
# Dataset Card for "RO-Offense-Sequences"

## 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
<!--
- **Paper:** News-RO-Offense - A Romanian Offensive Language Dataset and Baseline Models Centered on News Article Comments
-->
- **Homepage:** [https://github.com/readerbench/ro-offense-sequences](https://github.com/readerbench/ro-offense-sequences)
- **Repository:** [https://github.com/readerbench/ro-offense-sequences](https://github.com/readerbench/ro-offense-sequences)
- **Point of Contact:** [Andrei Paraschiv](https://github.com/AndyTheFactory)
- 
### Dataset Summary
a novel Romanian language dataset for offensive language detection with manually 
annotated offensive labels from a local Romanian sports news website (gsp.ro):


Resulting in 12,445 annotated messages

### Languages

Romanian

## Dataset Structure


### Data Instances


An example of 'train' looks as follows.

```
{
  'id': 5,
  'text':'PLACEHOLDER TEXT',
  'label': 'OTHER'
}
```


### Data Fields

- `id`: The unique comment ID, corresponding to the ID in [RO Offense](https://huggingface.co/datasets/readerbench/ro-offense)
- `text`: full comment text
- `label`: the type of offensive message (OTHER, PROFANITY, INSULT, ABUSE)

### Data Splits


Train | Other | Profanity | Insult | Abuse
:---| :---| :---| :---| :---:
9953 | 3656 | 1293 | 2236 | 2768

Test | Other | Profanity | Insult | Abuse
:---| :---| :---| :---| :---:
2492 | 916 | 324 | 559 | 693


## Dataset Creation


### Curation Rationale

Collecting data for abusive language classification for Romanian Language.

For the labeling of texts we loosely base our definitions on the Germeval 2019 task for detecting offensive language in german tweets  (Struß et al., 2019)

Data source: Comments on articles in Gazeta Sporturilor (gsp.ro) between 2011 and 2020 

Selection for annotation: we select comments from a pool of secific articles based on the number of comments in the article. 
The number of comments per article has the following distribution:
```
mean        183.820923
std         334.707177
min           1.000000
25%          20.000000
50%          58.000000
75%         179.000000
max        2151.000000
```

Based on this we select only comments from articles having between 20 and 50 comments. Also, we remove comments containing urls or three consecutive *, since these were mostly censored by editors or automatic profanity detection algorythms.

Additional, in order to have some meaningful messages for annotation, we select only messages with length between 50 and 500 characters. 


### Source Data

Sports News Articles comments


#### Initial Data Collection and Normalization



#### Who are the source language producers?

Sports News Article readers


### Annotations

- Andrei Paraschiv
- Irina Maria Sandu

#### Annotation process


##### OTHER
 
Label used for non offensive texts. 

##### PROFANITY

This is the "lighter" form of abusive language. When profane words are used without a direct intend on offending a target, or without ascribing some negative qualities to a target we use this label. Some messages in this class may even have a positive sentiment and uses swearwords as emphasis. Messages containing profane words that are not directed towards a specific group or person, we label as **PROFANITY**

Also, self censored messages with swear words having some letters hidden, or some deceitful misspellings of swearwords that have clear intend on circumventing profanity detectors will be treated as **PROFANITY**. 


##### INSULT

The message clearly wants to offend someone, ascribing negatively evaluated qualities or deficiences, labeling a person or a group of persons as unworthy or unvalued. Insults do imply disrespect and contempt directed towards a target.


##### ABUSE

This label marks messages containing the stronger form of offensive and abusive language. This type of language ascribes the target a social identity that is judged negatively by the majority of society, or at least is percieved as a mostly negative judged identity. Shameful, unworthy or morally unaceptable identytities fall in this category. In contrast to insults, instances of abusive language require that the target of judgment is seen as a representative of a group and it is ascribed negative qualities that are taken to be universal, omnipresent and unchangeable characteristics of the group. 

In contrast to insults, instances of abusive language require that the target of judgment tis seen as a representative of a group and it is ascribed negative qualities that are taken to be universal, omnipresent and unchangeable characteristics of the group.

Additional, dehumanizing language targeting a person or group is also classified as ABUSE. 

#### Who are the annotators?

Native speakers

### Personal and Sensitive Information

The data was public at the time of collection. PII removal has been performed.

## Considerations for Using the Data



### Social Impact of Dataset

The data definitely contains abusive language. The data could be used to develop and propagate offensive language against every target group involved, i.e. ableism, racism, sexism, ageism, and so on.

### Discussion of Biases


### Other Known Limitations


## Additional Information


### Dataset Curators


### Licensing Information

This data is available and distributed under Apache-2.0 license

### Citation Information

```
tbd
```


### Contributions
task_categories:text-classification task_ids:hate-speech-detection annotations_creators:expert-generated language_creators:found multilinguality:monolingual size_categories:1K<n<10K source_datasets:readerbench/ro-offense language:ro license:apache-2.0 hate-speech-detection offensive speech romanian nlp region:us
{"annotations_creators": ["expert-generated"], "language_creators": ["found"], "language": ["ro"], "license": "apache-2.0", "multilinguality": ["monolingual"], "size_categories": ["1K<n<10K"], "source_datasets": ["readerbench/ro-offense"], "task_categories": ["text-classification"], "task_ids": ["hate-speech-detection"], "pretty_name": "RO-Offense-Sequences", "tags": ["hate-speech-detection", "offensive speech", "romanian", "nlp"], "extra_gated_prompt": "Warning: this repository contains harmful content (abusive language, hate speech).", "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "train.csv"}, {"split": "test", "path": "test.csv"}]}, {"config_name": "ner", "data_files": [{"split": "train", "path": "train_ner.csv"}, {"split": "test", "path": "test_ner.csv"}]}]}
TAGS
#task_categories-text-classification #task_ids-hate-speech-detection #annotations_creators-expert-generated #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-readerbench/ro-offense #language-Romanian #license-apache-2.0 #hate-speech-detection #offensive speech #romanian #nlp #region-us 

Dataset Card for "RO-Offense-Sequences"
=======================================


Table of Contents
-----------------


* Dataset Description
	+ Dataset Summary
	+ Supported Tasks and Leaderboards
	+ Languages
* Dataset Structure
	+ Data Instances
	+ Data Fields
	+ Data Splits
* Dataset Creation
	+ Curation Rationale
	+ Source Data
	+ Annotations
	+ Personal and Sensitive Information
* Considerations for Using the Data
	+ Social Impact of Dataset
	+ Discussion of Biases
	+ Other Known Limitations
* Additional Information
	+ Dataset Curators
	+ Licensing Information
	+ Citation Information
	+ Contributions


Dataset Description
-------------------


* Homepage: URL
* Repository: URL
* Point of Contact: Andrei Paraschiv
* 


### Dataset Summary


a novel Romanian language dataset for offensive language detection with manually
annotated offensive labels from a local Romanian sports news website (URL):


Resulting in 12,445 annotated messages


### Languages


Romanian


Dataset Structure
-----------------


### Data Instances


An example of 'train' looks as follows.


### Data Fields


* 'id': The unique comment ID, corresponding to the ID in RO Offense
* 'text': full comment text
* 'label': the type of offensive message (OTHER, PROFANITY, INSULT, ABUSE)


### Data Splits




Dataset Creation
----------------


### Curation Rationale


Collecting data for abusive language classification for Romanian Language.


For the labeling of texts we loosely base our definitions on the Germeval 2019 task for detecting offensive language in german tweets (Struß et al., 2019)


Data source: Comments on articles in Gazeta Sporturilor (URL) between 2011 and 2020


Selection for annotation: we select comments from a pool of secific articles based on the number of comments in the article.
The number of comments per article has the following distribution:


Based on this we select only comments from articles having between 20 and 50 comments. Also, we remove comments containing urls or three consecutive \*, since these were mostly censored by editors or automatic profanity detection algorythms.


Additional, in order to have some meaningful messages for annotation, we select only messages with length between 50 and 500 characters.


### Source Data


Sports News Articles comments


#### Initial Data Collection and Normalization


#### Who are the source language producers?


Sports News Article readers


### Annotations


* Andrei Paraschiv
* Irina Maria Sandu


#### Annotation process


##### OTHER


Label used for non offensive texts.


##### PROFANITY


This is the "lighter" form of abusive language. When profane words are used without a direct intend on offending a target, or without ascribing some negative qualities to a target we use this label. Some messages in this class may even have a positive sentiment and uses swearwords as emphasis. Messages containing profane words that are not directed towards a specific group or person, we label as PROFANITY


Also, self censored messages with swear words having some letters hidden, or some deceitful misspellings of swearwords that have clear intend on circumventing profanity detectors will be treated as PROFANITY.


##### INSULT


The message clearly wants to offend someone, ascribing negatively evaluated qualities or deficiences, labeling a person or a group of persons as unworthy or unvalued. Insults do imply disrespect and contempt directed towards a target.


##### ABUSE


This label marks messages containing the stronger form of offensive and abusive language. This type of language ascribes the target a social identity that is judged negatively by the majority of society, or at least is percieved as a mostly negative judged identity. Shameful, unworthy or morally unaceptable identytities fall in this category. In contrast to insults, instances of abusive language require that the target of judgment is seen as a representative of a group and it is ascribed negative qualities that are taken to be universal, omnipresent and unchangeable characteristics of the group.


In contrast to insults, instances of abusive language require that the target of judgment tis seen as a representative of a group and it is ascribed negative qualities that are taken to be universal, omnipresent and unchangeable characteristics of the group.


Additional, dehumanizing language targeting a person or group is also classified as ABUSE.


#### Who are the annotators?


Native speakers


### Personal and Sensitive Information


The data was public at the time of collection. PII removal has been performed.


Considerations for Using the Data
---------------------------------


### Social Impact of Dataset


The data definitely contains abusive language. The data could be used to develop and propagate offensive language against every target group involved, i.e. ableism, racism, sexism, ageism, and so on.


### Discussion of Biases


### Other Known Limitations


Additional Information
----------------------


### Dataset Curators


### Licensing Information


This data is available and distributed under Apache-2.0 license


### Contributions



### Dataset Summary


a novel Romanian language dataset for offensive language detection with manually
annotated offensive labels from a local Romanian sports news website (URL):


Resulting in 12,445 annotated messages ### Languages


Romanian


Dataset Structure
----------------- ### Data Instances


An example of 'train' looks as follows. ### Data Fields


* 'id': The unique comment ID, corresponding to the ID in RO Offense
* 'text': full comment text
* 'label': the type of offensive message (OTHER, PROFANITY, INSULT, ABUSE) ### Data Splits




Dataset Creation
---------------- ### Curation Rationale


Collecting data for abusive language classification for Romanian Language.


For the labeling of texts we loosely base our definitions on the Germeval 2019 task for detecting offensive language in german tweets (Struß et al., 2019)


Data source: Comments on articles in Gazeta Sporturilor (URL) between 2011 and 2020


Selection for annotation: we select comments from a pool of secific articles based on the number of comments in the article.
The number of comments per article has the following distribution:


Based on this we select only comments from articles having between 20 and 50 comments. Also, we remove comments containing urls or three consecutive \*, since these were mostly censored by editors or automatic profanity detection algorythms.


Additional, in order to have some meaningful messages for annotation, we select only messages with length between 50 and 500 characters. ### Source Data


Sports News Articles comments #### Initial Data Collection and Normalization #### Who are the source language producers?


Sports News Article readers ### Annotations


* Andrei Paraschiv
* Irina Maria Sandu #### Annotation process ##### OTHER


Label used for non offensive texts. ##### PROFANITY


This is the "lighter" form of abusive language. When profane words are used without a direct intend on offending a target, or without ascribing some negative qualities to a target we use this label. Some messages in this class may even have a positive sentiment and uses swearwords as emphasis. Messages containing profane words that are not directed towards a specific group or person, we label as PROFANITY


Also, self censored messages with swear words having some letters hidden, or some deceitful misspellings of swearwords that have clear intend on circumventing profanity detectors will be treated as PROFANITY. ##### INSULT


The message clearly wants to offend someone, ascribing negatively evaluated qualities or deficiences, labeling a person or a group of persons as unworthy or unvalued. Insults do imply disrespect and contempt directed towards a target. ##### ABUSE


This label marks messages containing the stronger form of offensive and abusive language. This type of language ascribes the target a social identity that is judged negatively by the majority of society, or at least is percieved as a mostly negative judged identity. Shameful, unworthy or morally unaceptable identytities fall in this category. In contrast to insults, instances of abusive language require that the target of judgment is seen as a representative of a group and it is ascribed negative qualities that are taken to be universal, omnipresent and unchangeable characteristics of the group.


In contrast to insults, instances of abusive language require that the target of judgment tis seen as a representative of a group and it is ascribed negative qualities that are taken to be universal, omnipresent and unchangeable characteristics of the group.


Additional, dehumanizing language targeting a person or group is also classified as ABUSE. #### Who are the annotators?


Native speakers ### Personal and Sensitive Information


The data was public at the time of collection. PII removal has been performed.


Considerations for Using the Data
--------------------------------- ### Social Impact of Dataset


The data definitely contains abusive language. The data could be used to develop and propagate offensive language against every target group involved, i.e. ableism, racism, sexism, ageism, and so on. ### Discussion of Biases ### Other Known Limitations


Additional Information
---------------------- ### Dataset Curators ### Licensing Information


This data is available and distributed under Apache-2.0 license ### Contributions
TAGS
#task_categories-text-classification #task_ids-hate-speech-detection #annotations_creators-expert-generated #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-readerbench/ro-offense #language-Romanian #license-apache-2.0 #hate-speech-detection #offensive speech #romanian #nlp #region-us 
 ### Dataset Summary


a novel Romanian language dataset for offensive language detection with manually
annotated offensive labels from a local Romanian sports news website (URL):


Resulting in 12,445 annotated messages ### Languages


Romanian


Dataset Structure
----------------- ### Data Instances


An example of 'train' looks as follows. ### Data Fields


* 'id': The unique comment ID, corresponding to the ID in RO Offense
* 'text': full comment text
* 'label': the type of offensive message (OTHER, PROFANITY, INSULT, ABUSE) ### Data Splits




Dataset Creation
---------------- ### Curation Rationale


Collecting data for abusive language classification for Romanian Language.


For the labeling of texts we loosely base our definitions on the Germeval 2019 task for detecting offensive language in german tweets (Struß et al., 2019)


Data source: Comments on articles in Gazeta Sporturilor (URL) between 2011 and 2020


Selection for annotation: we select comments from a pool of secific articles based on the number of comments in the article.
The number of comments per article has the following distribution:


Based on this we select only comments from articles having between 20 and 50 comments. Also, we remove comments containing urls or three consecutive \*, since these were mostly censored by editors or automatic profanity detection algorythms.


Additional, in order to have some meaningful messages for annotation, we select only messages with length between 50 and 500 characters. ### Source Data


Sports News Articles comments #### Initial Data Collection and Normalization #### Who are the source language producers?


Sports News Article readers ### Annotations


* Andrei Paraschiv
* Irina Maria Sandu #### Annotation process ##### OTHER


Label used for non offensive texts. ##### PROFANITY


This is the "lighter" form of abusive language. When profane words are used without a direct intend on offending a target, or without ascribing some negative qualities to a target we use this label. Some messages in this class may even have a positive sentiment and uses swearwords as emphasis. Messages containing profane words that are not directed towards a specific group or person, we label as PROFANITY


Also, self censored messages with swear words having some letters hidden, or some deceitful misspellings of swearwords that have clear intend on circumventing profanity detectors will be treated as PROFANITY. ##### INSULT


The message clearly wants to offend someone, ascribing negatively evaluated qualities or deficiences, labeling a person or a group of persons as unworthy or unvalued. Insults do imply disrespect and contempt directed towards a target. ##### ABUSE


This label marks messages containing the stronger form of offensive and abusive language. This type of language ascribes the target a social identity that is judged negatively by the majority of society, or at least is percieved as a mostly negative judged identity. Shameful, unworthy or morally unaceptable identytities fall in this category. In contrast to insults, instances of abusive language require that the target of judgment is seen as a representative of a group and it is ascribed negative qualities that are taken to be universal, omnipresent and unchangeable characteristics of the group.


In contrast to insults, instances of abusive language require that the target of judgment tis seen as a representative of a group and it is ascribed negative qualities that are taken to be universal, omnipresent and unchangeable characteristics of the group.


Additional, dehumanizing language targeting a person or group is also classified as ABUSE. #### Who are the annotators?


Native speakers ### Personal and Sensitive Information


The data was public at the time of collection. PII removal has been performed.


Considerations for Using the Data
--------------------------------- ### Social Impact of Dataset


The data definitely contains abusive language. The data could be used to develop and propagate offensive language against every target group involved, i.e. ableism, racism, sexism, ageism, and so on. ### Discussion of Biases ### Other Known Limitations


Additional Information
---------------------- ### Dataset Curators ### Licensing Information


This data is available and distributed under Apache-2.0 license ### Contributions
passage: TAGS
#task_categories-text-classification #task_ids-hate-speech-detection #annotations_creators-expert-generated #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-readerbench/ro-offense #language-Romanian #license-apache-2.0 #hate-speech-detection #offensive speech #romanian #nlp #region-us 
### Dataset Summary


a novel Romanian language dataset for offensive language detection with manually
annotated offensive labels from a local Romanian sports news website (URL):


Resulting in 12,445 annotated messages### Languages


Romanian


Dataset Structure
-----------------### Data Instances


An example of 'train' looks as follows.### Data Fields


* 'id': The unique comment ID, corresponding to the ID in RO Offense
* 'text': full comment text
* 'label': the type of offensive message (OTHER, PROFANITY, INSULT, ABUSE)### Data Splits




Dataset Creation
----------------### Curation Rationale


Collecting data for abusive language classification for Romanian Language.


For the labeling of texts we loosely base our definitions on the Germeval 2019 task for detecting offensive language in german tweets (Struß et al., 2019)


Data source: Comments on articles in Gazeta Sporturilor (URL) between 2011 and 2020


Selection for annotation: we select comments from a pool of secific articles based on the number of comments in the article.
The number of comments per article has the following distribution:


Based on this we select only comments from articles having between 20 and 50 comments. Also, we remove comments containing urls or three consecutive \*, since these were mostly censored by editors or automatic profanity detection algorythms.


Additional, in order to have some meaningful messages for annotation, we select only messages with length between 50 and 500 characters.### Source Data


Sports News Articles comments#### Initial Data Collection and Normalization#### Who are the source language producers?


Sports News Article readers passage: ### Annotations


* Andrei Paraschiv
* Irina Maria Sandu#### Annotation process##### OTHER


Label used for non offensive texts.##### PROFANITY


This is the "lighter" form of abusive language. When profane words are used without a direct intend on offending a target, or without ascribing some negative qualities to a target we use this label. Some messages in this class may even have a positive sentiment and uses swearwords as emphasis. Messages containing profane words that are not directed towards a specific group or person, we label as PROFANITY


Also, self censored messages with swear words having some letters hidden, or some deceitful misspellings of swearwords that have clear intend on circumventing profanity detectors will be treated as PROFANITY.##### INSULT


The message clearly wants to offend someone, ascribing negatively evaluated qualities or deficiences, labeling a person or a group of persons as unworthy or unvalued. Insults do imply disrespect and contempt directed towards a target.##### ABUSE


This label marks messages containing the stronger form of offensive and abusive language. This type of language ascribes the target a social identity that is judged negatively by the majority of society, or at least is percieved as a mostly negative judged identity. Shameful, unworthy or morally unaceptable identytities fall in this category. In contrast to insults, instances of abusive language require that the target of judgment is seen as a representative of a group and it is ascribed negative qualities that are taken to be universal, omnipresent and unchangeable characteristics of the group.


In contrast to insults, instances of abusive language require that the target of judgment tis seen as a representative of a group and it is ascribed negative qualities that are taken to be universal, omnipresent and unchangeable characteristics of the group.


Additional, dehumanizing language targeting a person or group is also classified as ABUSE.#### Who are the annotators?


Native speakers### Personal and Sensitive Information


The data was public at the time of collection. PII removal has been performed.


Considerations for Using the Data
---------------------------------