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Anatomy into the battle of supporting or opposing reopening amid the COVID-19 pandemic on Twitter: A temporal and spatial analysis.

ABSTRACT: Reopening amid the COVID-19 pandemic has triggered a battle on social media. The supporters perceived that the lockdown policy could damage the economy and exacerbate social inequality. By contrast, the opponents believed it was necessary to contain the spread and ensure a safe environment for recovery. Anatomy into the battle is of importance to address public concerns, beliefs, and values, thereby enabling policymakers to determine the appropriate solutions to implement reopening policy. To this end, we investigated over 1.5 million related Twitter postings from April 17 to May 30, 2020. With the aid of natural language processing (NLP) techniques and machine learning classifiers, we classified each tweet into either a "supporting" or "opposing" class and then investigated the public perception from temporal and spatial perspectives. From the temporal dimension, we found that both political and scientific news that were extensively discussed on Twitter led to the perception of opposing reopening. Further, being the first mover with full reopen adversely affected the public reaction to reopening policy, while being the follower or late mover resulted in positive responses. From the spatial dimension, the correlation and regression analyses suggest that the state-level perception was very likely to be associated with political affiliation and health value.

SUBMITTER: Li L

PROVIDER: S-EPMC8277023 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundStarting in 2019 policies restricting the availability of flavored e-cigarette products were proposed or implemented in the United States to curb vaping by youth. People took to Twitter to voice their opposition, referencing the phrase "Flavors Save Lives." This study documented the emerging themes pertaining to "Flavors Saves Lives" over a 12-month period.MethodsThe study period was from May 1, 2019, to May 1, 2020. A stratified sampling procedure supplied 2500 tweets for analysis. Posts were classified by one or more of the following themes: (1) Political Referendum; (2) Institutional Distrust; (3) Individual Rights; (4) Misinformation; (5) THC Vaping is the Real Problem; (6) Smoking Cessation; (7) Adult Use; and (8) Not a Bot. The temporal pattern of tweets over the year was examined.ResultsPolitical Referendum (76.5%) and Institutional Distrust (31.3%) were the most prominent themes, followed by Not a Bot (11.0%), Individual Rights (10.4%), Adult Use (8.0%), Smoking Cessation (6.6%), Misinformation (5.9%), and THC Vaping is the Real Problem (3.5%). Total tweet frequencies increased in September 2019 and peaked in November 2019 before returning to relatively low numbers. Political Referendum and Institutional Distrust were consistently the most prevalent themes over time.ConclusionTwitter posts with the phrase "Flavors Save Lives" commonly discussed voting against political incumbents and mentioned distrust of government representatives. Findings demonstrated the possibility of near real-time Twitter monitoring of public opposition to flavor bans. These data may be valuable for designing tobacco control information campaigns in the future.Implications(a) Starting in 2019 policies restricting the availability of flavored e-cigarette products were proposed or implemented in the United States to curb vaping by youth. (b) This study content analyzed Twitter posts with the phrase "Flavors Save Lives" from a 12-month period to understand opposition to flavor restrictions. (c) Twitter posts commonly discussed voting against political incumbents and mentioned distrust of government representatives. (d) Findings demonstrated the possibility of near real-time Twitter monitoring of public opposition to flavor bans, and contribute to a more comprehensive assessment of different sub-population's responses to current and proposed tobacco control information policies.

Project description:BACKGROUND: The impact of socio-demographic factors and baseline health on the mortality burden of seasonal and pandemic influenza remains debated. Here we analyzed the spatial-temporal mortality patterns of the 1918 influenza pandemic in Spain, one of the countries of Europe that experienced the highest mortality burden. METHODS: We analyzed monthly death rates from respiratory diseases and all-causes across 49 provinces of Spain, including the Canary and Balearic Islands, during the period January-1915 to June-1919. We estimated the influenza-related excess death rates and risk of death relative to baseline mortality by pandemic wave and province. We then explored the association between pandemic excess mortality rates and health and socio-demographic factors, which included population size and age structure, population density, infant mortality rates, baseline death rates, and urbanization. RESULTS: Our analysis revealed high geographic heterogeneity in pandemic mortality impact. We identified 3 pandemic waves of varying timing and intensity covering the period from Jan-1918 to Jun-1919, with the highest pandemic-related excess mortality rates occurring during the months of October-November 1918 across all Spanish provinces. Cumulative excess mortality rates followed a south-north gradient after controlling for demographic factors, with the North experiencing highest excess mortality rates. A model that included latitude, population density, and the proportion of children living in provinces explained about 40% of the geographic variability in cumulative excess death rates during 1918-19, but different factors explained mortality variation in each wave. CONCLUSIONS: A substantial fraction of the variability in excess mortality rates across Spanish provinces remained unexplained, which suggests that other unidentified factors such as comorbidities, climate and background immunity may have affected the 1918-19 pandemic mortality rates. Further archeo-epidemiological research should concentrate on identifying settings with combined availability of local historical mortality records and information on the prevalence of underlying risk factors, or patient-level clinical data, to further clarify the drivers of 1918 pandemic influenza mortality.

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Anatomy into the battle of supporting or opposing reopening amid the COVID-19 pandemic on Twitter: A temporal and spatial analysis.

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