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CHS: Small: Automatically Identifying Misunderstandings of Social Intentions in Online Conversations

CHS：小：自动识别在线对话中社交意图的误解

基本信息

批准号：
1910147
负责人：
Cristian Danescu-Niculescu-Mi
金额：
$ 49.56万
依托单位：
Cornell University
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：
2019-08-01 至 2025-07-31
项目状态：
未结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1910147&HistoricalAwards=false
关键词：
CHS Small Automatically Identifying Misunderstandings

项目摘要

This project will develop new ways of understanding miscommunication in social media and will design novel interventions to prevent or reduce misunderstandings. Online conversations often become increasingly hostile over time, resulting in disruptive behavior such as trolling, personal attacks, harassment, and bullying. Prior work has shown that in some cases these derailments can be traced back to ill-intentioned individuals and to social contagion. In other cases, however, disruptions may result from simple misunderstandings: readers may misconstrue the social intentions of the poster, concluding that a message is meant to be friendly or hostile or aggressive when actually the opposite was intended. This project will create a framework for understanding when and why social intentions are misunderstood, computational methods for automatically capturing the effects of such misperceptions on the trajectory and outcome of online conversations, and tools that might help prevent misunderstandings. The research combines natural language processing and computer-mediated communication expertise, in the context of social computing and human-computer interaction. Unlike other work that examines how the content of messages is misunderstood, this research aims to understand problems identifying the pragmatic force of a message, for example whether it is meant as a command or request, a joke, or a hostile remark. Such "pragmatic failures" are rooted in the distinction between the literal meaning of a message and its intended meaning, as well as in the varying propensity of readers with different sociocultural backgrounds to perceive one or the other. This phenomenon is especially problematic online, where many of the paralinguistic social cues people rely on offline to disambiguate such utterances are attenuated or even completely missing. Three sets of research activities will: (1) investigate mismatches between the message creator's pragmatic intentions and how they are interpreted by different target audiences, (2) develop techniques to automatically identify when pragmatic misunderstandings have occurred and to detect their effects on conversations, and (3) develop interface tools to help prevent or overcome pragmatic failures.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.

该项目将开发新的方法来理解社交媒体中的误解，并设计新颖的干预措施来防止或减少误解。随着时间的推移，在线对话往往变得越来越敌对，导致破坏性行为，如拖钓，人身攻击，骚扰和欺凌。先前的研究表明，在某些情况下，这些出轨行为可以追溯到恶意的个人和社会传染。然而，在其他情况下，干扰可能来自简单的误解：读者可能误解了海报的社会意图，得出结论认为一个信息是友好的或敌对的或侵略性的，而实际上是相反的意图。该项目将创建一个框架，用于理解何时以及为什么社会意图被误解，自动捕获这种误解对在线对话的轨迹和结果的影响的计算方法，以及可能有助于防止误解的工具。该研究结合了自然语言处理和计算机介导的通信专业知识，在社会计算和人机交互的背景下。与其他研究信息内容如何被误解的工作不同，本研究旨在了解识别信息的语用力的问题，例如，它是否意味着命令或请求，笑话，或敌意的言论。这种“语用失误”的根源在于信息的字面意义和其预期意义之间的区别，以及不同社会文化背景的读者对这一点或另一点的不同倾向。这种现象在网络上尤其成问题，人们依赖于离线消除这种话语歧义的许多非语言社会线索被削弱甚至完全消失。三组研究活动将：（1）调查信息创建者的语用意图与不同目标受众如何解释它们之间的不匹配，（2）开发自动识别语用误解何时发生并检测其对会话的影响的技术，以及（3）开发接口工具，以帮助防止或克服务实的失败。这一奖项反映了NSF的法定使命，并已被认为是值得通过评估使用的支持基金会的学术价值和更广泛的影响审查标准。

项目成果

期刊论文数量（5）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

It Takes Two to Lie: One to Lie, and One to Listen

DOI：
10.18653/v1/2020.acl-main.353
发表时间：
2020-07
期刊：
影响因子：
0
作者：
Denis Peskov;Benny Cheng;Ahmed Elgohary;Joe Barrow;Cristian Danescu-Niculescu-Mizil;Jordan L. Boyd-Graber
通讯作者：
Denis Peskov;Benny Cheng;Ahmed Elgohary;Joe Barrow;Cristian Danescu-Niculescu-Mizil;Jordan L. Boyd-Graber

Don’t Let Me Be Misunderstood:Comparing Intentions and Perceptions in Online Discussions

不要让我被误解：比较在线讨论中的意图和看法