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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:

Predicting Depression From Language-Based Emotion Dynamics: Longitudinal Analysis of Facebook and Twitter Status Updates
Elizabeth Seabrook, Margaret L. Kern, Ben Fulcher, et al.
Journal of Medical Internet Research (2018) Vol. 20, Iss. 5, pp. e168-e168
Open Access | Times Cited: 133

Showing 1-25 of 133 citing articles:

Methods in predictive techniques for mental health status on social media: a critical review
Stevie Chancellor, Munmun De Choudhury
npj Digital Medicine (2020) Vol. 3, Iss. 1
Open Access | Times Cited: 387
Infodemiology and Infoveillance: Scoping Review
Amaryllis Mavragani
Journal of Medical Internet Research (2020) Vol. 22, Iss. 4, pp. e16206-e16206
Open Access | Times Cited: 216
Survey on AI-Based Multimodal Methods for Emotion Detection
Catherine Maréchal, Dariusz Mikołajewski, Krzysztof Tyburek, et al.
Lecture notes in computer science (2019), pp. 307-324
Open Access | Times Cited: 121
Detecting Signs of Depression in Tweets in Spanish: Behavioral and Linguistic Analysis
Ángela Leis, Francesco Ronzano, Miguel Ángel Mayer, et al.
Journal of Medical Internet Research (2019) Vol. 21, Iss. 6, pp. e14199-e14199
Open Access | Times Cited: 99
Digital health data-driven approaches to understand human behavior
Lisa A. Marsch
Neuropsychopharmacology (2020) Vol. 46, Iss. 1, pp. 191-196
Open Access | Times Cited: 81
Digital Technologies for Emotion-Regulation Assessment and Intervention: A Conceptual Review
Alexandra H. Bettis, Taylor A. Burke, Jacqueline Nesi, et al.
Clinical Psychological Science (2021) Vol. 10, Iss. 1, pp. 3-26
Open Access | Times Cited: 72
Emotion fusion for mental illness detection from social media: A survey
Tianlin Zhang, Kailai Yang, Shaoxiong Ji, et al.
Information Fusion (2022) Vol. 92, pp. 231-246
Open Access | Times Cited: 53
MHA: a multimodal hierarchical attention model for depression detection in social media
Zepeng Li, Zhengyi An, Wenchuan Cheng, et al.
Health Information Science and Systems (2023) Vol. 11, Iss. 1
Open Access | Times Cited: 26
Depression detection on online social network with multivariate time series feature of user depressive symptoms
Yicheng Cai, Haizhou Wang, Huali Ye, et al.
Expert Systems with Applications (2023) Vol. 217, pp. 119538-119538
Closed Access | Times Cited: 25
Measuring service quality based on customer emotion: An explainable AI approach
Yiting Guo, Yilin Li, Liu De, et al.
Decision Support Systems (2023) Vol. 176, pp. 114051-114051
Closed Access | Times Cited: 25
Leveraging Domain Knowledge to Improve Depression Detection on Chinese Social Media
Zhihua Guo, Nengneng Ding, Minyu Zhai, et al.
IEEE Transactions on Computational Social Systems (2023) Vol. 10, Iss. 4, pp. 1528-1536
Closed Access | Times Cited: 23
The Role of Humanization and Robustness of Large Language Models in Conversational Artificial Intelligence for Individuals With Depression: A Critical Analysis
Andrea Ferrario, Jana Sedláková, Manuel Trachsel
JMIR Mental Health (2024) Vol. 11, pp. e56569-e56569
Open Access | Times Cited: 9
Exploring the Utility of Community-Generated Social Media Content for Detecting Depression: An Analytical Study on Instagram
Benjamin J. Ricard, Lisa A. Marsch, Benjamin S. Crosier, et al.
Journal of Medical Internet Research (2018) Vol. 20, Iss. 12, pp. e11817-e11817
Open Access | Times Cited: 80
#suicidal - A Multipronged Approach to Identify and Explore Suicidal Ideation in Twitter
Pradyumna Prakhar Sinha, Rohan Mishra, Ramit Sawhney, et al.
(2019), pp. 941-950
Closed Access | Times Cited: 71
A review on recognizing depression in social networks: challenges and opportunities
Felipe T. Giuntini, Mirela T. Cazzolato, Maria de Jesus Dutra dos Reis, et al.
Journal of Ambient Intelligence and Humanized Computing (2020) Vol. 11, Iss. 11, pp. 4713-4729
Closed Access | Times Cited: 63
A Systematic review of the validity of screening depression through Facebook, Twitter, Instagram, and Snapchat
Ji‐In Kim, Zara A. Uddin, Yena Lee, et al.
Journal of Affective Disorders (2021) Vol. 286, pp. 360-369
Closed Access | Times Cited: 52
SetembroBR: a social media corpus for depression and anxiety disorder prediction
Wesley Ramos dos Santos, Rafael Lage de Oliveira, Ivandré Paraboni
Language Resources and Evaluation (2023) Vol. 58, Iss. 1, pp. 273-300
Closed Access | Times Cited: 19
Depression symptoms modelling from social media text: an LLM driven semi-supervised learning approach
Nawshad Farruque, Randy Goebel, Sudhakar Sivapalan, et al.
Language Resources and Evaluation (2024)
Open Access | Times Cited: 7
Analyzing Twitter as a Platform for Alzheimer-Related Dementia Awareness: Thematic Analyses of Tweets
Tiffany Cheng, Lisa Liu, Benjamin K.P. Woo
JMIR Aging (2018) Vol. 1, Iss. 2, pp. e11542-e11542
Open Access | Times Cited: 57
Conducting Sentiment Analysis
Lei Lei, Dilin Liu
arXiv (Cornell University) (2021)
Open Access | Times Cited: 40
Twitter-based measures of neighborhood sentiment as predictors of residential population health
Joseph Gibbons, Robert Malouf, Brian H. Spitzberg, et al.
PLoS ONE (2019) Vol. 14, Iss. 7, pp. e0219550-e0219550
Open Access | Times Cited: 39
Suitability of Text-Based Communications for the Delivery of Psychological Therapeutic Services to Rural and Remote Communities: Scoping Review
Anne Dwyer, Abílio de Almeida Neto, Dominique Estival, et al.
JMIR Mental Health (2021) Vol. 8, Iss. 2, pp. e19478-e19478
Open Access | Times Cited: 30
The relationship between linguistic expression in blog content and symptoms of depression, anxiety, and suicidal thoughts: A longitudinal study
Bridianne O’Dea, Tjeerd W. Boonstra, Mark Larsen, et al.
PLoS ONE (2021) Vol. 16, Iss. 5, pp. e0251787-e0251787
Open Access | Times Cited: 29
What users’ musical preference on Twitter reveals about psychological disorders
Soroush Zamani Alavijeh, Fattane Zarrinkalam, Zeinab Noorian, et al.
Information Processing & Management (2023) Vol. 60, Iss. 3, pp. 103269-103269
Closed Access | Times Cited: 12
Supervised machine learning models for depression sentiment analysis
Ibidun Christiana Obagbuwa, Samantha Danster, Onil Colin Chibaya
Frontiers in Artificial Intelligence (2023) Vol. 6
Open Access | Times Cited: 12
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