背景:年龄歧视是健康的主要但经常被忽视的社会决定因素。事实上,学者们普遍认为,年龄歧视是最不被认可的不平等形式之一,尽管很少有人尝试证实这一说法。这是第一项量化致力于年龄歧视的话语数量的研究,性别歧视,Twitter上的种族主义具体来说,从2007年到2022年的15年间,我们依靠标签的使用来代表围绕每种形式的不平等进行讨论的频率。我们还确定了针对每种不平等形式引发Twitter活动高峰的关键事件。
方法:从过去的奖学金中提取与种族主义和性别歧视相关的标签。我们还采用了雪球采样方法,即我们使用Twitter的搜索功能查询主题标签,以识别其他主题标签。由于对与年龄歧视相关的标签进行了有限的研究,我们查询了倡导团体使用的标签,并采用了滚雪球抽样方法来编制其他相关标签。收集的推文(N=154,353,047)跨越15年,从2007年8月23日到2022年12月31日。
结果:从2007年到2022年,与种族主义相关的标签被使用得最多,其次是与性别歧视相关的标签和与年龄歧视相关的标签。与种族主义相关的标签(N=99,250,348)比与年龄歧视相关的标签(N=1,648,926)多60倍。与性别歧视相关的标签(N=38,933,113)比与年龄歧视相关的标签多24倍。与年龄歧视相关的推文的线性趋势增加(p<0.001),性别歧视(p<0.05),种族主义(p<0.05)达到显著性。种族主义和性别歧视事件经常引起广泛的公众愤慨。相反,年龄歧视的实例很少引起社交媒体活动的高峰。相反,这些峰值主要是在发布关于年龄歧视的报告等事件中观察到的,与衰老有关的会议,或国际老年人日等纪念活动。
结论:有必要加快行动以提高对年龄歧视的认识。确保有关年龄歧视的讨论不局限于学术界和政策界,可以开展宣传运动,对公众进行有关该问题及其负面伴随因素的教育。
BACKGROUND: Ageism is a major but oft-overlooked social determinant of health. In fact, it is widely accepted among scholars that ageism is one of the least acknowledged forms of inequality, although few empirical attempts have been made to substantiate this claim. This is the first study that quantifies the amount of discourse dedicated to ageism, sexism, and racism on Twitter. Specifically, we rely on the usage of hashtags as a proxy for the frequency of discussions surrounding each form of inequality over a 15-year period from 2007 to 2022. We also identify key events that triggered spikes in Twitter activity for each form of inequality.
METHODS: Hashtags related to racism and sexism were extracted from past scholarship. We also employed a snowball sampling method whereby we queried the hashtags using Twitter\'s search function to identify other hashtags. As limited research has been conducted on ageism-related hashtags, we queried hashtags utilized by advocacy groups and adopted a snowball sampling method to compile other relevant hashtags. Tweets collected (N = 154,353,047) spanned 15 years, from August 23, 2007 to December 31, 2022.
RESULTS: From 2007 to 2022, racism-related hashtags were used the most, followed by sexism-related hashtags and ageism-related hashtags. Racism-related hashtags (N = 99,250,348) were mentioned about 60 times more than ageism-related hashtags (N = 1,648,926). Sexism-related hashtags (N = 38,933,113) were mentioned 24 times more than ageism-related hashtags. The increasing linear trend of tweets associated with ageism (p < 0.001), sexism (p < 0.05), and racism (p < 0.05) reached significance. Incidents of racism and sexism often generated widespread public outrage. Conversely, instances of ageism rarely caused spikes in social media activity. Rather, these spikes were mainly observed during events such as the release of a report on ageism, a conference related to aging, or observances such as International Day of Older Persons.
CONCLUSIONS: There is a need to hasten moves to raise awareness of ageism. To ensure that discussions on ageism are not confined to academic and policy circles, advocacy campaigns could be held to educate the public on the issue and its negative concomitants.