A case study of using natural language processing to extract consumer insights from tweets in American cities for public health crises.

BACKGROUND: The COVID-19 pandemic was a "wake up" call for public health agencies. Often, these agencies are ill-prepared to communicate with target audiences clearly and effectively for community-level activations and safety operations. The obstacle is a lack of data-driven approaches to obtaining insights from local community stakeholders. Thus, this study suggests a focus on listening at local levels given the abundance of geo-marked data and presents a methodological solution to extracting consumer insights from unstructured text data for health communication. METHODS: This study demonstrates how to combine human and Natural Language Processing (NLP) machine analyses to reliably extract meaningful consumer insights from tweets about COVID and the vaccine. This case study employed Latent Dirichlet Allocation (LDA) topic modeling, Bidirectional Encoder Representations from Transformers (BERT) emotion analysis, and human textual analysis and examined 180,128 tweets scraped by Twitter Application Programming Interface's (API) keyword function from January 2020 to June 2021. The samples came from four medium-sized American cities with larger populations of people of color. RESULTS: The NLP method discovered four topic trends: "COVID Vaccines," "Politics," "Mitigation Measures," and "Community/Local Issues," and emotion changes over time. The human textual analysis profiled the discussions in the selected four markets to add some depth to our understanding of the uniqueness of the different challenges experienced. CONCLUSIONS: This study ultimately demonstrates that our method used here could efficiently reduce a large amount of community feedback (e.g., tweets, social media data) by NLP and ensure contextualization and richness with human interpretation. Recommendations on communicating vaccination are offered based on the findings: (1) the strategic objective should be empowering the public; (2) the message should have local relevance; and, (3) communication needs to be timely.

COVID-19 vaccine hesitancy and short-term and long-term intentions among unvaccinated young adults: a mixed-method approach.

BACKGROUND: Only 63.8% of Americans who are 18-to-24-years-old have been fully vaccinated for COVID-19 as of June 1, 2022. The Grand Forks County, North Dakota is facing a similar challenge. As of June 2022, 47% of individuals in the 19-to-29-year-old age group are vaccinated. Focusing on unvaccinated individuals in their 20s, Study 1 aims to understand the ways in which receiving COVID-19 vaccines is construed using qualitative interviews; and Study 2 compares the predictors of short-term vaccination intention (i.e., next month) with those of long-term vaccination intention (i.e., three to 5 years) using an online survey. METHODS: For Study 1, we conducted five focus groups and four in-depth interviews via Zoom with a total of 26 unvaccinated individuals in their 20s living in the Grand Forks County. Constant comparison process was used to categorize data into themes and to recognize characteristics of the identified themes. The aim was to develop themes and associated characteristics. For Study 2, we conducted an online survey with a convenience sample of 526 unvaccinated individuals. Logistic regression estimated odds ratios (OR) and 95% confidence intervals (CI) for associations between attitudes, perceptions, and beliefs in misinformation and short-term and long-term vaccination intentions, accounting for demographics and socioeconomic status. RESULTS: In Study 1, two themes were identified: feelings of uncertainty sparked by profits and monetization and navigating the fear of the unknown. In Study 2, an increase in the confidence of COVID-19 vaccines showed significantly higher odds of short-term intention (OR = 2.658, 95%CI 1.770, 3.990) and long-term intention (OR = 1.568, 95% CI 1.105, 2.226). Believing in misinformation had significantly lower odds of short-term intention (OR = 0.712, 95%CI 0.513, 0.990), while more positive attitudes (OR = 1.439, 95% CI 1.024, 2.024), stronger preference in calculating the benefits of COVID-19 vaccines (OR = 2.108, 95% CI 1.541, 2.882), and greater perceived susceptibility (OR = 1.471, 95% CI 1.045, 2.070) to and severity of contracting COVID-19 (OR = 1.362, 95% CI 1.020, 1.820) were significantly associated with higher odds of long-term intention. CONCLUSIONS: Short-term and long-term intentions were predicted differently. Instilling strong confidence in COVID-19 vaccines should increase both short-term and long-term intentions.