BullyBlocker: toward an interdisciplinary approach to identify cyberbullying

BullyBlocker: toward an interdisciplinary approach to identify cyberbullying
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BullyBlocker:采用跨学科方法来识别网络欺凌

DOI:
10.1007/s13278-018-0496-z
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发表时间:
2018
影响因子:
2.8
通讯作者:
Rich, Christopher
Rich, Christopher
中科院分区:
--
文献类型:
--
作者:
Silva, Yasin N.;Hall, Deborah L.;Rich, Christopher

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网络欺凌是故意使用在线数字媒体来传达关于另一个人的虚假,尴尬或敌对信息。这是青少年最常见的网络风险,但超过一半的年轻人在发生时不会告诉父母。虽然已经有许多关于网络欺凌的性质和普遍性的研究,但在网络欺凌的自动识别领域,结合计算机科学和心理学的研究结果的研究相对较少。因此,我们工作的目标是采用跨学科的方法来开发一个自动化模型,用于识别和测量社交网站中的网络欺凌程度,以及基于该模型的Facebook应用程序,该应用程序通知父母他们的青少年是网络欺凌受害者的可能性。本文介绍了与建立一个计算机模型的网络欺凌识别相关的挑战,提出了心理学研究的主要成果,可用于通知这样的模型,介绍了一个整体模型和移动的应用程序设计的网络欺凌识别,提出了一种新的评估框架,用于评估识别模型的有效性,并强调了未来工作的关键领域。重要的是,这个可以应用于其他社交网站的模型是我们所知道的第一个将计算机科学和心理学联系起来解决这个及时性问题的模型。
Cyberbullying is the deliberate use of online digital media to communicate false, embarrassing, or hostile information about another person. It is the most common online risk for adolescents, yet well over half of young people do not tell their parents when it occurs. While there have been many studies about the nature and prevalence of cyberbullying, there have been relatively few in the area of automated identification of cyberbullying that integrate findings from computer science and psychology. The goal of our work is thus to adopt an interdisciplinary approach to develop an automated model for identifying and measuring the degree of cyberbullying in social networking sites, and a Facebook app, built on this model, that notifies parents about the likelihood that their adolescent is a cyberbullying victim. This paper describes the challenges associated with building a computer model for cyberbullying identification, presents key results from psychology research that can be used to inform such a model, introduces a holistic model and mobile app design for cyberbullying identification, presents a novel evaluation framework for assessing the effectiveness of the identification model, and highlights crucial areas of future work. Importantly, the proposed model—which can be applied to other social networking sites—is the first that we know of to bridge computer science and psychology to address this timely problem.
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