README.md

---
license: unknown
task_categories:
- text-classification
language:
- en
tags:
- NLP
- Reddit
- Human-Language
size_categories:
- n<1K
---
# 💟 Relationship Advice dataset card

<center>
    <img src="https://huggingface.co/datasets/yonatanko/Relationship_Advice/resolve/main/Relationship%20Advice%20Dataset.png">
</center>

## Table of Contents
- [Dataset Description](#dataset-description)
  - [Dataset Summary](#dataset-summary)
  - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
  - [Languages](#languages)
- [Dataset Structure](#dataset-structure)
  - [Data Instances](#data-instances)
  - [Data Fields](#data-fields)
  - [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
  - [Curation Rationale](#curation-rationale)
  - [Source Data](#source-data)
  - [Annotations](#annotations)
  - [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
  - [Social Impact of Dataset](#social-impact-of-dataset)
  - [Discussion of Biases](#discussion-of-biases)
  - [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
  - [Dataset Curators](#dataset-curators)
  - [Licensing Information](#licensing-information)
  - [Citation Information](#citation-information)
  - [Contributions](#contributions)

## Dataset Description

### Dataset Summary

The Relationship Advice dataset is an English-language compilation of posts and their respective comments concerning dating and human romantic relationships. The primary objective of this dataset is to aid LLMs in categorizing responses and providing appropriate answers based on the emotional needs expressed by the writer.
The data was gathered from two subreddits: [r/dating_advice](https://www.reddit.com/r/dating_advice/) and [r/relationship_advice](https://www.reddit.com/r/relationship_advice/).

### Supported Tasks and Leaderboards

- `text-classification`: This dataset can be used to train a text classification model. The model should categorize the comments into one of 6 labels, considering the post as the broader context.
- `Natural Language Inference`: Given a post and its two comments, the model needs to decide which comment is more helpful to the post writer. Thus, the model must infer the subtle semantics of both comments and their related post.

### Languages

The text in the dataset is in English

## Dataset Structure

### Data Instances

Each data point consists of a post, two comments, two labels, one for each comment (first task), and a third label indicating which comment is more helpful to the post writer (second task).

### Data Fields
- `example_id`: Index of the example, ranged between 1 and 400
- `post`: The post text
- `comment_1`: The first comment of the post
- `comment_2`: The second comment of the post
- `comment_1_label`: The label of the first comment.
- `comment_2_label`: The label of the second comment.
- `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 (150) 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 (40) consists of samples only from the "evaluation" batch, which is the second highest quality batch.

 The training set (210), consists of all the rest.

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



## Dataset Creation

### Significance and Advantages of Utilization

The Relationship Advice dataset was created to serve as a testing ground for machines to learn how to respond with greater sensitivity to users' emotional needs. To do so, the machines must be able to identify the type of response they are providing and, if multiple options are available, determine which one would be most appropriate and beneficial for the writer. Reddit provided the foundation for this dataset, as the language used in conversations on the platform is everyday language, and the topics involve a wide range of emotions, requiring a deep understanding of semantics and meanings conveyed in the text. Training machines with this data would help them improve their emotional intelligence and respond accordingly. 

### Source Data

#### Initial Data Collection and Normalization

The data from both subreddits was gathered using the Reddit API. The posts were filtered to have a maximum of 500 characters and at least two comments, with each comment being less than 500 characters. After the filtering process, 400 randomly sampled posts (along with their comments) were drawn from both subreddits.

#### Who are the source language producers?

The language producers are users of the [r/dating_advice](https://www.reddit.com/r/dating_advice/) and [r/relationship_advice](https://www.reddit.com/r/relationship_advice/) subreddits between 2022 and 2024. No further demographic information was available from the data source.

### Annotations

There were two annotation tasks

Task 1: Comment classification to one of the following labels
- Practical Advice
- Emotional support
- Commentators' opinion
- Hurtful
- Sarcasm
- Not Relevant

Task 2: Given a post and it's two comments, which comment is more helpful to the post writer. The labels are: 
- Comment 1
- Comment 2


#### Annotation process

There were to groups that annotated this data - The owners and external annotators.
The data was split to 3 batches: Exploration (80 items), Evaluation(80 items) and part 3 (240 items).

- Exploration batch: After defining the task, the authors began annotating the first 80 samples to identify data patterns and develop annotation guidelines based on their findings.
- Evaluation batch: Following the drafting of the guidelines, two of the authors proceeded to annotate this batch by the provided annotation guidelines.
- Part 3 batch: This batch was assigned to external annotators. The first 30 records were given to the annotators for annotation in order to enhance the clarity of the guidelines. After the necessary improvements, the final version of the guidelines was provided to the annotators, and they completed the labeling process.

#### Who are the annotators?

The owners of the dataset comprise two males and one female, while the external annotators, who contributed an alternative perspective to the annotation process, include one male and three females. All annotators are aged between 22 and 27 and are final-semester students at the Data Science and Decisions faculty at 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 Relationship Advice dataset has the potential to significantly impact how language models interact with users, especially in emotionally charged situations. By training models on this dataset, we can develop AI systems capable of providing sensitive, empathetic, and contextually appropriate responses in scenarios related to human relationships. This could enhance AI's ability to support individuals seeking advice or emotional support online, potentially alleviating feelings of isolation or distress.
However, it's crucial to consider the implications of deploying such models. While the AI can assist users, there is a risk of it reinforcing harmful stereotypes or providing advice that might not be suitable for all users. Therefore, continuous monitoring, improvement, and ethical considerations must accompany the development and deployment of models trained on this dataset to ensure they contribute positively to users' well-being.

### Discussion of Biases

Bias is an inherent risk in any dataset derived from human-generated content, especially from platforms like Reddit, where the user base may not be representative of the broader population. The Relationship Advice dataset could contain biases related to gender, cultural norms, socio-economic status, or other demographic factors that are not explicitly captured in the data but may influence the nature of the advice and comments.
Moreover, the subreddit communities from which the data is drawn may have their own subcultural biases, which can be reflected in the data. For example, advice given might skew toward certain perspectives that are more prevalent in these online communities, potentially marginalizing other viewpoints. Additionally, the annotation process, despite efforts to standardize it through guidelines, might also introduce biases based on the annotators' interpretations and backgrounds.
To mitigate these biases, users of the dataset should consider employing techniques such as bias detection and mitigation during model training and evaluation. It is also recommended that models trained on this dataset be tested across diverse user groups to ensure fairness and inclusivity in the responses generated.

### Other Known Limitations

While the Relationship Advice dataset provides a valuable resource for training models to understand and generate emotionally appropriate responses, it has several limitations:

- Limited Scope of Advice: The dataset is derived from a specific subset of Reddit users who engage with particular topics. As a result, the advice and perspectives captured may not generalize to broader contexts or different cultures.

- Post Length and Comment Limitation: Posts and comments are truncated at 500 characters, which may omit important context or nuance that could be crucial for understanding the full scope of the conversation. This limitation could affect the model’s ability to fully comprehend and appropriately respond to more complex or detailed scenarios.

- Lack of Demographic Information: The dataset does not include detailed demographic information about the users who generated the posts and comments. This lack of metadata makes it challenging to analyze how responses may vary across different demographic groups, and limits the ability to account for or correct potential biases related to user backgrounds.

- Annotation Subjectivity: Despite the use of guidelines, the annotation process is inherently subjective. Different annotators may interpret the same post or comment differently, leading to inconsistencies in the labels. This subjectivity could influence the performance of models trained on the dataset, especially in tasks requiring nuanced understanding of emotional content.

- Potential for Misuse: As with any dataset involving sensitive topics, there is a risk of misuse. Models trained on this data should not be deployed in critical settings without thorough evaluation and safeguards to prevent harm. For instance, using such models in mental health support contexts without proper oversight could lead to inappropriate or harmful advice being given to users.

## Additional Information

### Dataset Creators

The dataset was created by Yonatan Koifman, Yael Hari, and Yahav Cohen as part of a project for the NLP Research course at the Data Science & Decisions Faculty at the Technion.

### Contributions

Special thanks to Or Cohen, Or Dado, Kere Gruetke, and Tal Shalom for being the external annotators of our dataset.