Dataset Summary

Focusing on the PRIMATE dataset, our study reveals concerns regarding annotation validity, particularly for the lack of interest or pleasure symptom. Through re-annotation by a mental health professional, we introduce finer labels and textual spans as evidence, identifying a notable number of false positives. Our refined annotations offer a higher-quality test set for anhedonia detection. This study underscores the necessity of addressing annotation quality issues in mental health datasets, advocating for improved methodologies to enhance NLP model reliability in mental health assessments.

Using the Dataset

Considering that you got access to the PRIMATE dataset (https://github.com/primate-mh/Primate2022) and put the dataset file primate_dataset.json into the same folder as your script, run the following code to map the labels from our dataset to the posts from the PRIMATE dataset:

from datasets import load_dataset
import json

dataset = load_dataset("tartuNLP/reddit-anhedonia", trust_remote_code=True)

primate_dataset_path = 'primate_dataset.json'

with open(primate_dataset_path, encoding='utf-8') as f:
    primate_dataset = json.load(f)

def map_primate(example):
    example["post_title"] = primate_dataset[example["primate_id"]]["post_title"]
    example["post_text"] = primate_dataset[example["primate_id"]]["post_text"]
    return example

dataset = dataset.map(map_primate)

Languages

English

Dataset Structure

Data Instances

An example from the dataset looks as follows:

{
  "primate_id": 1394,
  "answerable": 0,
  "mentioned": 1,
  "writer_symptom": 1,
  "quote": [
    [
      1537,
      1710
    ]
  ]
}

Data Fields

[More Information Needed]

Data Splits

[More Information Needed]

Dataset Creation

Source Data

We take the Reddit posts from the PRIMATE dataset. Anyone interested in using our dataset, must first agree to the terms and conditions of the PRIMATE dataset and acquire it following the procedure described here: https://github.com/primate-mh/Primate2022

Annotations

Annotation process

A mental health professional (MHP) read all the posts in the subset and labelled them for the presence of loss of interest or pleasure (anhedonia). The MHP assigned three labels to each post: a) "mentioned" if the symptom is talked about in the text, but it is not possible to infer its duration or intensity; b) "answerable" if there is clear evidence of anhedonia; c) "writer's symptoms" which shows whether the author of the post discusses themselves or a third person. Additionally, the MHP selected the part of the text that supports the positive label.

Who are the annotators?

The MHP is the second author of the paper, who is also a practicing clinical psychology intern.

Personal and Sensitive Information

We do not distribute any posts with this dataset, only labels that are the results of our work.

Considerations for Using the Data

Discussion of Biases

We acknowledge the limitations inherent in our work and findings. First, the manually annotated explanations serve as a proxy for what clinicians might find informative in assessing Reddit posts flagged as depressive. While evaluating the informativeness of explanations in a true clinical setting would provide more insight, it falls beyond the scope of this work. Furthermore, our reannotation was carried out by only one mental health professional, which does not allow for performing an inter-annotator agreement analysis. However, we believe that our evidence-based labelling scheme partially mitigates this problem. Finally, anhedonia is extremely challenging to conceptualize and binary labels may not be the best choice in situations when the difference between the presence or absence of the symptom is marginal. In this case, labels based on the Likert scale, as in PHQ-9, would be more appropriate and allow us to capture the intensity of the symptom more accurately. Furthermore, different demographics, for example, adolescents and adults, express signs of anhedonia differently

Citation Information

If you find this useful in your research, please consider citing the our paper:

@inproceedings{milintsevich-etal-2024-model,
  title = "Your Model Is Not Predicting Depression Well And That Is Why: A Case Study of {PRIMATE} Dataset",
  author = {Milintsevich, Kirill and Sirts, Kairit and Dias, Ga{\"e}l},
  booktitle = "Proceedings of the 9th Workshop on Computational Linguistics and Clinical Psychology (CLPsych 2024)",
  month = mar,
  year = "2024",
  address = "St. Julians, Malta",
  publisher = "Association for Computational Linguistics",
  url = "https://aclanthology.org/2024.clpsych-1.13",
  pages = "166--171",
}