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

CHS：小型：协作研究：衡量和提高在线新闻讨论的质量

基本信息

批准号：
1717688
负责人：
Paul Resnick
金额：
$ 44.87万
依托单位：
Regents of the University of Michigan - Ann Arbor
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2017
资助国家：
美国
起止时间：
2017-08-15 至 2022-07-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1717688&HistoricalAwards=false
关键词：
CHS Small Collaborative Research Measuring

项目摘要

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.

该项目将加大人们在在线对话中发挥他人最佳能力的努力，并使人们更容易找到高质量的在线对话。目前，人们对关于公共事务的在线对话的语气和内容感到许多关切。在最好的情况下，每天的在线辩论可以引导人们考虑其他观点，甚至改变他们的想法。这种情况发生在人们可能不同意的环境中，但他们试图告知和说服对方，而不是简单地互相大喊大叫。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。分类器将评估关于Twitter、Facebook、Reddit等公共场所新闻文章的在线对话的质量，以及新闻页面的评论部分。一个会话分类工具（一个网站和一个浏览器扩展）将使用自动分类器来真实的推荐正在讨论特定新闻文章和质量分数高的场所。该研究的第二个目标是创建一个对话教练，帮助公众提高他们参与的对话空间的质量，帮助他们制作直接有助于质量和间接激励他人的信息。它将包括一个从对话中提取元素以帮助人们制作消息的消息助手和一个消息影响评估器，该评估器预测草案消息对后续对话质量指标的可能影响。分类器的训练数据将从对话参与者以及经过训练的编码器中收集，并将进行实验以确定向对话参与者提出的最有效的请求序列，以最大限度地提高贡献的动机。会话推荐器的创建将导致几个智力贡献，包括：（1）开发帮助人类评分员实现高评分员间可靠性的计算辅助;（2）确定激励会话参与者充当评分员的方法;（3）构建基于神经网络的分类器，当使用收集的评分作为训练数据进行训练时，该分类器实现高预测准确性;（4）开发技术，使分类器产生可解释的结果（解释）。会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会话部分，以及作者在撰写消息时发现相关和有用的部分;（2）构建一个预测模型，准确估计消息对后续会话质量的影响。