Stress-Coping Tweets Acquisition: A Two-phase Bootstrapping Method on Patterns and Semantic Features

Stress is integral to biological survival. However, without an appropriate coping response, high stress levels and long-term stressful situations may lead to negative mental health outcomes. Since the COVID-19 pandemic, remote assessment of mental health has become imperative. The majority of past studies focused on detecting users' stress levels rather than coping responses using social media. Because of the diversity of human expression and because people do not usually express stress and the corresponding coping response simultaneously, it is challenging to extract users' tweets about their coping responses to stressful events from their daily tweets. Consequently, there are two goals being pursued in this study: to anchor users' stress statuses and to detect their stress responses based on the existing stressful conditions. In order to accomplish these goals, we propose a framework that consists of two phases: the construction of stress dataset and the extraction of coping responses. Since the stressed users' data are lacking, the first phase is to construct a stress dataset based on stress-related hashtags, personal pronouns, and emotion recognition. In addition, to ensure the collection of enough tweets to observe the coping responses of stressed users, we broadened the survey's scope by collecting all tweets from the same user. In the second phase, stress-coping tweets were extracted by utilizing bootstrapping-based patterns and semantic features. The bootstrapping method was used to enrich word patterns for text expression and the semantic feature to assess the meaning of sentences. The collected data included the tweets of the stressed users identified in Phase 1 and the various coping responses from Phase 2 can contribute to developing a tool for the remote assessment of mental health. The experimental results show that our two-phase method outperforms the baseline and can help improve the efficiency of extracting stress-coping tweets.