Social Media Signals for Post-traumatic Stress and Anxiety in Crisis-Inflicted Communities

描述（由申请人提供）：每年有数百万人经历创伤性经历。虽然急性痛苦是对创伤的正常反应，但一小部分经历过创伤经历的人在创伤后很长时间内仍会表现出严重的压力反应。创伤后反应可能包括侵入性记忆、过度警觉的觉醒、注意力不集中、抑郁、与他人的情感疏离，以及从提供意义和自我实现的生活方面脱离。在功能评估中，这些复发性反应严重损害了个人、人际和职业功能。尽管有这些后果，但创伤后应激障碍的报道不足，而且在许多情况下完全没有被发现。寻求帮助的障碍包括：对易感症状缺乏了解，无法获得适当的补救/预防服务，害怕精神疾病的社会耻辱，缺乏社会支持，或认为情绪变化是危机后生活压倒性性质的一部分。近年来，许多危机康复工作已经认识到这些挑战，并将获得心理健康服务纳入其干预和支持计划。然而，治疗工作，通常包括药物，治疗，或两者兼而有之，早期干预更成功；事实上，随着病情的延长，完全康复的可能性也在下降。Twitter和Facebook等社交媒体在危机局势中发挥着越来越重要的作用：聚集和传播信息，同时提供反思和讨论集体悲伤和创伤的机会。因此，这些平台提供了一个机会，可以通过研究遭受危机的人群的情感反应，来调查是否可以在宏观尺度上检测到创伤后压力和焦虑。由于社交媒体的档案记录，对社会危机和创伤性事件的反应可以被纵向追踪。我们建议从这些平台中挖掘行为数据，以更好地了解人们对危机的反应范围。通过应用文本和社会网络分析的最先进技术，我们将超越危机行为的人口水平估计，以衡量特定危机社区的社会，心理，情感和语言属性，以及这些行为属性与危机背景下关键心理和公共卫生结果的关系。具体而言，我们将完成以下研究目标：1。设计数据挖掘技术，可以智能地过滤社交媒体帖子中与危机相关的内容，并结合可以提取人口代表性样本的统计校正方法。2. 使用语言信号和社交网络元数据来识别危机人口中的关键社区。3. 从这些社区的活动中制定行为措施，以反映他们因危机情况而患应激障碍的风险程度。这将包括自动扩展和改进现有技术的措施，以匹配社区特定的语言和方言，这些语言和方言在社交媒体写作中可能会有很大差异，同时也会考虑到危机事件的独特背景。4. 对陷入危机的社区的行为进行纵向研究，找出特别高风险群体的因素。如果成功，这项研究将(a)将与创伤后应激障碍的加剧（甚至易感性）相关的变量置于重要位置，(b)使新机制能够以近乎实时的方式识别风险社区，(c)为目前围绕创伤诊断的研究提供补充视角，这些研究通常依赖于实验室研究和调查。实际上，在危机背景下，社交媒体上表现出来的行为与焦虑和创伤后应激症状之间的潜在联系，也可以增强传统努力，提供有价值的干预措施，作为灾难应对的一部分。这项工作是与佐治亚理工学院交互式计算学院的计算语言学家和教师雅各布·爱森斯坦博士的密切合作，雅各布·爱森斯坦博士是发现语言数据中潜在的社会关系的专家，詹姆斯·佩内贝克博士是德克萨斯大学奥斯汀分校心理学系的社会心理学家和教师，他的专长是语言线索的心理解释领域。

DESCRIPTION (provided by applicant): Millions of people undergo traumatic experiences annually. While acute distress is a normative response to trauma, a small percent of the people who have undergone traumatic experiences continue to exhibit severe stress reactions long after the trauma. The posttraumatic reactions may include intrusive memories, hypervig- ilant arousal, impaired concentration, depression, emotional detachment from others, and disengagement from aspects of life that provide meaning and self-fulfillment. In functional assessments these recurrent reactions seriously impair intrapersonal, interpersonal, and occupational functioning. Despite these ramifications, post-traumatic stress disorders are under-reported, and in many cases go completely undetected. Barriers to help-seeking include: lack of knowledge about predisposition symptoms, the unavailability of appropriate remedial/prevention services, the fear of the social stigma of mental illness, lack of social support, or assumptions that the mood changes are a part of the overwhelming nature of post-crisis life. In recent times, many crisis rehabilitation efforts have recognized these challenges, and have incorporated access to mental health services in their intervention and support programs. However, treatment efforts, that typically comprise medication, therapy, or both, are more successful with early intervention; in fact, the likelihood of achieving full recovery decline as the illness lengthens. Social media such as Twitter and Facebook are increasingly serving an important role in crisis situations: aggregating and disseminating information, while providing opportunities for reflection and discussion of collective grief and trauma. These platforms thus provide an opportunity to investigate if post-traumatic stress and anxiety can be detected on a macro scale by studying the affective responses of crisis-inflicted populations. Due to social media's archival record, reactions to societal crisis and traumatic happenings can be tracked longitudinally. We propose to mine behavioral data from these platforms to better understand the range of responses of people to crises. By applying state-of-the-art techniques from text and social network analytics, we will go be- yond population-level estimates of crisis behavior, to gauge the social, psychological, emotional, and linguistic attributes of specific crisis-laden communities, and the relationship of these behavioral attributes to key mental and public health outcomes in a crisis context. Specifically, we will accomplish the following research aims: 1. Design data mining techniques that can intelligently filter social media posts for crisis-relevant content, incorporating statistical correction methods that can extract population representative samples. 2. Use linguistic signals and social network metadata to identify key communities in the crisis-laden population. 3. Develop behavioral measures from the activities of these communities that reflect the extent of their risk to stress disorders from the crisis situation. Ths will include measures that automatically expand and refine existing technologies to match community-specific language and dialects, which can vary dramatically in social media writing, as well as factor in the unique context of crisis events. 4. Perform a longitudinal study of the behavior of crisis-embroiled communities, identifying the factors that indicate especially high-ris groups. If successful, this research will (a) bring to the fore variables related to the exacerbaton of (or even predisposition to) PTSD, (b) enable new mechanisms to identify at-risk communities in a near real-time fashion, and (c) lend a complementary perspective on current research around trauma diagnosis, which relies typically on laboratory studies and surveys. On a practical note, a potential link between behavior manifested in social media in the context of a crisis, and anxiety and post-traumatic stress symptomatology can also augment traditional efforts in providing valuable interventions as a part of disaster response. This work is a close collaboration with computational linguist and faculty of School of Interactive Computing at Georgia Institute of Technology, Dr. Jacob Eisenstein, who is an expert in the discovery of social relationships latent in linguistic data, and Dr. James Pennebaker, a social psychologist and faculty in the Department of Psychology at the University of Texas, Austin, whose expertise is in the area of psychological interpretation of language cues.