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. © 2022 IEEE.

Towards Privacy-Preserving Spatial Distribution Crowdsensing: A Game Theoretic Approach

Acquiring the spatial distribution of users in mobile crowdsensing (MCS) brings many benefits to users (e.g., avoiding crowded areas during the COVID-19 pandemic). Although the leakage of users’location privacy has received a lot of research attention, existing works still ignore the rationality of users, resulting that users may not obtain satisfactory spatial distribution even if they provide true location information. To solve the problem, we employ game theory with incomplete information to model the interactions among users and seek an equilibrium state through learning approaches of the game. Specifically, we first model the service as a game in the satisfaction form and define the equilibrium for this service. Then, we design a LEFS algorithm for the privacy strategy learning of users when their satisfaction expectations are fixed, and further design LSRE that allows users to have dynamic satisfaction expectations. We theoretically analyze the convergence conditions and characteristics of the proposed algorithms, along with the privacy protection level obtained by our solution. We conduct extensive experiments to show the superiority and various performances of our proposal, which illustrates that our proposal can get more than 85% advantage in terms of the sensing distribution availability compared to the well-known differential privacy based solutions. IEEE

Epidemiological investigation and analysis of a cluster of COVID-19 in Taizhou, Zhejiang Province of China

Objective: To investigate and analyze the epidemiological characteristics of a cluster of coronavirus disease 2019 (COVID-19) spread by a super spreader in Taizhou, Zhejiang Province, and provide reference for prevention and control of COVID-19. Methods: The field epidemiological investigation was conducted to investigate the confirmed cases and close contacts;the data were analyzed with descriptive method. Chi-squared test was used to compare the differences of attack rates among close contacts. Results: A total of 23 epidemiological related cases were identified, including 20 confirmed cases and 3 asymptomatic infection cases. Thirteen (56.52%) were males, ten (43.48%) were females, and the median age was 51 years old with the range of 30 to 70 years old. The second generation case firstly developed symptoms on Jan 19th and was confirmed on Jan 30th, 2020.The first generation cases were from Wuhan and the last case was confirmed on Feb 3rd, 2020.The epidemic spread to the fifth generation with a total attack rate of 6.07% (21/346), among which the third generation case was a super spreader who directly transmitted to 12 subsequent cases with a significantly higher attack rate than other cases (27.27% vs. 2.98%, χ2=39.754, P<0.001). Conclusion: The expansion of the epidemic can be attributed to the lack of timely control of imported personnel from high risk regions, the lacking awareness of novel infectious diseases at the early stage of the epidemic and the appearance of the super spreader. To form a normalized prevention mechanism, it is necessary to improve the alertness of novel infectious diseases among medical staffs and the masses, implement prevention and control strategies in time. © 2021, Editorial Department of Fudan University Journal of Medical Sciences. All right reserved.