README.md

---
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
languages:
- ru-Ru
licenses:
- apache-2.0
multilinguality:
- monolingual
pretty_name: The Corpus for the analysis of author profiling in Russian-language texts.
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- multi-class-classification
- multi-label-classification
---

# Dataset Card for [author_profiling]

## Table of Contents
- [Dataset Description](#dataset-description)
  - [Dataset Summary](#dataset-summary)
  - [Supported Tasks](#supported-tasks-and-leaderboards)
  - [Languages](#languages)
- [Dataset Structure](#dataset-structure)
  - [Data Instances](#data-instances)
  - [Data Fields](#data-instances)
  - [Data Splits](#data-instances)
- [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

- **Homepage:** https://github.com/sag111/Author-Profiling
- **Repository:** https://github.com/sag111/Author-Profiling
- **Paper:** [Needs More Information]
- **Leaderboard:** [Needs More Information]
- **Point of Contact:** [Sboev Alexander](mailto:[email protected])

### Dataset Summary

The corpus for the author profiling analysis contains texts in Russian-language which labeled for 5 tasks:
1) gender -- 13530 texts with the labels, who wrote this: text female or male;
2) age -- 13530 texts with the labels, how old the person who wrote the text. This is a number from 12 to 80. In addition, for the classification task we added 5 age groups: 1-19; 20-29; 30-39; 40-49; 50+;
3) age imitation -- 7574 texts, where crowdsource authors is asked to write three texts: a) in their natural manner, b) imitating the style of someone younger, c) imitating the style of someone older;
4) gender imitation -- 5956 texts, where the crowdsource authors is asked to write texts: in their origin gender and pretending to be the opposite gender;
5) style imitation -- 5956 texts, where crowdsource authors is asked to write a text on behalf of another person of your own gender, with a distortion of the authors usual style.


Dataset is collected sing the Yandex.Toloka service [link](https://toloka.yandex.ru/en).

You can read the data using the following python code:
```
def load_jsonl(input_path: str) -> list:
    """
    Read list of objects from a JSON lines file.
    """
    data = []
    with open(input_path, 'r', encoding='utf-8') as f:
        for line in f:
            data.append(json.loads(line.rstrip('\n|\r')))
    print('Loaded {} records from {}/n'.format(len(data), input_path))
    
    return data

path_to_file = "./data/train.jsonl"
data = load_jsonl(path_to_file)
```

#### Here are some statistics:

1. For Train file: 
No. of documents -- 9586
No. of unique texts -- 9586
Text length in characters -- min: 103, max: 12763, mean: 498.1
No. of documents written -- by men: 4767, by women: 4819
No. of unique accounts -- 3054
No. of unique authors -- 3230; men: 1255, women: 1975
Age of the authors -- min: 12, max: 80, mean: 31.1
No. of documents by age group -- 1-19: 734, 20-29: 4477, 30-39: 2604, 40-49: 1063,50+: 708
No. of documents with gender imitation: 1392; without imitation: 2827; not applicable: 5367
No. of documents with age imitation -- younger: 1777; older: 1787; without imitation: 1803; not applicable: 4219
No. of documents with style imitation: 1412; without imitation: 2807; not applicable: 5367.

2. For Valid file: 
No. of documents -- 1368
No. of unique texts -- 1368
Text length in characters -- min: 199, max: 2982, mean: 497.9
No. of documents written -- by men: 705, by women: 663
No. of unique accounts -- 437
No. of unique authors -- 461; men: 184, women: 277
Age of the authors -- min: 14, max: 78, mean: 32.4
No. of documents by age group -- 1-19: 88, 20-29: 510, 30-39: 457, 40-49: 242, 50+: 71
No. of documents with gender imitation: 213; without imitation: 425; not applicable: 730
No. of documents with age imitation -- younger: 243; older: 236; without imitation: 251; not applicable: 638
No. of documents with style imitation: 212; without imitation: 426; not applicable: 730.

3. For Test file: 
No. of documents -- 2576
No. of unique texts -- 2576
Text length in characters -- min: 200, max: 3262, mean: 503.3
No. of documents written -- by men: 1293, by women: 1283
No. of unique accounts -- 873
No. of unique authors -- 915; men: 357, women: 558
Age of the authors -- min: 13, max: 71, mean: 30.4
No. of documents by age group -- 1-19: 253, 20-29: 1163, 30-39: 713, 40-49: 292, 50+: 155
No. of documents with gender imitation: 356; without imitation: 743; not applicable: 1477
No. of documents with age imitation -- younger: 497; older: 483; without imitation: 497; not applicable: 1099
No. of documents with style imitation: 371; without imitation: 728; not applicable: 1477.

### Supported Tasks and Leaderboards

This dataset is intended for multi-class and multi-label text classification.

The baseline models currently achieve the following F1 metrics scores (table):

=== coming soon ===

### Languages

The text in the dataset is in Russian.

## Dataset Structure

### Data Instances

Each instance is a text in Russian with some author profiling annotations.

An example for an instance from the dataset is shown below:
```
{
  'id': 'crowdsource_4916',
  'text': 'Ты очень симпатичный, Я давно не с кем не встречалась. Ты мне сильно понравился, ты умный интересный и удивительный, приходи ко мне в гости , у меня есть вкусное вино , и приготовлю вкусный ужин, посидим пообщаемся, узнаем друг друга поближе.',
  'account_id': '996ff96ebe8c0c51116f32bff0a55bf0',
  'author_id': 'author_#504'
  'age': 22,
  'age_group': '20-29',
  'gender': 'male',
  'no_imitation': 0,
  'age_imitation': nan,
  'gender_imitation': 1.0,
  'style_imitation': 0.0,
  'meta': {
    'Unnamed: 0': 4915,
    'age': 22,
    'doc_ind': 2408,
    'gender': 1,
    'imitation_type': 'gender_im',
    'source': 'gender_imit_crowdsource',
    'user_id': '996ff96ebe8c0c51116f32bff0a55bf0',
    'doc_id': 
    'id_gender_imit_cs_4916'
  },
}
```

### Data Fields

Data Fields includes:
- id -- unique identifier of the sample;

- text -- authors text written by a crowdsourcing user;

- author_id -- unique identifier of the user;

- account_id -- unique identifier of the account (several different people (who know each other) could perform a crowdsourcing task under the same account);

- age -- age annotations;

- age_group -- age group annotations;

- no_imitation -- imitation annotations. 
  Label codes: 
    - 0 -- there is some imitation in the text;
    - 1 -- the text is written without any imitation

- age_imitation -- age imitation annotations. 
  Label codes: 
    - 'younger' -- someone younger than the author is imitated in the text;
    - 'older' -- someone older than the author is imitated in the text;
    - 0 -- the text is written without age imitation;
    - nan -- not supported (the text was not written for this task)

- gender_imitation -- gender imitation annotations. 
  Label codes: 
    - 0 -- the text is written without gender imitation;
    - 1 -- the text is written with a gender imitation;
    - nan -- not supported (the text was not written for this task)

- style_imitation -- style imitation annotations. 
  Label codes: 
  - 0 -- the text is written without style imitation; 
  - 1 -- the text is written with a style imitation; 
  - nan -- not supported (the text was not written for this task).

### Data Splits

The dataset includes a set of train/valid/test splits with 9586, 1368 and 2576 texts respectively. 
The unique authors do not overlap between the splits.

## Dataset Creation

### Curation Rationale

The formed dataset of examples consists of texts in Russian using a crowdsourcing platform. The created dataset can be used to improve the accuracy of supervised classifiers in author profiling tasks.

### Source Data

#### Initial Data Collection and Normalization

Data was collected from crowdsource platform. Each text was written by the author specifically for the task provided.

#### Who are the source language producers?

Russian-speaking Yandex.Toloka users.

### Annotations

#### Annotation process

We used a crowdsourcing platform to collect texts. Each respondent is asked to fill a questionnaire including their gender, age and native language. 

For age imitation task the respondents are to choose a
topic out of a few suggested, and write three texts on it:
1) Text in their natural manner;
2) Text imitating the style of someone younger;
3) Text imitating the style of someone older.

For gender and style imitation task each author wrote three texts in certain different styles: 
1) Text in the authors natural style;
2) Text imitating other gender style;
3) Text in a different style but without gender imitation.

The topics to choose from are the following.
- An attempt to persuade some arbitrary listener to meet the respondent at their place;
- A story about some memorable event/acquisition/rumour or whatever else the imaginary listener is supposed to enjoy;
- A story about oneself or about someone else, aiming to please the listener and win their favour;
- A description of oneself and one’s potential partner for a dating site; 
- An attempt to persuade an unfamiliar person to come;
- A negative tour review.

The task does not pass checking and is considered improper work if it contains:
- Irrelevant answers to the questionnaire;
- Incoherent jumble of words;
- Chunks of text borrowed from somewhere else;
- Texts not conforming to the above list of topics.

Texts checking is performed firstly by automated search for borrowings (by an anti-plagiarism website), and then by manual review of compliance to the task.

#### Who are the annotators?

Russian-speaking Yandex.Toloka users.

### Personal and Sensitive Information

All personal data was anonymized. Each author has been assigned an impersonal, unique identifier.

## Considerations for Using the Data

### Social Impact of Dataset

[Needs More Information]

### Discussion of Biases

[Needs More Information]

### Other Known Limitations

[Needs More Information]

## Additional Information

### Dataset Curators

Researchers at AI technology lab at NRC "Kurchatov Institute". See the [website](https://sagteam.ru/).

### Licensing Information

Apache License 2.0.

### Citation Information

If you have found our results helpful in your work, feel free to cite our publication. 
```
Citation Information coming soon here.
```
### Contributions

Thanks to [@naumov-al](https://github.com/naumov-al) for adding this dataset.