CHS: Small: Collaborative Research: Measuring and Promoting the Quality of Online News Discussions

这个项目将扩大人们在在线对话中展示他人最好的一面的努力，并将使人们更容易找到高质量的在线对话。目前，关于公共事务的在线对话的基调和内容令人担忧。在最好的情况下，每天的在线辩论可以引导人们考虑不同的观点，甚至改变主意。这发生在人们可能不同意的环境中，但他们试图相互告知并说服对方，而不是简单地对彼此大喊大叫。这项研究的第一个目标是创建自动分类器来衡量日常在线政治谈话的质量。分类器将评估Twitter、Facebook、Reddit等公共场所以及新闻页面的评论部分关于新闻文章的在线对话的质量。对话查找器工具(网站和浏览器扩展)将使用自动分类器实时推荐正在讨论特定新闻文章和质量分数较高的地点。这项研究的第二个目标是创建一个对话教练，通过帮助公众制定直接有助于提高质量并间接激励他人的信息，来帮助普通公众提高他们参与的对话空间的质量。它将包括一个消息助手，它从对话中提取元素，以帮助人们制作消息，以及一个消息影响评估器，它预测消息草稿对后续对话质量指标的可能影响。在线对话的质量将根据传播学学者所阐述的各种维度来衡量。除训练有素的编码员外，还将从对话参与者那里收集分类器的培训数据，并将进行实验，以确定向对话参与者提出的最有效的请求顺序，以便最大限度地激发做出贡献的动机。会话推荐器的创建将带来几个智力贡献，包括：(1)开发计算辅助工具，帮助人类评分者实现高评分者之间的可靠性；(2)确定激励对话参与者充当评分者的方法；(3)设计基于神经网络的分类器，在使用收集的评分作为训练数据进行训练时，达到高预测精度；(4)开发技术，使分类器产生可解释的结果(解释)。对话教练的创建将带来两个智力贡献：(1)识别可以自动提取的对话部分，以及作者在撰写消息时认为相关和有用的部分；(2)构建预测模型，准确估计消息对后续对话质量的影响。

This project will amplify the efforts of people to bring out the best in other people in online conversations, and will make it easier for people to find high quality online conversations. There are numerous concerns about the tone and content of online conversations on public affairs at the present time. At its best, everyday online debate can lead people to consider alternative perspectives and even change their minds. This happens in environments where people may disagree, but where they try to inform and convince each other rather than simply yell at each other. The first goal of the research is to create automated classifiers to measure the quality of everyday online political talk. Classifiers will estimate the quality of online conversations about news articles in public venues such as Twitter, Facebook, Reddit, and the comments sections of news pages. A Conversation Finder tool (a website and a browser extension) will use the automated classifiers to recommend, in real time, venues where particular news articles are being discussed and where the quality scores are high. The second goal of the research is to create a Conversation Coach that helps the general public to improve the quality of conversation spaces they participate in, by helping them craft messages that directly contribute to quality and that indirectly inspire others. It will include a Message Assistant that extracts elements from conversations in order to help people craft messages and a Message Impact Assessor that predicts the likely impact of a draft message on the quality metrics for subsequent conversations.Quality of online conversations will be measured in terms of a variety of dimensions that communication scholars have articulated as desirable. Training data for the classifiers will be collected from conversation participants in addition to trained coders, and experiments will be conducted to determine the most effective sequence of requests to make of conversation participants in order to maximize motivation to contribute. Creation of the Conversation Recommender will lead to several intellectual contributions, including: (1) developing computational assists that help human raters achieve high inter-rater reliability; (2) identifying methods to motivate conversation participants to act as raters; (3) architecting neural-network based classifiers that achieve high prediction accuracy when trained using the collected ratings as training data; (4) developing techniques to make the classifiers produce interpretable results (explanations). Creation of the Conversation Coach will lead to two intellectual contributions: (1) identifying parts of conversations that can be automatically extracted and that writers find relevant and useful when composing messages; (2) architecting a predictive model that accurately estimates the impact of messages on subsequent conversation quality.