Using Natural Language Processing to Explore Mental Health Insights From UK Tweets During the COVID-19 Pandemic: Infodemiology Study.
Using Natural Language Processing to Explore Mental Health Insights From UK Tweets During the COVID-19 Pandemic: Infodemiology Study.
复制标题
DOI：
10.2196/32449
复制
发表时间：
2022-01
期刊：
影响因子：
--
通讯作者：
中科院分区：
文献类型：
--
作者：
有必要考虑软情报的价值，利用可访问的自然语言处理（NLP）工具，作为分析证据的来源，以支持公共卫生研究成果和决策。本研究的目的是探讨使用NLP分析软智能的价值。作为一个案例研究，我们选择并使用了一个商业化的NLP平台来识别、收集和询问大量与2019冠状病毒病大流行期间心理健康相关的英国推文。我们制定了一项搜索策略，包括与心理健康、COVID-19和封锁限制相关的术语列表，以便在24周内通过Twitter的高级搜索应用程序编程接口前瞻性地整理相关推文。我们部署了一个现成的商业NLP平台，以探索英国各地的推文频率和情绪，并确定讨论的关键主题。一系列关键字过滤器被用来清理检索到的初始数据，并被设置为跟踪特定的心理健康问题。所有经过整理的推文都是匿名的。我们识别并分析了2020年7月23日至2021年1月6日期间英国用户账户发布的286，902条推文。平均情绪得分为50%，表明在研究期间所有推文的总体情绪都是中性的。数量（在12，622和51，340之间）和情绪（在25%和49%之间）的主要波动似乎与任何地方和/或国家社交距离措施的关键变化一致。围绕心理健康的推文两极分化，讨论了积极和消极的情绪。研究期间持续讨论的主要话题包括疫情对人们心理健康的影响（积极和消极）、对封锁的恐惧和焦虑，以及对政府的愤怒和不信任。使用NLP平台，我们能够快速挖掘和分析来自英国推文的新兴健康相关见解，以了解大流行病如何影响人们的心理健康和福祉。这种类型的实时分析证据可以作为一个有用的情报来源，机构，地方领导人和医疗保健决策者可以从中借鉴，特别是在健康危机期间。
There is need to consider the value of soft intelligence, leveraged using accessible natural language processing (NLP) tools, as a source of analyzed evidence to support public health research outputs and decision-making. The aim of this study was to explore the value of soft intelligence analyzed using NLP. As a case study, we selected and used a commercially available NLP platform to identify, collect, and interrogate a large collection of UK tweets relating to mental health during the COVID-19 pandemic. A search strategy comprised of a list of terms related to mental health, COVID-19, and lockdown restrictions was developed to prospectively collate relevant tweets via Twitter’s advanced search application programming interface over a 24-week period. We deployed a readily and commercially available NLP platform to explore tweet frequency and sentiment across the United Kingdom and identify key topics of discussion. A series of keyword filters were used to clean the initial data retrieved and also set up to track specific mental health problems. All collated tweets were anonymized. We identified and analyzed 286,902 tweets posted from UK user accounts from July 23, 2020 to January 6, 2021. The average sentiment score was 50%, suggesting overall neutral sentiment across all tweets over the study period. Major fluctuations in volume (between 12,622 and 51,340) and sentiment (between 25% and 49%) appeared to coincide with key changes to any local and/or national social distancing measures. Tweets around mental health were polarizing, discussed with both positive and negative sentiment. Key topics of consistent discussion over the study period included the impact of the pandemic on people’s mental health (both positively and negatively), fear and anxiety over lockdowns, and anger and mistrust toward the government. Using an NLP platform, we were able to rapidly mine and analyze emerging health-related insights from UK tweets into how the pandemic may be impacting people’s mental health and well-being. This type of real-time analyzed evidence could act as a useful intelligence source that agencies, local leaders, and health care decision makers can potentially draw from, particularly during a health crisis.