task_categories:text-classification language:en size_categories:n<1K format:csv modality:tabular modality:text library:datasets library:pandas library:mlcroissant library:polars region:us addictions
---
task_categories:
- text-classification
language:
- en
tags:
- addictions
pretty_name: Personal Addiction Stories
size_categories:
- n<1K
---

# Addiction Stories Dataset

## Table of Content
- [Dataset Description](#dataset-description)
  - [Dataset Summary](#dataset-summary)
  - [Professional Warning](#professional-warning)
  - [Uses](#uses)
  - [Languages](#languages)
- [Dataset Structure](#dataset-structure)
  - [Data Fields](#data-fields)
  - [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
  - [Data Collection and Processing](#data-collection-and-processing)
- [Annotation](#annotation)
  - [Objective Task](#objective-task)
  - [Subjective Task](#subjective-task)
  - [Annotation Process](#annotation-process)
  - [Annotation Aggregation](#annotation-aggregation)
  - [Annotation Distribution](#annotation-distribution)


## Dataset Description

### Dataset Summary
The dataset consists of 491 personal addiction stories, where individuals shared details about their addictions, including what they were addicted to, the circumstances that led them to start, and the effects on their lives.

- **Curated by:** Keren Haruvi, Omri Itzhaki, Ofir Ganor
- **Language(s) (NLP):** English
  
### Professional Warning
The content in this dataset contains personal addiction stories that may evoke strong emotions. Readers should approach these narratives with sensitivity. If you or someone you know is affected by addiction, please seek support from a mental health professional.

### Uses
The dataset was created to understand the factors that lead people to develop addictions and to explore the relationship between the circumstances that trigger addiction and the perceived danger of these addictions. 
It was motivated by the need to fill a gap in understanding personal addiction experiences from a first-hand perspective, providing insights that can enhance addiction prevention and support strategies.

### Languages
The text in the dataset is in English, as spoken by Reddit users on the [r/AskReddit] subreddit, threads titled:
- What is your addiction and how did you get hooked?
- What is your worst addiction
- What is your worst addiction in life right now
- Whats an addiction that people often overlook
- We are all addicted to something. What is yours?

## Dataset Structure

#### Data Fields

- `example_id`: A unique identifier for each instance.
- `text`: The text field contains a personal story of an addiction experience.
- `label`: The majority vote objective label assigned to the addiction story.
- `annotator1_t1_label`: The objective label provided by the first annotator.
- `annotator2_t1_label`: The objective label provided by the second annotator.
- `annotator3_t1_label`: The objective label provided by the third annotator.
- `annotator1_t2_label`: A subjective label provided by the first annotator.
- `annotator2_t2_label`: A subjective label provided by the second annotator.
- `annotator3_t2_label`: A subjective label provided by the third annotator.
- `split`: The dataset split indicating whether the instance belongs to the "train," "eval," or "test" sets.


#### Data Splits

The data is split into a training, validation and test set:
|                        | Train   | Valid  |  Test  |
| -----                  | ------  | -----  |  ----  |
| number of comments     |   291   |   50   |  150   |
| average comment length | 154.07  | 144.52 | 144.31 |

### Data Collection and Processing

- Dataset gathered from Reddit, focusing on posts related to addiction, such as: “What is your addiction and how did you get hooked?”
- Reddit API utilized to extract 23,372 comments of individuals sharing their addiction stories.
- Comments that were either too long (more than 400 words) or too short (fewer than 105 words) were filtered out.


## Annotation

### Objective Task
**Question:** *What motivated the writer to start their addiction?* 

The annotator was instructed to focus on the initial reasons for starting the addiction, rather than the ongoing reasons for continuing it. They were also advised to use common sense to infer motivations from the text while remaining objective and true to the writer's words.

The labels include 7 categories:
- Pleasure: Initiated for enjoyment or fun (e.g., starting video games for enjoyment).
- Achievement and Perfection: Driven by a desire to achieve goals or standards (e.g., weight lifting for a perfect body).
- A Physically Traumatic Event: Began due to managing a physical injury or condition (e.g., painkiller addiction after an accident).
- An Emotionally Traumatic Event: Started due to emotional trauma, whether positive or negative (e.g., alcoholism after a loved one's death).
- Social Influence: Motivated by social factors, such as peer pressure or societal norms (e.g., trying something due to friends' influence).
- Boredom: Started to alleviate boredom or fill free time (e.g., knitting to pass time).
- Not Mentioned: Used when the passage lacks clear motivations or context.
  
### Subjective Task
**Question:** *How dangerous is the addiction?*  

The annotator was instructed to assess how dangerous the addiction is, defined as posing risks to physical health, mental health, or risky behaviors. Danger can have both short-term and long-term effects, impacting the individual or others.
The annotator was also instructed to focus on the addiction itself, regardless of recovery status, and basing the assessment on the user's reported effects, avoiding personal experiences as a basis for labeling.

The labels include 5 possible ordinal values, ranging from 1 (not dangerous at all, e.g., addiction to the smell of eucalyptus) to 5 (life-threatening danger, e.g., addiction to heroin or severe anorexia).

#### Annotation Process
Annotators were presented with passages in English where individuals share their personal addiction stories, detailing how their addiction began and its impact. Each annotator must answer two questions for each passage:
- What circumstances motivated the writer to start their addiction?
- How dangerous is the addiction?

Annotators were instucted to focus on one addiction mentioned in the passage:
- If a single addiction is discussed, base the answers on that addiction.
- If multiple addictions are mentioned, prioritize the one that the writer elaborates on the most. If no single addiction stands out, choose the most dangerous one based on the definition provided in Question 2.

#### Annotation Aggregation
Each comment in the dataset was annotated by three different annotators to ensure reliability and robustness in the labeling process.

For the objective task, the label assigned to each comment is determined by a majority vote among the three annotators. This approach allows us to capture the most representative label for each comment, mitigating the impact of individual biases.

For the subjective task, the annotation results in a vector representing the labels provided by all three annotators. This vector reflects the varying perceptions of the annotators regarding the level of danger associated with each addiction story, allowing for a more nuanced understanding of the subjective ratings.

#### Annotation Distribution
- The dataset exhibits an imbalance in label distribution for the objective task:

|                                | Train  | Valid  |  Test  |
| ----------                     | ------ | -----  |  ----  |
| A physically traumatic event   |   0.07 |  0.02  |  0.07  |
| An emotionally traumatic event |   0.2  | 0.16   | 0.17   |
| Social Influence               |   0.1  |   0.1  |  0.11  |
| Achievement and perfectionism  |   0.07 |   0.14 |  0.09  |
| Pleasure                       |   0.23 |   0.28 |  0.21  |
| Boredom                        |   0.03 |   0.04 |  0.03  |
| Not Mentioned                  |   0.3  |   0.26 |  0.34  |

- The dataset exhibits an imbalance in label distribution for the subjective task: 
±36% of ratings fall into Category 1 (*not dangerous*), while Category 5 (*very dangerous*) is the least represented at ±9%.

|                        | Train        | Valid         |  Test       |
| -----                  | ------       | -----         |  ----       |
| average score ± SD     |   2.5±1.34   |   2.39±1.34   |  2.3±1.24   |