IBSS: The Spread and Impact of Moral Messages: Machine Learning, Network Evolution, and Behavioral Prediction
IBSS:道德信息的传播和影响:机器学习、网络进化和行为预测
基本信息
- 批准号:1520031
- 负责人:
- 金额:$ 64.03万
- 依托单位:
- 依托单位国家:美国
- 项目类别:Standard Grant
- 财政年份:2015
- 资助国家:美国
- 起止时间:2015-08-15 至 2019-01-31
- 项目状态:已结题
- 来源:
- 关键词:
项目摘要
In the immediate aftermath of the 2013 Boston Marathon tragedy, hundreds of thousands of prosocial acts were evident on social media, such as reposted links for blood donation sites, information regarding how to get in touch with loved ones, and even offers to provide food and shelter for those in need. Far from isolated acts, these behaviors occurred within social networks, amid shared moral messages of empathy and solidarity. This interdisciplinary research project will examine how people respond to public crises and how moral reactions shape these responses in social networks. The project will contribute new theoretical insights and methodological advances in moral psychology, network sociology, computer science, and other fields. It will enhance understanding of how moral concerns spread through social networks and explore new theoretical frameworks dealing with human moral decision making and group dynamics. These theoretical frameworks will guide the development of artificial intelligence techniques for building descriptive models of morality, and the new methods of sentiment analysis and machine learning will be used to assess theoretical models of moral concerns and social influence in networks. By examining factors influencing the spread of moral messages and participation in prosocial activities, such as charitable giving, the project may help increase the well-being of individuals in emergency situations. The project also will facilitate future inquiry into how the public and persistent nature of social media may provide new ways to understand and forecast social change.The interdisciplinary science of morality has developed well-validated measures of moral concerns using a number of different approaches, such as Moral Foundations Theory and Schwartz's Values Circumplex. Empirical research in this field usually has assessed moral judgments via questionnaires gathering information well after actions have occurred, however. Sociology has done more to assess behavior as it occurs but has used even more limited measures. Recent innovations in computer science offer new ways to gather information about the structure of moral judgments and large-scale behavior in natural settings as well as the relationships between the two. The investigators will employ these new computer-based methods to examine texts from social media in order to examine the structure of moral concerns and values without relying on preset questionnaires. They will investigate the network dynamics of the spread of moral messages and behaviors, and they will determine how moral content in social media can predict real-world behavior at both individual and societal scales. The investigators will couple machine learning and sentiment analysis techniques with theories about moral cognition and social dynamics. Among questions they will pursue are how well everyday moral judgments (made without researcher prompting) correspond with dominant psychological theories of morality and whether it is possible to model and predict how moral influence can lead to subsequent prosocial or antisocial behavior. This project is supported through the NSF Interdisciplinary Behavioral and Social Sciences Research (IBSS) competition.
在2013年波士顿马拉松悲剧发生后不久,社交媒体上出现了数十万起亲社会行为,例如重新发布献血网站的链接,关于如何与亲人联系的信息,甚至为有需要的人提供食物和住所。这些行为远非孤立的行为,而是发生在社交网络中,在共同的同情和团结的道德信息中。 这个跨学科的研究项目将研究人们如何应对公共危机,以及道德反应如何在社交网络中塑造这些反应。 该项目将在道德心理学、网络社会学、计算机科学和其他领域提供新的理论见解和方法论进展。 它将加强对道德问题如何通过社交网络传播的理解,并探索处理人类道德决策和群体动力学的新理论框架。 这些理论框架将指导人工智能技术的发展,用于构建道德的描述性模型,情感分析和机器学习的新方法将用于评估网络中道德关注和社会影响的理论模型。 通过审查影响道德信息传播和参与慈善捐赠等亲社会活动的因素,该项目可能有助于提高紧急情况下个人的福祉。 该项目还将促进未来的调查如何社会媒体的公共性和持久性的性质可能提供新的方式来理解和预测社会变革。跨学科的道德科学已经开发出了良好的验证措施的道德关注使用一些不同的方法,如道德基础理论和施瓦茨的价值观回旋。 然而,这一领域的实证研究通常在行为发生后通过问卷收集信息来评估道德判断。 社会学已经做了更多的评估行为,因为它发生,但使用更有限的措施。 计算机科学的最新创新提供了新的方法来收集有关道德判断结构和自然环境中大规模行为以及两者之间关系的信息。 研究人员将使用这些新的基于计算机的方法来检查来自社交媒体的文本,以便在不依赖预设问卷的情况下检查道德关注和价值观的结构。 他们将研究道德信息和行为传播的网络动态,并确定社交媒体中的道德内容如何在个人和社会尺度上预测现实世界的行为。研究人员将把机器学习和情感分析技术与道德认知和社会动力学理论结合起来。 他们将探讨的问题包括日常道德判断(在没有研究人员提示的情况下做出的)与主流道德心理学理论的对应程度,以及是否有可能建模和预测道德影响如何导致随后的亲社会或反社会行为。 该项目通过NSF跨学科行为和社会科学研究(IBSS)竞赛获得支持。
项目成果
期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
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Morteza Dehghani其他文献
The cultural influence model: when accented natural language spoken by virtual characters matters
文化影响模型:当虚拟角色所说的带口音的自然语言很重要时
- DOI:
- 发表时间:
2017 - 期刊:
- 影响因子:3
- 作者:
P. Khooshabeh;Morteza Dehghani;Angela Nazarian;J. Gratch - 通讯作者:
J. Gratch
The Moral Foundations Reddit Corpus
道德基础 Reddit 语料库
- DOI:
10.48550/arxiv.2208.05545 - 发表时间:
2022 - 期刊:
- 影响因子:0
- 作者:
Jackson Trager;Alireza S. Ziabari;A. Davani;Preni Golazazian;Farzan Karimi;Ali Omrani;Zhihe Li;Brendan Kennedy;N. K. Reimer;M. Reyes;Kelsey Cheng;Mellow Wei;Christina Merrifield;Arta Khosravi;E. Álvarez;Morteza Dehghani - 通讯作者:
Morteza Dehghani
Title: into the Wild: Big Data Analytics in Moral Psychology
标题:走进野外:道德心理学中的大数据分析
- DOI:
- 发表时间:
2016 - 期刊:
- 影响因子:0
- 作者:
Morteza Dehghani;Kate M. Johnson;Rumen Iliev;J. Graham - 通讯作者:
J. Graham
1 ANALOGY AND MORAL DECISION MAKING
1 类比与道德决策
- DOI:
- 发表时间:
2009 - 期刊:
- 影响因子:0
- 作者:
Morteza Dehghani;D. Gentner;Ken Forbus;H. Ekhtiari;Sonya S. Sachdeva - 通讯作者:
Sonya S. Sachdeva
Correction to: Phosphodiesterase 10A Inhibition Leads to Brain Region-Specific Recovery Based on Stroke Type
- DOI:
10.1007/s12975-020-00858-1 - 发表时间:
2020-10-29 - 期刊:
- 影响因子:4.300
- 作者:
Shirin Z. Birjandi;Nora Abduljawad;Shyama Nair;Morteza Dehghani;Kazunori Suzuki;Haruhide Kimura;S. Thomas Carmichael - 通讯作者:
S. Thomas Carmichael
Morteza Dehghani的其他文献
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{{ truncateString('Morteza Dehghani', 18)}}的其他基金
SCC-CIVIC-FA Track B: Everyday Respect: Measuring & Improving Communication During Motor Vehicle Stops
SCC-CIVIC-FA 轨道 B:日常尊重:测量
- 批准号:
2322026 - 财政年份:2023
- 资助金额:
$ 64.03万 - 项目类别:
Standard Grant
SCC-CIVIC-PG Track B: Everyday Respect: Measuring & Improving Police Officer Communication During Motor Vehicle Stops
SCC-CIVIC-PG 轨道 B:日常尊重:测量
- 批准号:
2228785 - 财政年份:2022
- 资助金额:
$ 64.03万 - 项目类别:
Standard Grant
RAPID: Investigating Social Influence and Mitigating Disinformation Campaigns in Non-English Social Media
RAPID:调查非英语社交媒体中的社会影响力并减少虚假信息活动
- 批准号:
2304209 - 财政年份:2022
- 资助金额:
$ 64.03万 - 项目类别:
Standard Grant
EAGER: SaTC: Investigation of misinformation beliefs expressed and spread online
EAGER:SaTC:对网上表达和传播的错误信息信念进行调查
- 批准号:
2140473 - 财政年份:2021
- 资助金额:
$ 64.03万 - 项目类别:
Standard Grant
CAREER: Developing Computational Methods to predict Hate Crimes
职业:开发预测仇恨犯罪的计算方法
- 批准号:
1846531 - 财政年份:2019
- 资助金额:
$ 64.03万 - 项目类别:
Continuing Grant
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