OpenFraming：我们带来了ML ;你带来了数据。与您的数据交互并发现其框架
OpenFraming: We brought the ML; you bring the data. Interact with your data and discover its frames
摘要：
当记者报道新闻故事时，他们可以从多个角度或视角报道故事。例如，一篇关于COVID-19的新闻文章可能会关注个人预防措施，如戴口罩，而另一篇可能会关注COVID-19对经济的影响。这些观点被称为“框架”，使用时可能会影响公众对问题的看法和意见。我们介绍了一个基于Web的系统，用于分析和分类文本文档中的框架。我们的目标是基于主题建模和深度学习，为来自不同学科的研究人员提供有效的自动框架发现和标记工具。为此，我们提供了关于各种问题的最先进的预训练框架分类模型，以及用于在用户提供的语料库上训练新分类模型的用户友好管道。研究人员可以提交他们的文件，并获得文件的框架。用户参与的程度是灵活的：他们可以运行已经预先训练过的模型;提交标记的文档并训练一个新的模型进行框架分类;或者提交未标记的文档并获得文档的潜在框架。组成我们系统的代码也是开源的，并且有很好的文档记录，使系统透明且可扩展。该系统可在http://www.openframing.org和我们的GitHub页面https://github.com/davidatbu/openFraming上获得。
英文摘要：
When journalists cover a news story, they can cover the story from multiple angles or perspectives. A news article written about COVID-19 for example, might focus on personal preventative actions such as mask-wearing, while another might focus on COVID-19's impact on the economy. These perspectives are called "frames," which when used may influence public perception and opinion of the issue. We introduce a Web-based system for analyzing and classifying frames in text documents. Our goal is to make effective tools for automatic frame discovery and labeling based on topic modeling and deep learning widely accessible to researchers from a diverse array of disciplines. To this end, we provide both state-of-the-art pre-trained frame classification models on various issues as well as a user-friendly pipeline for training novel classification models on user-provided corpora. Researchers can submit their documents and obtain frames of the documents. The degree of user involvement is flexible: they can run models that have been pre-trained on select issues; submit labeled documents and train a new model for frame classification; or submit unlabeled documents and obtain potential frames of the documents. The code making up our system is also open-sourced and well-documented, making the system transparent and expandable. The system is available on-line at http://www.openframing.org and via our GitHub page https://github.com/davidatbu/openFraming .
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。
