理论驱动的自杀推文自动内容分析：使用LDA数据集的基于典型性的分类
Theory-Driven Automated Content Analysis of Suicidal Tweets : Using Typicality-Based Classification for LDA Dataset
摘要：
这项研究提供了一个方法框架，计算机分类推文根据变量的计划行为理论。我们提出了一个自动文本分析的顺序过程，它结合了监督的方法和无监督的方法，以使计算机检测TPB变量在每个鸣叫。我们进行了潜在狄利克雷分配（LDA），最近邻，然后评估新标记的推文的“典型性”，以预测分类边界。此外，本研究报告的结果，从内容分析的自杀相关的推文，确定在Twitter的信息环境的特点。与现存的关于自杀报道的文献一致，研究结果表明，推文通常包含提示自杀行为控制的信息，而很少提供有关自杀的威慑信息。最后，我们强调方法的进步和实证理论研究的影响。
英文摘要：
This study provides a methodological framework for the computer to classify tweets according to variables of the Theory of Planned Behavior. We present a sequential process of automated text analysis which combined supervised approach and unsupervised approach in order to make the computer to detect one of TPB variables in each tweet. We conducted Latent Dirichlet Allocation (LDA), Nearest Neighbor, and then assessed "typicality" of newly labeled tweets in order to predict classification boundary. Furthermore, this study reports findings from a content analysis of suicide-related tweets which identify traits of information environment in Twitter. Consistent with extant literature about suicide coverage, the findings demonstrate that tweets often contain information which prompt perceived behavior control of committing suicide, while rarely provided deterring information on suicide. We conclude by highlighting implications for methodological advances and empirical theory studies.
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