权益分类	功能权益	普通用户	{{item.name}}会员
{{category.name}}	{{benefitItem.name}}

EAGER: SaTC: Early-Stage Interdisciplinary Collaboration: Modeling Memory Illusion for Predicting Trust in Online Information

EAGER：SaTC：早期跨学科合作：建模记忆错觉以预测在线信息的信任

基本信息

批准号：
1915801
负责人：
Aiping Xiong
金额：
$ 30万
依托单位：
Pennsylvania State Univ University Park
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2019
资助国家：
美国
起止时间：
2019-06-01 至 2022-05-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1915801&HistoricalAwards=false
关键词：
EAGER SaTC Early Stage Interdisciplinary

项目摘要

This project integrates advances in data science and key findings from psychological research to improve the prediction of trust in information on social media by modeling the psychological phenomenon known as the memory illusion. The memory illusion refers to memory errors that people make to remember information as an outcome of interpreting and making inferences from their past experience. This project will use social media data to examine the memory illusion with online information, and to understand how it is associated with people's trust in information on social media. Better understanding on the extent and impact of the memory illusion phenomenon using big data will inform machine-learning approaches to better measure trust in information with an additional human information-processing perspective, benefiting society by providing reliable online information, and increasing people's overall trust in information on social media.This project pursues several research goals to advance the state-of-art of machine learning models to predict people's trust in information on social media. The first goal is to characterize the formation of associative inferences on Twitter information, and understand how it contributes to individuals' trust in tweets. To advance this goal, the research will use big data and data-driven machine learning models. Based on the insights learned from big data, the second goal is to establish the causal relations between identified associative inferences and people's trust of social media information with laboratory and online user studies. The last goal is to model associative inferences into machine learning algorithms to improve the prediction of user trust in online information. The project will advance the state-of-the-art with regard to our understanding on people's trust in social media information in particular and human memory illusion in general. Through interdisciplinary socio-technical collaboration, the project will advance machine-learning models considering human information processing to improve the prediction of people's trust in information on social media, and improve understanding of human behavior using a big data approach to reveal relations among psychological phenomena on a scale that has not been possible with the smaller data sets collected in the laboratory. The interdisciplinary research using data science and psychological research will address theory-based research questions regarding the relationships of information veracity, trust, and information context. Students will participate in all phases of the research.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.

该项目整合了数据科学的进步和心理学研究的关键发现，通过对被称为记忆错觉的心理现象进行建模，来改善对社交媒体信息信任的预测。记忆错觉是指人们在记忆信息时所犯的错误，这些错误是从他们过去的经验中解释和推断的结果。该项目将使用社交媒体数据来检查在线信息的记忆错觉，并了解它如何与人们对社交媒体信息的信任度相关联。使用大数据更好地了解记忆错觉现象的程度和影响，将为机器学习方法提供信息，以更好地衡量对信息的信任，并从人类信息处理的角度出发，通过提供可靠的在线信息造福社会，并增加人们对社交媒体信息的整体信任度。该项目追求几个研究目标，以推进国家的，机器学习模型的艺术，以预测人们对社交媒体上信息的信任。第一个目标是描述Twitter信息的联想推理的形成，并了解它如何有助于个人对推文的信任。为了推进这一目标，该研究将使用大数据和数据驱动的机器学习模型。基于从大数据中获得的见解，第二个目标是通过实验室和在线用户研究建立识别的关联推理与人们对社交媒体信息的信任之间的因果关系。最后一个目标是将关联推理建模到机器学习算法中，以提高在线信息中用户信任的预测。该项目将推进我们对人们对社交媒体信息的信任以及人类记忆错觉的理解。通过跨学科的社会技术合作，该项目将推进考虑人类信息处理的机器学习模型，以提高人们对社交媒体信息的信任度的预测，并使用大数据方法来提高对人类行为的理解，以揭示心理现象之间的关系，这是实验室收集的较小数据集无法实现的。利用数据科学和心理学研究的跨学科研究将解决有关信息真实性，信任和信息上下文之间关系的理论研究问题。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。