metadata

license: unknown
task_categories:
  - text-classification
language:
  - en
tags:
  - NLP
  - Reddit
  - Loneliness
  - Human-Language
pretty_name: 'Reddit Loneliness: Causes and intensity'
size_categories:
  - n<1K

Reddit Loneliness: Causes and intensity

Dataset Description

Dataset Summary

The Loneliness- cause and intensity dataset is an English-language compilation of posts focused on loneliness among individuals and the different types of loneliness they experience. . The primary objective of this dataset is to aid various NLP models in predicting loneliness and its causes from text, which may be useful in the fields of mental health, NLP contextual understanding, and emotional classification The data was gathered from two subreddits: r/lonely and r/offmychest

Supported tasks

text-classification: This dataset can be used to train a text classification model that categorizes posts into multiple labels of loneliness, including "Not Lonely." The model should consider the context of the post and its title, allowing for overlapping labels to capture different aspects of loneliness..
Natural Language Inference: The model should determine how lonely an individual feels based on their post and title, rating the intensity on a scale from 1 to 5. The model needs to accurately evaluate the emotional tone of the content to provide this rating.

Languages

The text in the dataset is in English

Data instances

Each data point includes a title, a post, a label indicating the causes of loneliness described in the post (first task), and a separate label rating the intensity of loneliness on a scale from 1 to 5.

Data Fields

example_id: Index of the example, ranged between 1 and 509
title: The Title's text
post: The post text
annotator{i}_t1_label: Label assigned by annotator i for the relevant categories of loneliness (first task).
annotator{i}_t2_label: Label assigned by annotator i indicating the intensity of loneliness on a scale from 1 to 5 (second task).
t1_label: The final label for the first task, determined by the most frequently chosen labels among annotators.
t2_label: The final label for the second task, calculated as the average of the annotators' ratings for this task.
batch: The annotation batch this datapoint belong to. One of "exploration", "evaluation" and "part 3"

Data Splits

The data is split into a training, validation and test set. The samples are picked at random according to the following scheme:

The test set (153) consists of samples only from the "part 3" batch since these are the samples that were annotated by the external annotators, thus giving it the highest quality.

The validation set (45) consists of samples only from the "evaluation" batch, which is the second highest quality batch.

The training set (300), consists of all the rest.

	Train	Dev	Test
exploration	80	0	0
evaluation	40	40	0
part 3	90	0	150

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