Summary of Machines Do See Color: a Guideline to Classify Different Forms Of Racist Discourse in Large Corpora, by Diana Davila Gordillo et al.
Machines Do See Color: A Guideline to Classify Different Forms of Racist Discourse in Large Corpora
by Diana Davila Gordillo, Joan Timoneda, Sebastian Vallejo Vera
First submitted to arxiv on: 17 Jan 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Medium Difficulty summary: This paper presents a step-by-step guide for identifying and categorizing various forms of racist discourse in large datasets, filling a gap in current research. By conceptualizing racism’s manifestations and contextualizing them to the time and place of interest, researchers can identify discursive forms. The authors utilize XLM-RoBERTa (XLM-R), a cross-lingual model for supervised text classification with advanced context understanding. Experimental results show that XLM-R outperforms state-of-the-art approaches in classifying racism in large corpora. A case study on Ecuadorian indígena community tweets between 2018 and 2021 demonstrates the approach’s effectiveness. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Low Difficulty summary: This research helps us understand and identify different types of racist language in large collections of text. The scientists start by defining what racism is and how it can appear differently in various contexts. They then use a special computer model called XLM-RoBERTa to analyze the text and classify it as racist or not. The results show that this approach works better than others at identifying racism in large groups of tweets about an indigenous community in Ecuador. |
Keywords
* Artificial intelligence * Discourse * Supervised * Text classification
