Summary of Empirical Evaluation Of Public Hatespeech Datasets, by Sadar Jaf and Basel Barakat
Empirical Evaluation of Public HateSpeech Datasets
by Sadar Jaf, Basel Barakat
First submitted to arxiv on: 27 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 This study evaluates the quality of public datasets used for training machine learning algorithms to detect hate speech on social media platforms. Despite the benefits of social media, targeted hate speech remains a significant risk for users. Existing datasets are limited, leading to inaccurate classification and hindering effective training of algorithms. The paper presents a comprehensive analysis of several popular public datasets, highlighting their limitations and strengths through statistical analyses. This work aims to improve the development of more accurate machine learning models for hate speech detection by addressing dataset limitations. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study looks at the problem of hate speech on social media. Right now, there are many datasets that people use to train machines to detect hate speech. But these datasets have some big problems. This makes it hard for machines to accurately detect hate speech. The researchers looked at several popular datasets and found their flaws. They want to fix this by making better datasets so machines can do a better job of detecting hate speech. |
Keywords
» Artificial intelligence » Classification » Machine learning
