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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:

Multi-level Attention Network using Text, Audio and Video for Depression Prediction
Anupama Ray, Siddharth Krishna Kumar, Rutvik Reddy, et al.
(2019), pp. 81-88
Open Access | Times Cited: 97

Showing 26-50 of 97 citing articles:

A Prompt-Based Topic-Modeling Method for Depression Detection on Low-Resource Data
Yanrong Guo, Jilong Liu, Lei Wang, et al.
IEEE Transactions on Computational Social Systems (2023) Vol. 11, Iss. 1, pp. 1430-1439
Closed Access | Times Cited: 12
Machine Learning-Based Multi-Modal and Multi-Granularity Feature Fusion Framework for Accurate Prediction of Molecular Properties
Shihao Nan, Zhongmei Li, Saimeng Jin, et al.
Industrial & Engineering Chemistry Research (2025)
Closed Access
Transformer-based fusion model for mild depression recognition with EEG and pupil area signals
Jing Zhu, Yuanlong Li, Changlin Yang, et al.
Medical & Biological Engineering & Computing (2025)
Closed Access
Identifying Depressive Symptoms from Tweets: Figurative Language Enabled Multitask Learning Framework
Shweta Yadav, Jainish Chauhan, Joy Prakash Sain, et al.
Proceedings of the 17th international conference on Computational linguistics - (2020), pp. 696-709
Open Access | Times Cited: 30
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
Prospects for the Use of Machine Learning for Mood Disorders
Ekaterina Mosolova, A. Е. Alfimov, E G Kostyukova, et al.
Digital Diagnostics (2025)
Open Access
Sentence-level multi-modal feature learning for depression recognition
Guanghua Zhang, Guangping Zhuo, Yang Yang, et al.
Frontiers in Psychiatry (2025) Vol. 16
Open Access
Automatic Depression Detection via Learning and Fusing Features From Visual Cues
Yanrong Guo, Chenyang Zhu, Shijie Hao, et al.
IEEE Transactions on Computational Social Systems (2022) Vol. 10, Iss. 5, pp. 2806-2813
Open Access | Times Cited: 15
Exploring the Capabilities of a Language Model-Only Approach for Depression Detection in Text Data
Misha Sadeghi, Bernhard Egger, Reza Agahi, et al.
IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI ...) (2023) Vol. 5, pp. 1-5
Closed Access | Times Cited: 8
Reading Between the Frames: Multi-modal Depression Detection in Videos from Non-verbal Cues
David Gimeno-Gómez, Ana-Maria Bucur, Adrian Cosma, et al.
Lecture notes in computer science (2024), pp. 191-209
Closed Access | Times Cited: 3
A Multimodal Approach for Detection and Assessment of Depression Using Text, Audio and Video
Wei Zhang, Kaining Mao, Jie Chen
Phenomics (2024) Vol. 4, Iss. 3, pp. 234-249
Closed Access | Times Cited: 3
Harnessing emotions for depression detection
Sahana M. Prabhu, Himangi Mittal, Rajesh Varagani, et al.
Pattern Analysis and Applications (2021) Vol. 25, Iss. 3, pp. 537-547
Closed Access | Times Cited: 18
Distinguishing apathy and depression in older adults with mild cognitive impairment using text, audio, and video based on multiclass classification and shapely additive explanations
Ying Zhou, Xiuyu Yao, Wei Han, et al.
International Journal of Geriatric Psychiatry (2022) Vol. 37, Iss. 11
Closed Access | Times Cited: 14
Review of automated depression detection: Social posts, audio and video, open challenges and future direction
Uma Yadav, Ashish K. Sharma, Dipti Patil
Concurrency and Computation Practice and Experience (2022) Vol. 35, Iss. 1
Closed Access | Times Cited: 14
Scoping Review on the Multimodal Classification of Depression and Experimental Study on Existing Multimodal Models
Umut Ariöz, Urška Smrke, Nejc Plohl, et al.
Diagnostics (2022) Vol. 12, Iss. 11, pp. 2683-2683
Open Access | Times Cited: 14
Depression prediction based on BiAttention-GRU
Yongzhong Cao, Yameng Hao, Bin Li, et al.
Journal of Ambient Intelligence and Humanized Computing (2022) Vol. 13, Iss. 11, pp. 5269-5277
Closed Access | Times Cited: 13
DEPAC: a Corpus for Depression and Anxiety Detection from Speech
Mashrura Tasnim, Malikeh Ehghaghi, Brian Diep, et al.
(2022)
Open Access | Times Cited: 13
Domain Adaptation for Enhancing Speech-Based Depression Detection in Natural Environmental Conditions Using Dilated CNNs
Zhaocheng Huang, Julien Epps, Dale Joachim, et al.
Interspeech 2022 (2020), pp. 4561-4565
Open Access | Times Cited: 20
A review and meta-analysis of machine intelligence approaches for mental health issues and depression detection
Ravita Chahar, Ashutosh Kumar Dubey, Sushil Kumar Narang
International Journal of Advanced Technology and Engineering Exploration (2021) Vol. 8, Iss. 83
Open Access | Times Cited: 18
Objective Prediction of Next-Day’s Affect Using Multimodal Physiological and Behavioral Data: Algorithm Development and Validation Study
Salar Jafarlou, Jocelyn Lai, Iman Azimi, et al.
JMIR Formative Research (2022) Vol. 7, pp. e39425-e39425
Open Access | Times Cited: 12
Fusing Multi-Level Features from Audio and Contextual Sentence Embedding from Text for Interview-Based Depression Detection
Junqi Xue, Ruihan Qin, Xinxu Zhou, et al.
ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (2024), pp. 6790-6794
Closed Access | Times Cited: 2
Approximating the Mental Lexicon from Clinical Interviews as a Support Tool for Depression Detection
Esaú Villatoro-Tello, Gabriela Ramírez-de-la-Rosa, Daniel Gática-Pérez, et al.
(2021), pp. 557-566
Closed Access | Times Cited: 15
Mutual Information Based Fusion Model (MIBFM): Mild Depression Recognition Using EEG and Pupil Area Signals
Jing Zhu, Changlin Yang, Xiannian Xie, et al.
IEEE Transactions on Affective Computing (2022) Vol. 14, Iss. 3, pp. 2102-2115
Closed Access | Times Cited: 11
Gamification Design Elements to Enhance Adolescent Motivation in Diagnosing Depression
Nurul Fasihah Jamaludin, Tengku Siti Meriam Tengku Wook, Siti Fadzilah Mat Noor, et al.
International Journal of Interactive Mobile Technologies (iJIM) (2021) Vol. 15, Iss. 10, pp. 154-154
Open Access | Times Cited: 14
Cost-effective Models for Detecting Depression from Speech
Mashrura Tasnim, Jekaterina Novikova
(2022), pp. 1687-1694
Open Access | Times Cited: 8

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