PDF(Pubmed)
Abstract：
UNASSIGNED: Identifying hate speech (HS) is a central concern within online contexts. Current methods are insufficient for efficient preemptive HS identification. In this study, we present the results of an analysis of automatic HS identification applied to popular alt-right YouTube videos.
UNASSIGNED: This essay describes methodological challenges of automatic HS detection. The case study concerns data on a formative segment of contemporary radical right discourse. Our purpose is twofold. (1) To outline an interdisciplinary mixed-methods approach for using automated identification of HS. This bridges the gap between technical research on the one hand (such as machine learning, deep learning, and natural language processing, NLP) and traditional empirical research on the other. Regarding alt-right discourse and HS, we ask: (2) What are the challenges in identifying HS in popular alt-right YouTube videos?
UNASSIGNED: The results indicate that effective and consistent identification of HS communication necessitates qualitative interventions to avoid arbitrary or misleading applications. Binary approaches of hate/non-hate speech tend to force the rationale for designating content as HS. A context-sensitive qualitative approach can remedy this by bringing into focus the indirect character of these communications. The results should interest researchers within social sciences and the humanities adopting automatic sentiment analysis and for those analysing HS and radical right discourse.
UNASSIGNED: Automatic identification or moderation of HS cannot account for an evolving context of indirect signification. This study exemplifies a process whereby automatic hate speech identification could be utilised effectively. Several methodological steps are needed for a useful outcome, with both technical quantitative processing and qualitative analysis being vital to achieve meaningful results. With regard to the alt-right YouTube material, the main challenge is indirect framing. Identification demands orientation in the broader discursive context and the adaptation towards indirect expressions renders moderation and suppression ethically and legally precarious.
UNASSIGNED: This essay describes methodological challenges of automatic HS detection. The case study concerns data on a formative segment of contemporary radical right discourse. Our purpose is twofold. (1) To outline an interdisciplinary mixed-methods approach for using automated identification of HS. This bridges the gap between technical research on the one hand (such as machine learning, deep learning, and natural language processing, NLP) and traditional empirical research on the other. Regarding alt-right discourse and HS, we ask: (2) What are the challenges in identifying HS in popular alt-right YouTube videos?
UNASSIGNED: The results indicate that effective and consistent identification of HS communication necessitates qualitative interventions to avoid arbitrary or misleading applications. Binary approaches of hate/non-hate speech tend to force the rationale for designating content as HS. A context-sensitive qualitative approach can remedy this by bringing into focus the indirect character of these communications. The results should interest researchers within social sciences and the humanities adopting automatic sentiment analysis and for those analysing HS and radical right discourse.
UNASSIGNED: Automatic identification or moderation of HS cannot account for an evolving context of indirect signification. This study exemplifies a process whereby automatic hate speech identification could be utilised effectively. Several methodological steps are needed for a useful outcome, with both technical quantitative processing and qualitative analysis being vital to achieve meaningful results. With regard to the alt-right YouTube material, the main challenge is indirect framing. Identification demands orientation in the broader discursive context and the adaptation towards indirect expressions renders moderation and suppression ethically and legally precarious.
摘要：
■识别仇恨言论（HS）是在线环境中的核心问题。目前的方法不足以进行有效的抢占式HS识别。在这项研究中,我们介绍了应用于流行的alt-rightYouTube视频的自动HS识别分析结果。
■本文描述了自动HS检测的方法学挑战。案例研究涉及当代激进权利话语形成部分的数据。我们的目标是双重的。(1)概述了使用自动HS识别的跨学科混合方法方法。这一方面弥合了技术研究(如机器学习、深度学习,和自然语言处理，NLP)和传统的实证研究。关于另类权利话语和HS，我们问：（2）在流行的alt-rightYouTube视频中识别HS的挑战是什么？
■结果表明，有效和一致地识别HS通信需要进行定性干预，以避免任意或误导性应用。仇恨/非仇恨言论的二元方法往往会迫使将内容指定为HS的理由。对上下文敏感的定性方法可以通过关注这些交流的间接特征来解决这一问题。结果应该引起社会科学和人文学科中采用自动情感分析以及分析HS和激进权利话语的研究人员的兴趣。
■HS的自动识别或调节不能解释间接意义的演变背景。这项研究举例说明了可以有效利用自动仇恨语音识别的过程。需要几个方法步骤才能获得有用的结果，技术定量处理和定性分析对于取得有意义的结果至关重要。关于alt-rightYouTube材料，主要挑战是间接框架。识别要求在更广泛的话语背景下进行定位，而对间接表达的适应使适度和压制在道德和法律上都不稳定。
■本文描述了自动HS检测的方法学挑战。案例研究涉及当代激进权利话语形成部分的数据。我们的目标是双重的。(1)概述了使用自动HS识别的跨学科混合方法方法。这一方面弥合了技术研究(如机器学习、深度学习,和自然语言处理，NLP)和传统的实证研究。关于另类权利话语和HS，我们问：（2）在流行的alt-rightYouTube视频中识别HS的挑战是什么？
■结果表明，有效和一致地识别HS通信需要进行定性干预，以避免任意或误导性应用。仇恨/非仇恨言论的二元方法往往会迫使将内容指定为HS的理由。对上下文敏感的定性方法可以通过关注这些交流的间接特征来解决这一问题。结果应该引起社会科学和人文学科中采用自动情感分析以及分析HS和激进权利话语的研究人员的兴趣。
■HS的自动识别或调节不能解释间接意义的演变背景。这项研究举例说明了可以有效利用自动仇恨语音识别的过程。需要几个方法步骤才能获得有用的结果，技术定量处理和定性分析对于取得有意义的结果至关重要。关于alt-rightYouTube材料，主要挑战是间接框架。识别要求在更广泛的话语背景下进行定位，而对间接表达的适应使适度和压制在道德和法律上都不稳定。
