A Dataset for Research on Depression in Social Media

Ríssola, Esteban A.; Bahrainian, Seyed Ali; Crestani, Fábio

doi:10.1145/3340631.3394879

Cited by 17 publications

(5 citation statements)

References 19 publications

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“…Social media's Text datasets have emerged as one of the acceptable option for assessing depression and suicide emotion among Natural Language Processing (NLP) research experts. Text based samples are mostly collected from Twitter, Reddit [44], Facebook, weibo etc websites donated by various institutions or researchers [38]. In 2021 the Computational Linguistics and Clinical Psychology CLPsych 2021 workshop organized a Task challenge for detecting suicidal risk [29].…”

Section: Datasetmentioning

confidence: 99%

Exploratory analysis of suicidal tendency in depression investigation social media post

MOBIN,

MRIDHA,

MAHMUD

2024

Preprint

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Depression and suicide are interconnected. A depressive condition influences the chance of suicide. Though, to what extent of depression level triggers suicidal attempt is a scrutiny. The traditional interview-based clinical diagnostic approach is not effective for predicting a depressed person’s psychiatric status. Since patients often prefer not to disclose their emotions, they are reluctant to seek help from psychotherapists or doctors. Social media’s footprint often sees people discussing things that are not disclosed publicly. Emotionally distressed people often disclose themselves, seek empathy, and reveal physiological states. Hence, it has become a valuable source of research for depression and suicidal behaviors. This study uses social media datasets for exploratory data analysis to estimate the degree of suicidal thoughts within a depressed person’s post. The objective is to determine if a depressed individual has a suicidal tendency, determine the degree of intensity, or do the opposite. This study presents an unsupervised feature analysis using the topic model approach followed by supervised classification to quantify significance. Latent suicidal intents,cross-topic co-occurrence patterns, and dominant high-impact keywords of suicide are revealed from unsupervised Latent Dirichlet Allocation (LDA) modeling. Furthermore, supervised machine learning classifier models are applied to determine the severity of suicide tendencies. To get the best results, cutting-edge text embedding vectorization techniques and machine learning estimators are applied. Statistical measurements depict the degree of suicide intensity within the depressed label post. From the analysis, it is revealed that suicidal tendency among people with depression is extremely high. The depressed person’s post showed 60% similarities in various categories of suicidal intensity.

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Section: Datasetmentioning

confidence: 99%

Exploratory analysis of suicidal tendency in depression investigation social media post

MOBIN,

MRIDHA,

MAHMUD

2024

Preprint

View full text Add to dashboard Cite

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“…Through this, automatic filtering and selection of the most indicative posts of a user can be made for use in dataset creation. This idea is similar to Ríssola et al [3], which employed a series of heuristics to recognize posts portraying depression symptoms for use in constructing a post-level training set from existing depression datasets annotated at the user level. As such, we use Integrated Gradients [5] to compute attribution scores for a text-set.…”

Section: Interpretabilitymentioning

confidence: 99%

“…Our three submitted runs achieve high Recall at the expense of lower Precision scores. The precision of our models can be improved by incorporating a mechanism for weighting user posts according to the prevalence of signs of depression [38]. As such, a text-set containing few posts with signs of depression will not induce a positive prediction.…”

Section: Tablementioning

confidence: 99%

“…In the task of mental health disorders detection from social media text, many approaches operate on the post-level [2,3,4], considering that, for instance, if a user is depressed, then all their posts might contain some information regarding this issue. However, we posit that this method of post-level classification is unsuitable -many posts are unrelated and uninformative to the particular task.…”

Section: Introductionmentioning

confidence: 99%

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An End-to-End Set Transformer for User-Level Classification of Depression and Gambling Disorder

Bucur¹,

Cosma²,

Dinu³

et al. 2022

Preprint

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This work proposes a transformer architecture for user-level classification of gambling addiction and depression that is trainable end-to-end. As opposed to other methods that operate at the post level, we process a set of social media posts from a particular individual, to make use of the interactions between posts and eliminate label noise at the post level. We exploit the fact that, by not injecting positional encodings, multi-head attention is permutation invariant and we process randomly sampled sets of texts from a user after being encoded with a modern pretrained sentence encoder (RoBERTa / MiniLM). Moreover, our architecture is interpretable with modern feature attribution methods and allows for automatic dataset creation by identifying discriminating posts in a user's text-set. We perform ablation studies on hyper-parameters and evaluate our method for the eRisk 2022 Lab on early detection of signs of pathological gambling and early risk detection of depression. The method proposed by our team BLUE obtained the best ERDE5 score of 0.015, and the second-best ERDE50 score of 0.009 for pathological gambling detection. For the early detection of depression, we obtained the second-best ERDE50 of 0.027.

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“…The prevalence of depression can vary by population and region, with data subject to change over time due to numerous factors, including shifts in diagnostic criteria, advances in knowledge, and societal changes [7]. Despite these variations, depression remains a significant global mental health challenge.…”

Section: Introductionmentioning

confidence: 99%

Novel Transformer Based Contextualized Embedding and Probabilistic Features for Depression Detection From Social Media

Abbas,

Munir,

Raza

et al. 2024

IEEE Access

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Depression constitutes a significant mental health condition, impacting an individual's emotional state, thought processes, and ability to carry out everyday tasks. Depression is defined by ongoing feelings of sadness, diminished interest in previously enjoyed activities, alterations in hunger, sleep disturbances, decreased vitality, and challenges with focus. The impact of depression extends beyond the individual, affecting society at large through decreased productivity and higher healthcare costs. In the realm of social media, users often express their thoughts and emotions through posts, which can provide insightful data for identifying patterns of depression. This research aims to detect depression early by analyzing social media user content with machine learning techniques. We have built advanced machine learning models using a benchmark depression database containing 20,000 tagged tweets from user profiles identified as depressed or non-depressed. We are introducing an innovative BERT-RF feature engineering method that extracts Contextualized Embeddings and Probabilistic Features from textual input. The Bidirectional Encoder Representations from Transformers (BERT) model, based on the Transformer architecture, is used to extract Contextualized Embedding features. These features are then fed into a random forest model to generate class probabilistic features. These prominent features aid in enhancing the identification of depression from social media. In order to classify tweets using the features derived from the BERT-RF features selection step, we have used five popular classifiers: Random Forest (RF), Multilayer Perceptron (MLP), K-Neighbors Classifier (KNC), Logistic Regression (LR), and Long Short-Term Memory (LSTM). Evaluation experiments show that our approach, using BERT-RF for feature engineering, enables the Logistic Regression model to outperform state-of-the-art methods with a high accuracy score of 99%. We have validated the results through k-fold cross-validation and statistical T-tests. We achieved 99% k-fold accuracy during the validation of the proposed approach. This research contributes significantly to computational linguistics and mental health analytics by providing a robust approach to the early detection of user depression from social media content.

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A Dataset for Research on Depression in Social Media

Cited by 17 publications

References 19 publications

Exploratory analysis of suicidal tendency in depression investigation social media post

Exploratory analysis of suicidal tendency in depression investigation social media post

An End-to-End Set Transformer for User-Level Classification of Depression and Gambling Disorder

Novel Transformer Based Contextualized Embedding and Probabilistic Features for Depression Detection From Social Media

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