检测新闻文章中的毒性：在保加利亚的应用
Detecting Toxicity in News Articles: Application to Bulgarian
摘要：
网络媒体的目标是接触到越来越多的受众，吸引越来越长的注意力。这种竞争创造了一个奖励耸人听闻的，虚假的和有毒的新闻的环境。为了帮助限制它们的传播和影响，我们提出并开发了一种可以识别各种类型的有毒内容的新闻毒性检测器。虽然之前的研究主要集中在英语上，但这里我们的目标是保加利亚语。我们通过抓取一个网站创建了一个新的数据集，该网站五年来一直在收集保加利亚的新闻文章，这些文章被手动分类为八个毒性组。然后，我们训练了一个多类分类器，它有九个类别：八个有毒和一个无毒。我们尝试了基于ElMo、BERT和XLM的不同表示，以及各种特定于领域的特性。由于我们的数据集很小，我们为每种特征类型创建了一个单独的模型，并最终将这些模型组合成一个元分类器。评价结果显示，准确度为59.0%，宏观F1评分为39.7%，与多数类基线（Acc= 30.3%，宏观F1 =5.2%）相比有相当大的改善。
英文摘要：
Online media aim for reaching ever bigger audience and for attracting ever longer attention span. This competition creates an environment that rewards sensational, fake, and toxic news. To help limit their spread and impact, we propose and develop a news toxicity detector that can recognize various types of toxic content. While previous research primarily focused on English, here we target Bulgarian. We created a new dataset by crawling a website that for five years has been collecting Bulgarian news articles that were manually categorized into eight toxicity groups. Then we trained a multi-class classifier with nine categories: eight toxic and one non-toxic. We experimented with different representations based on ElMo, BERT, and XLM, as well as with a variety of domain-specific features. Due to the small size of our dataset, we created a separate model for each feature type, and we ultimately combined these models into a meta-classifier. The evaluation results show an accuracy of 59.0% and a macro-F1 score of 39.7%, which represent sizable improvements over the majority-class baseline (Acc=30.3%, macro-F1=5.2%).
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。
