京都大学 · 情報科学
Qian Niu教授の研究室は、公衆衛生とデジタル技術の交差点に焦点を当てており、特にSNS上での感染症に関する世論やワクチンへの態度を分析することで、政策立案に役立つ知見を提供しています。COVID-19の影響下におけるワクチン接種の意思決定要因や、テレワークに伴う健康リスクの解明にも取り組んでいます。また、大規模言語モデル(LLM)と人間の認知プロセスの類似性を分析し、AIと認知科学の統合的アプローチを模索しています。
Figures are computed from collected data and may differ slightly.
Health issues associated with teleworking should be given adequate attention.
Negative sentiment toward vaccines dominated positive sentiment in Japan, and the concerns about side effects might have outweighed fears of infection at the beginning of the vaccination process. Topic modeling on negative tweets indicated that the government and policy makers should take prompt actions in building a safe and convenient vaccine reservation and rollout system, which requires both flexibility of the medical care system and the acceleration of digitalization in Japan. The public sh
Abstract Background The pandemic of COVID-19 is causing a crisis in public health, food systems, and employment. Vaccination is considered as one of the most effective ways for containing the pandemic, but widespread vaccine hesitation on social media may curtail uptake progress. Fully comprehending public sentiment towards the COVID-19 vaccine is critical to building confidence on the vaccines and achieving herd immunity, especially in Japan with inadequate vaccine confidence. Objective This st
Our work indicates that awareness of the danger of COVID-19 might increase the willingness to get vaccinated. With a sufficient vaccine supply, effective delivery of vaccine reservation information may be an important factor for people to get vaccinated. We did not find evidence for increased vaccine confidence in Japan during the period of our study. We recommend policy makers to share accurate and prompt information about the infectious diseases and vaccination and to make efforts on smoother
This comprehensive review explores the intersection of Large Language Models (LLMs) and cognitive science, examining similarities and differences between LLMs and human cognitive processes. We analyze methods for evaluating LLMs cognitive abilities and discuss their potential as cognitive models. The review covers applications of LLMs in various cognitive fields, highlighting insights gained for cognitive science research. We assess cognitive biases and limitations of LLMs, along with proposed m
The significant terms for the explanation of HFMD cases before and during COVID-19 were different. Awareness of HFMD infections in Japan may have improved during the COVID-19 pandemic. Continuous monitoring is important to promote public health and prevent resurgence. The public interest reflected in information-seeking behavior can be helpful for public health surveillance.
Abstract Background The global public health and socioeconomic impacts of coronavirus disease 2019 (COVID-19) have been substantial, making herd immunity by COVID-19 vaccination an important factor for protecting people and retrieving the economy. Among all the countries, Japan became one of the countries with the highest COVID-19 vaccination rate in several months, although the vaccine confidence in Japan is the lowest worldwide. Objective We attempted to find the reasons for the rapid coronavi
Abstract Background The World Health Organization (WHO) established a global strategy to accelerate the elimination of cervical cancer in 2020; fortunately, it can be effectively controlled or prevented by the human papillomavirus (HPV) vaccine. Nevertheless, Japan has been experiencing an HPV vaccine crisis since 2013, with a vaccination rate of less than 1% lasting for seven years. We examined trend changes related to cervical cancer topics and assessed the feasibility and limitations of Googl
Abstract Background Despite the reinstatement of proactive human papillomavirus (HPV) vaccine recommendations in 2022, Japan continues to face persistently low HPV vaccination rates, posing significant public health challenges. Misinformation, complacency, and accessibility issues have been identified as key factors undermining vaccine uptake. Objective This study aims to understand how factors such as misinformation, public health events, and attitudes toward other vaccines, like COVID-19, infl
Abstract Background COVID-19 pandemic affected common disease infections, while the impact on hand, foot, and mouth disease (HFMD) is unclear. Google Trends data is beneficial in approximately real-time statistics and easily accessed, expecting to be used for infection explanation from information-seeking behavior perspectives. We aimed to explain HFMD cases before and during COVID-19 using Google Trends data. Methods HFMD cases were obtained from the National Institute of Infectious Disease, an
Additional file 2. Translation tables and interpretations.
Additional file 1. Counts of tokens calculated using "HFMD" related Tweets.
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