Journalists are most likely to receive abuse: analysing online abuse of UK public figures across sport, politics, and journalism on Twitter.

IF 2.5 2区 计算机科学 Q1 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS
EPJ Data Science Pub Date : 2025-01-01 Epub Date: 2025-05-23 DOI:10.1140/epjds/s13688-025-00556-8
Liam Burke-Moore, Angus R Williams, Jonathan Bright
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引用次数: 0

Abstract

Engaging with online social media platforms is an important part of life as a public figure in modern society, enabling connection with broad audiences and providing a platform for spreading ideas. However, public figures are often disproportionate recipients of hate and abuse on these platforms, degrading public discourse. While significant research on abuse received by groups such as politicians and journalists exists, little has been done to understand the differences in the dynamics of abuse across different groups of public figures, systematically and at scale. To address this, we present analysis of a novel dataset of 45.5M tweets targeted at 4602 UK public figures across 3 domains (members of parliament, footballers, journalists), labelled using fine-tuned transformer-based language models. We find that MPs receive more abuse in absolute terms, but that journalists are most likely to receive abuse after controlling for other factors. We show that abuse is unevenly distributed in all groups, with a small number of individuals receiving the majority of abuse, and that for some groups, abuse is more temporally uneven, being driven by specific events, particularly for footballers. We also find that a more prominent online presence and being male are indicative of higher levels of abuse across all 3 domains.

记者最有可能受到虐待:分析英国公众人物在推特上对体育、政治和新闻的在线虐待。
参与在线社交媒体平台是现代社会公众人物生活的重要组成部分,可以与广泛的受众建立联系,并提供传播思想的平台。然而,公众人物往往是这些平台上仇恨和辱骂的不成比例的接受者,这降低了公共话语的尊严。虽然对政治家和记者等群体所遭受的虐待进行了大量研究,但很少有人去了解不同公众人物群体在系统和规模上的虐待动态差异。为了解决这个问题,我们对一个新的数据集进行了分析,该数据集包含4550万条推文,针对3个领域(国会议员、足球运动员、记者)的4602名英国公众人物,并使用微调的基于转换器的语言模型进行了标记。我们发现,从绝对值来看,国会议员受到的虐待更多,但在控制了其他因素后,记者最可能受到虐待。我们发现,虐待在所有群体中分布不均,少数人受到大多数虐待,对某些群体来说,虐待在时间上更不均匀,这是由特定事件驱动的,尤其是对足球运动员而言。我们还发现,男性在网络上的表现越突出,在这三个领域受到的虐待程度越高。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
EPJ Data Science
EPJ Data Science MATHEMATICS, INTERDISCIPLINARY APPLICATIONS -
CiteScore
6.10
自引率
5.60%
发文量
53
审稿时长
13 weeks
期刊介绍: EPJ Data Science covers a broad range of research areas and applications and particularly encourages contributions from techno-socio-economic systems, where it comprises those research lines that now regard the digital “tracks” of human beings as first-order objects for scientific investigation. Topics include, but are not limited to, human behavior, social interaction (including animal societies), economic and financial systems, management and business networks, socio-technical infrastructure, health and environmental systems, the science of science, as well as general risk and crisis scenario forecasting up to and including policy advice.
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