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首页> 外文会议>Workshop on Natural Language Processing and Computational Social Sciences >Social media data as a lens onto care-seeking behavior among women veterans of the US armed forces
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Social media data as a lens onto care-seeking behavior among women veterans of the US armed forces

机译：社交媒体数据作为镜头在美国武装部队女性退伍军人中寻求追求行为

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In this article, we examine social media data as a lens onto support-seeking among women veterans of the US armed forces. Social media data hold a great deal of promise as a source of information on needs and support-seeking among individuals who are excluded from or systematically prevented from accessing clinical or other institutions ostensibly designed to support them. We apply natural language processing (NLP) techniques to more than 3 million Tweets collected from 20,000 Twitter users. We find evidence that women veterans are more likely to use social media to seek social and community engagement and to discuss mental health and veterans' issues significantly more frequently than their male counterparts. By contrast, male veterans tend to use social media to amplify political ideologies or to engage in partisan debate. Our results have implications for how organizations can provide outreach and services to this uniquely vulnerable population, and illustrate the utility of non-traditional observational data sources such as social media to understand the needs of marginalized groups.
机译：在本文中，我们将社交媒体数据作为镜头作为镜头，在美国武装部队的女性退伍军人之间寻求支持。社交媒体数据作为需要和寻求支持的信息来源，这些数据持有来自或系统地被排除在外或系统地防止的个人中寻求临床或其他旨在支持它们的其他机构。我们将自然语言处理（NLP）技术应用于从20,000名推特用户收集的超过300万推文。我们发现证据表明，妇女退伍军人更有可能使用社交媒体来寻求社会和社区参与，并比男性同行更频繁地讨论心理健康和退伍军人的问题。相比之下，雄性退伍军人倾向于使用社交媒体来放大政治意识形态或从事党派辩论。我们的结果对组织如何向这种独特的弱势群体提供外联和服务的影响，并说明了非传统观测数据来源的效用，例如社交媒体，以了解边缘化群体的需求。

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