Tracking COVID-19 Discourse on Twitter in North America: Infodemiology Study Using Topic Modeling and Aspect-Based Sentiment Analysis.
Tracking COVID-19 Discourse on Twitter in North America: Infodemiology Study Using Topic Modeling and Aspect-Based Sentiment Analysis.
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DOI：
10.2196/25431
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发表时间：
2021-02-10
影响因子：
7.4
通讯作者：
Janjua NZ
中科院分区：
文献类型：
--
作者：
Jang H;Rempel E;Roth D;Carenini G;Janjua NZ
社交媒体是我们了解人们对社会问题的反应的丰富来源。由于 COVID-19 已经影响了人们的生活，因此必须了解人们对公共卫生干预措施的反应并了解他们的担忧。我们的目标是调查北美地区（尤其是加拿大）人们对 COVID-19 的反应和担忧。我们使用主题建模和基于方面的情感分析 (ABSA) 分析了与 COVID-19 相关的推文，并与公共卫生专家一起解释了结果。为了深入了解针对 COVID-19 的特定公共卫生干预措施的有效性，我们将讨论的主题的时间表与实施干预措施的时间进行了比较，并在我们的分析中协同纳入了人们对 COVID-19 相关方面的情绪信息。此外，为了进一步调查反亚裔种族主义，我们比较了亚裔和加拿大人的情绪时间线。主题建模确定了 20 个主题，公共卫生专家根据每个主题的排名最高的单词和代表性推文提供了对主题的解释。解读和时间线分析表明，发现的主题及其趋势与社交距离、边境限制、洗手、呆在家里和戴口罩等公共卫生宣传和干预措施高度相关。使用ABSA和人机交互对数据进行训练后，我们获得了545个方面术语（例如“疫苗”、“经济”和“口罩”）和60个观点术语，例如“传染性”（负面）和“专业”（正面），用于推断公共卫生专家选择的20个关键方面的情绪。结果显示，与整体疫情、错误信息和亚洲人相关的负面情绪，以及与身体距离相关的积极情绪。在领域专家的参与下使用自然语言处理技术进行分析可以为公共卫生提供有用的信息。这项研究首次使用主题建模和人机交互特定领域 ABSA 来分析加拿大与美国的 COVID-19 相关推文。此类信息可以帮助公共卫生机构了解公众的担忧以及哪些公共卫生信息在使用 Twitter 的人群中引起共鸣，这有助于公共卫生机构设计新干预措施的政策。
Social media is a rich source where we can learn about people’s reactions to social issues. As COVID-19 has impacted people’s lives, it is essential to capture how people react to public health interventions and understand their concerns. We aim to investigate people’s reactions and concerns about COVID-19 in North America, especially in Canada. We analyzed COVID-19–related tweets using topic modeling and aspect-based sentiment analysis (ABSA), and interpreted the results with public health experts. To generate insights on the effectiveness of specific public health interventions for COVID-19, we compared timelines of topics discussed with the timing of implementation of interventions, synergistically including information on people’s sentiment about COVID-19–related aspects in our analysis. In addition, to further investigate anti-Asian racism, we compared timelines of sentiments for Asians and Canadians. Topic modeling identified 20 topics, and public health experts provided interpretations of the topics based on top-ranked words and representative tweets for each topic. The interpretation and timeline analysis showed that the discovered topics and their trend are highly related to public health promotions and interventions such as physical distancing, border restrictions, handwashing, staying home, and face coverings. After training the data using ABSA with human-in-the-loop, we obtained 545 aspect terms (eg, “vaccines,” “economy,” and “masks”) and 60 opinion terms such as “infectious” (negative) and “professional” (positive), which were used for inference of sentiments of 20 key aspects selected by public health experts. The results showed negative sentiments related to the overall outbreak, misinformation and Asians, and positive sentiments related to physical distancing. Analyses using natural language processing techniques with domain expert involvement can produce useful information for public health. This study is the first to analyze COVID-19–related tweets in Canada in comparison with tweets in the United States by using topic modeling and human-in-the-loop domain-specific ABSA. This kind of information could help public health agencies to understand public concerns as well as what public health messages are resonating in our populations who use Twitter, which can be helpful for public health agencies when designing a policy for new interventions.
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影响因子：
3.7
作者：
Gore RJ;Diallo S;Padilla J
通讯作者：
Padilla J
在Twitter上对旅游景点情绪的时间和时空调查。
影响因子：
3.7
作者：
Padilla JJ;Kavak H;Lynch CJ;Gore RJ;Diallo SY
通讯作者：
Diallo SY
DOI：
10.18653/v1/d16-1057
发表时间：
2016-11
期刊：
Proceedings of the Conference on Empirical Methods in Natural Language Processing. Conference on Empirical Methods in Natural Language Processing
影响因子：
--
作者：
Hamilton WL;Clark K;Leskovec J;Jurafsky D
通讯作者：
Jurafsky D
影响因子：
9
作者：
Dalrymple, Kajsa E.;Young, Rachel;Tully, Melissa
通讯作者：
Tully, Melissa
DOI：
10.1073/pnas.0307752101
发表时间：
2004-04-06
影响因子：
11.1
作者：
Griffiths, TL;Steyvers, M
通讯作者：
Steyvers, M