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.

该项目将扩大人们在在线对话中提出最好的人的努力，并使人们更容易找到高质量的在线对话。目前，人们对在线对话的语调和内容有很多担忧。最好的是，日常在线辩论可以使人们考虑其他观点，甚至改变主意。这发生在人们可能不同意的环境中，但是他们试图互相告知和说服，而不是简单地互相说服。该研究的第一个目标是创建自动分类器，以衡量日常在线政治谈话的质量。分类器将估算有关在Twitter，Facebook，Reddit和新闻页面的评论部分等公共场所中有关新闻文章的在线对话的质量。对话查找器工具（网站和浏览器扩展程序）将使用自动分类器，以实时推荐讨论特定新闻文章的场所，质量分数很高。该研究的第二个目标是创建一个对话教练，通过帮助他们制作直接有助于质量并间接启发他人的信息来帮助公众提高参与对话空间的质量。它将包括一条消息助理，该消息助理从对话中提取元素，以帮助人们制作信息和一个消息影响评估者，以预测消息草案对随后对话的质量指标的可能影响。在线对话的质量将根据各种维度来衡量，这些维度可以表明沟通学者表达为可取的。除了训练有素的编码人员之外，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，以确定最有效的请求顺序，以使对话参与者最大化贡献的动力。推荐对话的创建将导致一些智力贡献，包括：（1）开发计算辅助工具，以帮助人类评估者实现高评估者的可靠性；（2）确定激励对话参与者充当评估者的方法；（3）架构基于神经网络的分类器，这些分类器在使用收集的评级作为培训数据进行培训时，可以实现高预测准确性；（4）开发使分类器产生可解释结果的技术（说明）。对话教练的创建将导致两个智力贡献：（1）确定可以自动提取的对话的一部分，并在撰写消息时发现相关和有用；（2）构建一个预测模型，该模型可以准确估算消息对随后的对话质量的影响。