On the State of Social Media Data for Mental Health Research

Harrigian, Keith; Aguirre, Carlos; Dredze, Mark

doi:10.18653/v1/2021.clpsych-1.2

Cited by 17 publications

(15 citation statements)

References 72 publications

(33 reference statements)

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“…Researchers in this realm build algorithms for mental health identification and prediction, often relying on handcrafted or automated features for building AI models [14], [15]. Contributions toward this endeavor have emerged through exclusive studies on computational intelligence with ethical research protocols, as it is mandatory to address ethical concerns due to the sensitive nature of datasets from which such algorithms develop [16]- [18].…”

Section: A Current Position Of Communitymentioning

confidence: 99%

NLP as a Lens for Causal Analysis and Perception Mining to Infer Mental Health on Social Media

Garg¹,

Saxena²,

Naseem³

et al. 2023

Preprint

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<p>Interactions among humans on social media often convey intentions behind their actions, yielding a psychological language resource for Mental Health Analysis (MHA) of online users. The success of Computational Intelligence Techniques (CIT) for inferring mental illness from such social media resources points to NLP as a lens for causal analysis and perception mining. However, we argue that more consequential and explainable research is required for optimal impact on clinical psychology practice and personalized mental healthcare. To bridge this gap, we posit two significant dimensions: (1) Causal analysis to illustrate a cause and effect relationship in the user generated text; (2) Perception mining to infer psychological perspectives of social effects on online users intentions. Within the scope of Natural Language Processing (NLP), we further explore critical areas of inquiry associated with these two dimensions, specifically through recent advancements in discourse analysis. This position paper guides the community to explore solutions in this space and advance the state of practice in developing conversational agents for inferring mental health from social media. We advocate for a more explainable approach toward modeling computational psychology problems through the lens of language as we observe an increased number of research contributions in dataset and problem formulation for causal relation extraction and perception enhancements while inferring mental states.</p>

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Section: A Current Position Of Communitymentioning

confidence: 99%

NLP as a Lens for Causal Analysis and Perception Mining to Infer Mental Health on Social Media

Garg¹,

Saxena²,

Naseem³

et al. 2023

Preprint

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“…Gaining access to datasets in this area proved challenging, as also discussed by Harrigian et al (2021), for numerous reasons including IRB restrictions, personal reluctance, or unresponsiveness to data access requests. We ultimately acquired datasets pertaining to suicide (Shing et al, 2018;Zirikly et al, 2019), stress (Turcan and McKeown, 2019), and depression (Losada and Crestani, 2016;Parapar et al, 2021).…”

Section: Data Sourcing and Ethical Guidelinesmentioning

confidence: 99%

“…As per our agreements with the creators of these datasets we are unable to share data directly, but we provide a table in the Appendix to summarize dataset statistics. We encourage researchers to examine the data and related private datasets, and thank the respective authors as well as Harrigian et al (2021) for creating a curated repository of mental health data and pointers facilitating data discovery.…”

Section: Data Descriptionmentioning

confidence: 99%

Are You Really Okay? A Transfer Learning-based Approach for Identification of Underlying Mental Illnesses

Aich¹,

Parde²

2022

Proceedings of the Eighth Workshop on Computational Linguistics and Clinical Psychology

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Evidence has demonstrated the presence of similarities in language use across people with various mental health conditions. In this work we investigate these relationships both as described in literature and as a data analysis problem. We also introduce a novel transfer learning based approach that learns from linguistic feature spaces of previous conditions and predicts unknown ones. Our model achieves strong performance, with F 1 scores of 0.75, 0.80, and 0.76 at detecting depression, stress, and suicidal ideation in a first-of-its-kind transfer task and offering promising evidence that language models can harness learned patterns from known mental health conditions to aid in their prediction of others that may lie latent.

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“…Nowadays, social media messages are analyzed via techniques such as text classification to find the relationships and patterns, the aim frequently being to use techniques to study human behavior [9]. Several researchers in psychology have extracted behavioral data from the social media platforms such as Twitter, Sina Weibo, Tumblr, Reddit in order to analyze the psychological states of social media users [10]. Twitter data were obtain to analyze the four types of mental disorders: 1) emotional disorder, 2) trauma, 3) seasonal depression, and 4) major depressive disorder [11], to identify and study the types of depression symptoms, to detect the depression from features associated with depression [12], and to classify the depression [13].…”

Section: Introductionmentioning

confidence: 99%

Weighted Voting Ensemble for Depressive Disorder Analysis with Multi-objective Optimization

Nuankaew

Doenribram

et al. 2022

Curr. Appl. Sci. Technol.

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The Twitter platform is a popular tool that is widely used by researchers to collect data on users’ personal lives, feelings and emotions. These data sets can be further analyzed using text mining techniques to predict the disorder of depression. There are nine symptoms of depression that are classified by American Psychiatric Association using DSM-5 criteria. The symptoms can be difficult to identify effectively. The unweighted vote ensemble is not practical for multi-class data. Therefore, this research proposes the multi-objective optimization algorithms for depressive symptom prediction modeling (MOADSP) for the weighted voting ensemble, which can improve its effectiveness compared to the singer model. The objectives of this research were 1) to find the appropriate number of features; 2) to improve the weights of the prediction models based on the recall of the class for the ensemble; and 3) to compare the performance of the single, unweighted, and weighted voting ensemble models for depressive disorder. An information gain was used to select the features. The single classification techniques used in the experiment that had their frameworks tested were the Naïve Bayes, Random Forest, and K-Nearest techniques, while the vote ensemble models used were the unweighted and weighted models. MOADSP was applied to the weighted vote ensemble models. The results showed that the best recall classifier was KNN (98.60%), and the highest recall classifier was AVG TP weighted (98.43%) for the training model. The highest recall in the class depressive classifier was AVG TP weighted (80.00%) for the testing. This proposed method was beneficial for the prediction of depressive disorder.

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On the State of Social Media Data for Mental Health Research

Cited by 17 publications

References 72 publications

NLP as a Lens for Causal Analysis and Perception Mining to Infer Mental Health on Social Media

NLP as a Lens for Causal Analysis and Perception Mining to Infer Mental Health on Social Media

Are You Really Okay? A Transfer Learning-based Approach for Identification of Underlying Mental Illnesses

Weighted Voting Ensemble for Depressive Disorder Analysis with Multi-objective Optimization

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