grawlix plugin that replaces racial slurs with nonsense symbols
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Updated
Apr 19, 2017 - JavaScript
grawlix plugin that replaces racial slurs with nonsense symbols
Sinhalese Language based Hate Speech Detection
Privy filter is made to give the method for removing/accommodating for unethical bias in data.
The Ally App is a place where people can go to learn more about how to combat racial inequalities, view media resources that aid in combatting those inequalities, get assistance from people seeking to be allies, give assistance, and start petitions.
Classify tweets as Racist or Non-Racist using Natural Language Processing and Machine Learning
Project for course 'Big data in society' at VU Amsterdam Summer School, in 2016.
Tracking the changes in online sinophobic hate speech resulting from the progress of the Covid19 pandemic to raise awareness and provide anti-racists organizations with contextualized information to support their actions.
Anti-racism resources translated into Hindi, Urdu, Punjabi & more.
Racism Detection built using Long Short-Term Memory (LSTM) on Indonesian texts twitter
black v white - deaths by cop disparity heat map
Project CRANE (Crisis Racism and Narrative Evaluation) aims to support researchers and anti-racist organisations that wish to use state-of-the-art text analysis algorithms to study how specific events impact online hate speech and racist narratives. CRANE Toolbox is a Python package: once installed, the tools in CRANE are available as functions …
Hindi translations for the popular White Supremacy graphic featuring a set 📄 of 67 terms to describe overt & covert forms of racism
Urdu translations for the popular White Supremacy graphic featuring a set 📄 of 67 terms to describe overt & covert forms of racism
Punjabi translations for the popular White Supremacy graphic featuring a set 📄 of 67 terms to describe overt & covert forms of racism
Antiracist self-education information and resources
Hate Speech in the form of racism and sexism has become a nuisance on Twitter and it is important to segregate these sort of tweets from the rest. In this problem, I have taken the Twitter data that has both normal and hate tweets. So the task was to identify the tweets which have a positive connotation from the tweets that have a negative one.
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