Analyzing and Identifying Teens' Stressful Periods and Stressor Events From a Microblog
Analyzing and Identifying Teens' Stressful Periods and Stressor Events From a Microblog
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从微博分析和识别青少年的压力期和压力事件
DOI：
10.1109/jbhi.2016.2586519
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发表时间：
2017-09-01
影响因子：
7.7
通讯作者：
Feng, Ling
中科院分区：
文献类型：
--
作者：
Li, Qi;Xue, Yuanyuan;Feng, Ling
青少年因心理压力导致的健康问题增多已引起全球关注。长期的压力若没有针对性的帮助和引导，会对青少年的健康成长产生负面影响，威胁到我们社会的未来发展。到目前为止，有关检测青少年心理压力的研究是从微博上的每条个人帖子中揭示的。然而，除了压力时刻之外，识别青少年的压力期以及引发每个压力期的压力源事件，对于从表象到本质地理解压力更为可取。在本文中，我们定义了从开放社交媒体微博中识别青少年压力期和压力源事件的问题。从青少年在学校压力事件期间的发帖行为的案例研究入手，我们通过腾讯微博（本文中简称为微博）上的一系列帖子，为压力源事件和有压力的发帖行为之间的相关性建立了一个基于泊松分布的概率模型。利用该模型，我们发现了青少年最大的压力期，并进一步提取了导致这些压力期的可能的压力源事件的细节。我们根据常见的压力维度和事件类型，将提取的压力源事件进行概括并以层级形式呈现。以一所高中的122个预定的与学习相关的压力事件作为事实依据，我们对2012年1月1日至2015年2月1日期间124名学生的帖子进行了测试，并获得了一些有希望的实验结果：（压力期：召回率0.761，准确率0.737，F值0.734）和（排名前三的压力源事件：召回率0.763，准确率0.756，F1值0.759）。提取出的最突出的压力源事件在自我认知领域，其次是学校生活领域。这符合青少年心理调查结果，即学校生活中的问题通常伴随着青少年的内心认知问题。与最先进的排名第一的个人生活事件检测方法相比，我们的压力源事件检测方法在准确率上高出13.72%，在召回率上高出19.18%，在F1值上高出16.50%，证明了我们所提出的框架的有效性。
Increased health problems among adolescents caused by psychological stress have aroused worldwide attention. Long-standing stress without targeted assistance and guidance negatively impacts the healthy growth of adolescents, threatening the future development of our society. So far, research focused on detecting adolescent psychological stress revealed from each individual post on microblogs. However, beyond stressful moments, identifying teens' stressful periods and stressor events that trigger each stressful period is more desirable to understand the stress from appearance to essence. In this paper, we define the problem of identifying teens' stressful periods and stressor events from the open social media microblog. Starting from a case study of adolescents' posting behaviors during stressful school events, we build a Poisson-based probability model for the correlation between stressor events and stressful posting behaviors through a series of posts on Tencent Weibo (referred to as the microblog throughout the paper). With the model, we discover teens' maximal stressful periods and further extract details of possible stressor events that cause the stressful periods. We generalize and present the extracted stressor events in a hierarchy based on common stress dimensions and event types. Taking 122 scheduled stressful study-related events in a high school as the ground truth, we test the approach on 124 students' posts from January 1,2012 to February 1,2015 and obtain some promising experimental results: (stressful periods: recall 0.761, precision 0.737, and F-measure 0.734) and (top-3 stressor events: recall 0.763, precision 0.756, and F-1-measure 0.759). The most prominent stressor events extracted are in the self-cognition domain, followed by the school life domain. This conforms to the adolescent psychological investigation result that problems in school life usually accompanied with teens' inner cognition problems. Compared with the state-of-the-art top-1 personal life event detection approach, our stressor event detection method is 13.72% higher in precision, 19.18% higher in recall, and 16.50% higher in F-1- measure, demonstrating the effectiveness of our proposed framework.