yael-katsman/Loneliness-Causes-and-Intensity

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.
• Emotion Detection: The dataset is ideal for training models that detect emotional intensity, specifically measuring loneliness on a scale of 1 to 5

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 post's title
• post: The post's 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"
• metadata: The Subreddit which the post was taken from."

Data Splits

The data is split into training, validation, and test sets. The samples are picked at random, with the test set consisting of 153 samples, the validation set consisting of 45 samples, and the training set consisting of 300 samples.

	Train	validation	Test
exploration	61	3	27
evaluation	66	8	35
part 3	173	34	61

Dataset Creation

Source Data

Initial Data Collection and Preprocessing

The data was collected using the Reddit API, initially extracting 550 posts. After filtering out irrelevant content, such as posts with community guidelines, non-English language, and other non-pertinent material, 520 posts remained. The dataset was then split into 70% from "r/lonely" and 30% from "r/offmychest," resulting in a final set of 498 posts. Posts were limited to 300 words, excluding titles, to maintain annotators’ focus and uphold the overall quality of the dataset.

Who are the source language producers?

The source language producers are users of the r/lonely and r/r/offmychest. No further demographic information was available.

Annotations

There were two annotation tasks: Task 1 - Classifying Causes of Loneliness: This task involves classifying posts into multiple categories reflecting the causes of loneliness:

Lack of family contact Lack of friends Lack of romantic relationships Lack of community or social support Lack of physical touch Other Not lonely

Task 2 - Rating Intensity of Loneliness: This task requires rating the intensity of loneliness on a scale from 1 to 5, based on the annotators’ personal assessment, where 1 represents 'not lonely' and 5 represents 'extremely lonely.'

The complete annotation guidelines can be found in guidelines.pdf

Annotation process

• Exploration Batch: Initial annotations were completed by the dataset creators to define categories and develop guidelines (99 posts).
• Evaluation Batch: Further annotations were carried out by the creators following the drafted guidelines (101 posts).
• Part 3 Batch: This batch was assigned to external annotators to refine the guidelines and annotate the remaining posts (298 posts).

The annotation process was carried out by two groups. The first group (owners) consisted of three female annotators who worked on the initial exploration and evaluation. The second group (external annotators) included three males who contributed during the final annotation phase.

The annotators

The annotation process was carried out by two groups. The first group, known as the owners, comprised three females who were responsible for the initial exploration and evaluation stages. The second group, referred to as the external annotators, included three males who contributed during the final stages of the annotation process. All annotators were aged between 21 and 30 and were students at the Data Science and Decisions faculty at the Technion.

Personal and Sensitive Information

The posts and comments do not contain any personal information and are submitted anonymously. No identifiers regarding the authors were obtained.

Considerations for Using the Data

Social Impact of Dataset

The dataset could help improve NLP models aimed at understanding loneliness, a growing mental health concern. By identifying different types of loneliness, this data may support the development of tools that assist mental health professionals or offer resources to those feeling isolated.

Potential Biases

Bias is an inherent challenge in any dataset derived from human-generated content, particularly when sourced from platforms like Reddit, where the user demographic may not fully represent the broader population. The Loneliness : cause and intensity dataset could potentially reflect biases linked to factors such as gender, cultural norms, age, and socio-economic status, which are not explicitly captured in the data but could shape the experiences and expressions of loneliness. These underlying biases may influence the nature of the posts and comments, potentially skewing the content toward certain perspectives more common within specific online communities.

Additionally, the subreddit communities used to collect the data may have their own subcultural biases. Posts from these communities might reflect dominant viewpoints that marginalize other, less-represented perspectives, particularly when discussing topics as subjective as loneliness. For instance, the causes of loneliness discussed in these posts might reflect only certain aspects of human experience, while leaving out or underrepresenting others.

The annotation process, despite following standardized guidelines, may also introduce bias due to the personal interpretations of the annotators, shaped by their backgrounds and experiences. Even with careful adherence to the guidelines, subjective elements, such as interpreting the intensity of loneliness, can vary between annotators.

To address these concerns, users of this dataset should consider implement bias detection and mitigation techniques when training and evaluating models. Moreover, models developed using this dataset should be tested across diverse user groups to ensure inclusivity and fairness in the resulting predictions or applications. This approach will help to reduce the impact of biases and improve the reliability of models trained on this dataset for broader real-world applications.

Other Known Limitations

• Limited Dataset Size: With 498 posts, the dataset is relatively small, which may limit the robustness of models trained on it. The smaller sample size can result in overfitting or reduced generalizability to new data.

• Imbalance in Loneliness Categories: Some types of loneliness, such as "Lack of Family Contact" and "Physical Touch," appear much less frequently than others. This imbalance may lead to models being biased towards more common categories like "Lack of Friends," potentially reducing performance in detecting less frequent types of loneliness.

• Lack of User Demographics: The dataset does not include demographic information about the users who posted. Without this context, it's difficult to assess how factors like age, gender, or cultural background may influence expressions of loneliness or how well models generalize across different populations.

• Short Post Length: The dataset includes posts that are generally short (up to 300 words), which may limit the depth of information available for analysis. While concise, these posts may not capture the full complexity of the emotional experiences or factors contributing to loneliness.

Creators and Contributors

Dataset Creators

The dataset was created by Yael Katsman, Hilly Segal, and Yarden Kamienney as part of a project for the NLP Research course at the Data Science & Decisions Faculty at the Technion.

Acknowledgments

We extend our gratitude to Amit Frechter, Michael Fishman, and Yonatan Sabag for their valuable work as external annotators. Additionally, we are thankful to Roi Reichart and Nitay Calderon for their guidance and mentorship throughout the dataset's development process.