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.

National Science Foundation (NSF)
Division of Information and Intelligent Systems (IIS)
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William Bainbridge
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Cornell University
United States
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