Dataset:

Task Categories: text-classification
Languages: tl
Multilinguality: monolingual
Size Categories: 10K<n<100K
Licenses: unknown
Language Creators: crowdsourced
Annotations Creators: machine-generated

Dataset Card for Hate Speech in Filipino

Dataset Summary

Contains 10k tweets (training set) that are labeled as hate speech or non-hate speech. Released with 4,232 validation and 4,232 testing samples. Collected during the 2016 Philippine Presidential Elections.

Supported Tasks and Leaderboards

[More Information Needed]

Languages

The dataset is primarily in Filipino, with the addition of some English words commonly used in Filipino vernacular

Dataset Structure

Data Instances

Sample data:

{
  "text": "Taas ni Mar Roxas ah. KULTONG DILAW NGA NAMAN",
  "label": 1
}

Data Fields

[More Information Needed]

Data Splits

[More Information Needed]

Dataset Creation

Curation Rationale

This study seeks to contribute to the filling of this gap through the development of a model that can automate hate speech detection and classification in Philippine election-related tweets. The role of the microblogging site Twitter as a platform for the expression of support and hate during the 2016 Philippine presidential election has been supported in news reports and systematic studies. Thus, the particular question addressed in this paper is: Can existing techniques in language processing and machine learning be applied to detect hate speech in the Philippine election context?

Source Data

Initial Data Collection and Normalization

The dataset used in this study was a subset of the corpus 1,696,613 tweets crawled by Andrade et al. and posted from November 2015 to May 2016 during the campaign period for the Philippine presidential election. They were culled based on the presence of candidate names (e.g., Binay, Duterte, Poe, Roxas, and Santiago) and election-related hashtags (e.g., #Halalan2016, #Eleksyon2016, and #PiliPinas2016).

Data preprocessing was performed to prepare the tweets for feature extraction and classification. It consisted of the following steps: data de-identification, uniform resource locator (URL) removal, special character processing, normalization, hashtag processing, and tokenization.

[More Information Needed]

Who are the source language producers?

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Annotations

Annotation process

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Who are the annotators?

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Personal and Sensitive Information

[More Information Needed]

Considerations for Using the Data

Social Impact of Dataset

[More Information Needed]

Discussion of Biases

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Other Known Limitations

[More Information Needed]

Additional Information

Dataset Curators

Jan Christian Cruz

Licensing Information

[More Information Needed]

Citation Information

@article{Cabasag-2019-hate-speech, title={Hate speech in Philippine election-related tweets: Automatic detection and classification using natural language processing.}, author={Neil Vicente Cabasag, Vicente Raphael Chan, Sean Christian Lim, Mark Edward Gonzales, and Charibeth Cheng}, journal={Philippine Computing Journal}, volume={XIV}, number={1}, month={August}, year={2019} }

Contributions

Thanks to @anaerobeth for adding this dataset.

Models trained or fine-tuned on hate_speech_filipino

None yet