The proliferation of social media enables people to express their opinions widely online. However, at the same time, this has resulted in the emergence of conflict and hate, making online environments uninviting for users. Although researchers have found that hate is a problem across multiple platforms, there is a lack of models for online hate detection using multi-platform data. To address this research gap, we collect a total of 197,566 comments from four platforms: YouTube, Reddit, Wikipedia, and Twitter, with 80% of the comments labeled as non-hateful and the remaining 20% labeled as hateful. We then experiment with several classification algorithms (Logistic Regression, Naïve Bayes, Support Vector Machines, XGBoost, and Neural Networks) and feature representations (Bag-of-Words, TF-IDF, Word2Vec, BERT, and their combination). While all the models significantly outperform the keyword-based baseline classifier, XGBoost using all features performs the best (F1 = 0.92). Feature importance analysis indicates that BERT features are the most impactful for the predictions. Findings support the generalizability of the best model, as the platform-specific results from Twitter and Wikipedia are comparable to their respective source papers. We make our code publicly available for application in real software systems as well as for further development by online hate researchers.

中文翻译：

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为多个社交媒体平台开发在线仇恨分类器

社交媒体的普及使人们能够在网上广泛表达自己的观点。但是，与此同时，这导致了冲突和仇恨的出现，使在线环境不适合用户使用。尽管研究人员发现仇恨是跨多个平台的问题，但仍缺乏使用多平台数据进行在线仇恨检测的模型。为了弥补这一研究差距，我们从四个平台（YouTube，Reddit，Wikipedia和Twitter）上收集了197,566条评论，其中80％的评论被标记为不讨厌，其余20％的标记为可恨。然后，我们尝试使用几种分类算法（逻辑回归，朴素贝叶斯，支持向量机，XGBoost和神经网络）和特征表示（单词包，TF-IDF，Word2Vec，BERT及其组合）。尽管所有模型的性能均明显优于基于关键字的基线分类器，但使用所有功能的XGBoost表现最佳（F1 = 0.92）。特征重要性分析表明，BERT特征对预测影响最大。由于Twitter和Wikipedia的特定于平台的结果可与各自的原始论文相提并论，因此发现结果支持最佳模型的通用性。我们公开提供代码，以供实际软件系统中的应用程序以及在线仇恨研究人员的进一步开发使用。因为Twitter和Wikipedia在特定平台上的结果可与它们各自的原始资料相媲美。我们公开提供代码，以供实际软件系统中的应用程序以及在线仇恨研究人员的进一步开发使用。因为Twitter和Wikipedia在特定平台上的结果可与它们各自的原始资料相媲美。我们公开提供代码，以供实际软件系统中的应用程序以及在线仇恨研究人员的进一步开发使用。