arXiv 2206.129832023-09-24cs.CLcs.LG

可解释且高性能的仇恨与冒犯性言论检测

Explainable and High-Performance Hate and Offensive Speech Detection

Yale University（耶鲁大学）；Clarkson University（克拉克森大学）

Marzieh Babaeianjelodar, Gurram Poorna Prudhvi, Stephen Lorenz, Keyu Chen, Sumona Mondal, Soumyabrata Dey, Navin Kumar

发表信息：: 24th International Conference on Human-Computer Interaction 2022

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AI总结：

本文提出基于XGBoost并用SHAP解释输出的仇恨与冒犯性言论检测模型，在不平衡及下采样Twitter数据上对比多种黑盒模型验证了性能与可解释性。

摘要：

通过社交媒体平台传播信息可能会营造出对弱势社群充满敌意的环境，并使社会中的某些群体沉默。为减少此类情况，研究人员已开发若干模型来检测仇恨与冒犯性言论。由于在社交媒体平台上检测仇恨和冒犯性言论可能错误地将个人排除在平台之外，从而降低信任，因此有必要构建可解释、可理解的模型。为此，我们基于XGBoost算法构建了一个可解释、可理解的高性能模型，并使用Twitter数据进行训练。对于不平衡的Twitter数据，XGBoost在仇恨言论检测上优于LSTM、AutoGluon和ULMFiT模型，其F1分数为0.75，而三者分别为0.38、0.37和0.38。当我们将数据下采样为三个各含约5000条推文的独立类别时，XGBoost的表现优于LSTM、AutoGluon和ULMFiT；其仇恨言论检测F1分数为0.79，三者分别为0.69、0.77和0.66。在下采样版本的冒犯性言论检测中，XGBoost同样与LSTM、AutoGluon和ULMFiT进行了比较，F1分数为0.83，三者分别为0.88、0.82和0.79。我们对XGBoost模型的输出使用Shapley Additive Explanations（SHAP），使其相较于属于黑盒模型的LSTM、AutoGluon和ULMFiT具备可解释性和可理解性。

英文摘要：

The spread of information through social media platforms can create environments possibly hostile to vulnerable communities and silence certain groups in society. To mitigate such instances, several models have been developed to detect hate and offensive speech. Since detecting hate and offensive speech in social media platforms could incorrectly exclude individuals from social media platforms, which can reduce trust, there is a need to create explainable and interpretable models. Thus, we build an explainable and interpretable high performance model based on the XGBoost algorithm, trained on Twitter data. For unbalanced Twitter data, XGboost outperformed the LSTM, AutoGluon, and ULMFiT models on hate speech detection with an F1 score of 0.75 compared to 0.38 and 0.37, and 0.38 respectively. When we down-sampled the data to three separate classes of approximately 5000 tweets, XGBoost performed better than LSTM, AutoGluon, and ULMFiT; with F1 scores for hate speech detection of 0.79 vs 0.69, 0.77, and 0.66 respectively. XGBoost also performed better than LSTM, AutoGluon, and ULMFiT in the down-sampled version for offensive speech detection with F1 score of 0.83 vs 0.88, 0.82, and 0.79 respectively. We use Shapley Additive Explanations (SHAP) on our XGBoost models' outputs to makes it explainable and interpretable compared to LSTM, AutoGluon and ULMFiT that are black-box models.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。