Doctoral Dissertation Research in DRMS: Judgments of Answerers and the Answers They Give

人类社会生活的一个基本特征是提问和回答问题，这是一项复杂的任务，需要一个人决定问谁，如何表达问题，以及如何解释答案和判断其价值。然而，尽管这项活动是中心活动，但人们对这一过程每一步所涉及的一般原则知之甚少，这使得很难设计出更好的方法来帮助人们提出问题和找到他们需要的信息。这个问题在信息时代变得更加紧迫：人们问的问题比以往任何时候都多，涉及的话题更广，他们不仅把这些问题带给朋友和家人，还把这些问题带给更广泛的网络公告栏和社交媒体世界上的陌生人和熟人。这篇论文结合了心理学和计算机科学的方法，解决了我们在理解上的差距。来自机器学习和人工智能的工具将被用来分析人们在在线公告牌系统上产生的大规模问题和答案的集合，结果将被用来建立解释和预测人们如何以及何时找到好的问题答案的一般理论。在测试这些理论的实验室实验中，参与者将看到具有不同潜在属性的问题和答案，结果将确定对信息收集特别关键的特征。数据科学调查的大规模性质为理论的发展提供了基础，这些理论基于问题与答案之间的微妙模式，而实验室工作则允许对问题的因果性质进行调查：对于提问者来说，什么才是好的答案？人们探索和了解世界的最常见方式之一是通过提问从他人那里寻求信息。这种行为需要两种判断：(1)应该向谁寻求答案；(2)应该如何判断答案是否满足问题？这两个判断是相互依存的，需要人们在不确定的情况下做出决定。这项针对这些判断是如何做出的双管齐下的调查，不仅包括基于实验室的实验，这些实验是社会科学和决策科学的传统优势，还包括一个数据科学部分，研究这些决定是如何在野外做出的。这种混合的方法将被用来检验好答案的定义特征和给出好答案的人。研究的重点是人与人之间的语言对话中的问题和答案。数据科学和计算语言学的技术将被用来分析在线论坛上产生的问题和答案，从StackExchange上的技术帮助请求到Mumsnet上父母提出的更微妙的社交问题。问题和答案之间关系的两个关键属性--它们重叠的程度，以及答案对问题所构成的解空间的关注程度--被假设为对应于如何评估答案。受控实验室实验将被用来阐明回答问题的人的偏好。提问者对潜在回答者的偏好类型直接影响到收到的信息的相关性和质量，进而影响后续回答和进一步决策的效用。贝叶斯框架被用来形式化提问者面临的任务：根据他们过去的回答行为来推断一个人是什么类型的回答者。行为实验将测试该模型的关键预测。这一奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

A basic feature of human social life is asking and answering questions, a complex task that requires one to decide whom to ask, how to phrase the question, and how to interpret the answer and judge its worth. Despite the centrality of the activity, however, little is understood about the general principles involved in each step of the process, making it difficult to design better ways to help people ask questions and find the information they need. The problem becomes even more pressing in the information age: people ask more questions than ever before, on a wider range of topics, and they take those questions not only to friends and family, but also to strangers and acquaintances on the wider world of online bulletin boards and social media. This dissertation work addresses gaps in our understanding, using a combination of methods from psychology and computer science. Tools from machine learning and artificial intelligence will be used to analyze large-scale collections of questions and answers that people produce on online bulletin board systems and results will be used to build general theories that explain and predict how and when people find good answers to their questions. In laboratory experiments to test these theories, participants will be shown questions and answers with varying underlying properties, and the results will identify features that are particularly crucial for information gathering. The large-scale nature of the data science investigation affords the development of theories based on subtle patterns in the relationship between question and answer, while the laboratory work allows investigation into the causal nature of the problem: what, for the question-asker, makes an answer good?One of the most common ways people explore and learn about the world is by seeking information from others through asking questions. This behavior requires two kinds of judgments: (1) from whom one should seek answers and (2) how should one judge whether the answer satisfies the question? These two judgments are inter-dependent and require that people make decisions under uncertainty. This two-pronged investigation into how such judgments are made includes not only the lab-based experiments that are a traditional strength of social and decision sciences, but also a data science component that looks at how these decisions are made in the wild. This mix of methodologies will be used to examine the defining features of good answers and those who give them. The research focuses on questions and answers that are given in human-to-human linguistic dialogue. Techniques from data science and computational linguistics will be used to analyze collections of questions and answers produced on online discussion forums, ranging from requests for technical help on Stackexchange to more nuanced, social questions asked by parents on Mumsnet. Two key properties of the relationship between questions and answers—the extent to which they overlap, and the degree to which answers focus the solution space posed by the question—are hypothesized to correspond to how answers are evaluated. Controlled laboratory experiments will be used to elucidate preferences for those who answer questions. The types of preferences a questioner has for potential answerers has direct consequences for the relevance and quality of information received, and in turn affects the utility of subsequent answers and further decisions. A Bayesian framework is used to formalize the task facing the questioner: to infer what type of answerer an individual is, given their past answering behavior. Behavioral experiments will test key predictions of the model.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.