysis and Mining (2024) Vol. 14, Iss. 1
Closed Access | Times Cited: 3
Effectiveness of artificial intelligence in detecting and managing depressive disorders: Systematic review
Yoonseo Park, Sewon Park, Munjae Lee
Journal of Affective Disorders (2024) Vol. 361, pp. 445-456
Closed Access | Times Cited: 3
An End-to-End framework for extracting observable cues of depression from diary recordings
Izidor Mlakar, Umut Ariöz, Urška Smrke, et al.
Expert Systems with Applications (2024) Vol. 257, pp. 125025-125025
Open Access | Times Cited: 3
Automatic depression prediction via cross-modal attention-based multi-modal fusion in social networks
Lidong Wang, Yin Zhang, Bin Zhou, et al.
Computers & Electrical Engineering (2024) Vol. 118, pp. 109413-109413
Closed Access | Times Cited: 2
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