datavaluepeople/Intimidating-speech-Sahel-FR-EN

Model Card for Model ID

This model classifies text - specifically social media posts - on if they contain intimidating speech. It has been fine-tuned on a small sample (n=8442) of facebook comments, which are only slightly imbalanced (n_intimidating=3115, n_not_intimidating=5327). These comments were placed under facebook posts originating from 3 countries in the Sahel region (Burkina Faso, Niger, Mali). The language in the dataset is predominantly French and English, with some code-switching between the two languages. It is also slang-heavy as it is social media data.

Model Details

Model Description

• Developed by: datavaluepeople in conjunction with How to Build UP for a project to find and counteract intimidating speech which
• Language(s) (NLP): French, English
• License: agpl-3.0
• Finetuned from model: google-bert/bert-base-multilingual-uncased

Uses

This model is intended for use by (digital) peacebuilders and anyone working towards depolarising the digital public sphere. We have used it in conjunction with Phoenix, an ethical, open source, non-commercial platform designed to enable peacebuilders and mediators to conduct ethical and participatory social media listening in order to inform conflict transformation work. There is a lot of social media data, and this allows peacebuilders to quickly find instances of comments that discourage the dialogue to continue through intimidation of the other participants. The peacebuilder can then use this to inform programming, as well as conduct targeted interventions to mitigate that intimidation.

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Recommendations

Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.

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Training Details

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Summary

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Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

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