Tweeting Negative Emotion: An Investigation of Twitter Data in the Aftermath of Violence on College Campuses
Tweeting Negative Emotion: An Investigation of Twitter Data in the Aftermath of Violence on College Campuses
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DOI：
10.1037/met0000099
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发表时间：
2016-12-01
影响因子：
7
通讯作者：
Silver, Roxane Cohen
中科院分区：
文献类型：
--
作者：
Jones, Nickolas M.;Wojcik, Sean P.;Silver, Roxane Cohen
研究受创伤事件影响的社区往往成本高昂，需要在灾难发生时迅速采取行动进入现场，而且对一些受创伤的受访者来说可能是侵入性的。通常，个体在创伤事件后进行研究，没有基线数据来比较他们的灾后反应。考虑到这些挑战，我们在2014年至2015年期间使用了3个案例研究中的纵向Twitter数据，以检查加州Isla Vista、亚利桑那州Flagstaff和俄勒冈州Roseburg社区大学校园附近或校园内的暴力事件的影响，并与对照社区进行比较。为了确定可能生活在每个社区的用户，我们寻找了这些社区的本地Twitter账户，并下载了他们各自追随者的推文。然后使用计算机文本分析方法（语言调查和单词计数，LIWC）对推文进行编码，以确定是否存在与事件相关的负面情绪词。在案例研究1中，我们观察到在大规模暴力事件发生后，抽样的追随者中事件后负面情绪表达的增加，并显示了反应模式如何根据审查的时间框架出现不同。在案例研究2中，我们在案例研究1的控制组用户中复制了在该社区校园枪击事件造成1名学生死亡后的结果模式。在案例研究3中，我们在可能生活在受大规模枪击事件影响的社区的另一组Twitter用户中复制了这种模式。我们讨论了使用Twitter数据进行创伤相关研究，并为有兴趣使用Twitter回答自己在该领域的研究问题的研究人员提供指导。
Studying communities impacted by traumatic events is often costly, requires swift action to enter the field when disaster strikes, and may be invasive for some traumatized respondents. Typically, individuals are studied after the traumatic event with no baseline data against which to compare their postdisaster responses. Given these challenges, we used longitudinal Twitter data across 3 case studies to examine the impact of violence near or on college campuses in the communities of Isla Vista, CA, Flagstaff, AZ, and Roseburg, OR, compared with control communities, between 2014 and 2015. To identify users likely to live in each community, we sought Twitter accounts local to those communities and downloaded tweets of their respective followers. Tweets were then coded for the presence of event-related negative emotion words using a computerized text analysis method (Linguistic Inquiry and Word Count, LIWC). In Case Study 1, we observed an increase in postevent negative emotion expression among sampled followers after mass violence, and show how patterns of response appear differently based on the timeframe under scrutiny. In Case Study 2, we replicate the pattern of results among users in the control group from Case Study 1 after a campus shooting in that community killed 1 student. In Case Study 3, we replicate this pattern in another group of Twitter users likely to live in a community affected by a mass shooting. We discuss conducting trauma-related research using Twitter data and provide guidance to researchers interested in using Twitter to answer their own research questions in this domain.