Introduced invasive plants are a major environmental problem, but public interest in invasive plants is generally considered low compared to climate change and threatened flagship species, hindering support for effective management and policy. To understand what does drive public interest in invasive plants in the US, we investigated Google Trends search data from 2010 to 2020 for 209 introduced plant species found in the continental US. Using a phylogenetically-controlled structural equation model, we investigated three hypothesized drivers of interest: (1) plant abundance as quantified by national and state-level occurrence records in the Global Biodiversity Information Facility, (2) four key plant traits that might influence plant conspicuousness to the general public: ornamental use, human health risks, monoculture formation, and plants with positive economic value, and (3) media coverage, in particular the volume and sentiment of news articles over the same 10-year period. Public search interest was highest for the most abundant introduced species and those with human health risks, but significantly lower for ornamentals. News coverage was mostly negatively toned and disproportionately focused on a relatively small group of widespread invasive species, with significantly lower and more positively-worded coverage of ornamentals. Ultimately, we suggest that a narrow emphasis on a few highly covered ‘notorious’ invasive plant species, with lower and more positive coverage of ornamental introduced species, could send mixed messages and weaken public awareness of the threats of biological invasions. However, the generally strong linkages between public search interest and media coverage of invasive plants suggests ample opportunity to improve messaging and increase public awareness.
A Linguistic Analysis of News Coverage of E-Healthcare in China with a Heterogeneous Graphical Model
E-healthcare has been envisaged as a major component of the infrastructure of modern healthcare, and has been developing rapidly in China. For healthcare, news media can play an important role in raising public interest and utilization of a particular service and complicating (and, perhaps clouding) debate on public health policy issues. We conducted a linguistic analysis of news reports from January 2015 to June 2021 related to E-healthcare in mainland China, using a heterogeneous graphical modeling approach. This approach can simultaneously cluster the datasets and estimate the conditional dependence relationships of keywords. It was found that there were eight phases of media coverage. The focuses and main topics of media coverage were extracted based on the network hub and module detection. The temporal patterns of media reports were found to be mostly consistent with the policy trend. Specifically, in the policy embryonic period (2015–2016), two phases were obtained, industry management was the main topic, and policy and regulation were the focuses of media coverage. In the policy development period (2017–2019), four phases were discovered. All the four main topics, namely industry development, health care, financial market, and industry management, were present. In 2017 Q3–2017 Q4, the major focuses of media coverage included social security, healthcare and reform, and others. In 2018 Q1, industry regulation and finance became the focuses. In the policy outbreak period (2020–), two phases were discovered. Financial market and industry management were the main topics. Medical insurance and healthcare for the elderly became the focuses. This analysis can offer insights into how the media responds to public policy for E-healthcare, which can be valuable for the government, public health practitioners, health care industry investors, and others.
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- NSF-PAR ID:
- 10419383
- Date Published:
- Journal Name:
- Entropy
- Volume:
- 24
- Issue:
- 11
- ISSN:
- 1099-4300
- Page Range / eLocation ID:
- 1557
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Abstract -
Chinese government lifted its “Zero COVID-19” policy in December 2022. The estimated COVDI-19 new cases and deaths after the policy change are 167–279 million (about 12.0% to 20.1% of the Chinese population) and 0.68–2.1 million, respectively. Recent data also revealed continuous drops in fertility rate and historically lowest growth in gross domestic production in China. Thus, balancing COVID-19 control and economic recovery in China is of paramount importance yet very difficult. Supply chain disruption, essential service reduction and shortage of intensive care units have been discussed as the challenges associated with lifting “Zero COVID-19” policy. The additional challenges may include triple epidemic of COVID-19, respiratory syncytial virus and influenza, mental health issues of healthcare providers, care givers and patients, impact on human mobility, lack of robust genomic and epidemiological data and long COVID-19. However, the policy-associated opportunities and other challenges are largely untouched, but warrant attention of and prompt reactions by the policy makers, healthcare providers, public health officials and other stakeholders. The associated benefits are quick reach of herd immunity, boost of economy and businesses activities and increase in social activities. At this moment, we must embrace the policy change, effectively mitigate its associated problems and timely and effectively maximize its associated benefits.more » « less
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Background As a number of vaccines for COVID-19 are given emergency use authorization by local health agencies and are being administered in multiple countries, it is crucial to gain public trust in these vaccines to ensure herd immunity through vaccination. One way to gauge public sentiment regarding vaccines for the goal of increasing vaccination rates is by analyzing social media such as Twitter. Objective The goal of this research was to understand public sentiment toward COVID-19 vaccines by analyzing discussions about the vaccines on social media for a period of 60 days when the vaccines were started in the United States. Using the combination of topic detection and sentiment analysis, we identified different types of concerns regarding vaccines that were expressed by different groups of the public on social media. Methods To better understand public sentiment, we collected tweets for exactly 60 days starting from December 16, 2020 that contained hashtags or keywords related to COVID-19 vaccines. We detected and analyzed different topics of discussion of these tweets as well as their emotional content. Vaccine topics were identified by nonnegative matrix factorization, and emotional content was identified using the Valence Aware Dictionary and sEntiment Reasoner sentiment analysis library as well as by using sentence bidirectional encoder representations from transformer embeddings and comparing the embedding to different emotions using cosine similarity. Results After removing all duplicates and retweets, 7,948,886 tweets were collected during the 60-day time period. Topic modeling resulted in 50 topics; of those, we selected 12 topics with the highest volume of tweets for analysis. Administration and access to vaccines were some of the major concerns of the public. Additionally, we classified the tweets in each topic into 1 of the 5 emotions and found fear to be the leading emotion in the tweets, followed by joy. Conclusions This research focused not only on negative emotions that may have led to vaccine hesitancy but also on positive emotions toward the vaccine. By identifying both positive and negative emotions, we were able to identify the public's response to the vaccines overall and to news events related to the vaccines. These results are useful for developing plans for disseminating authoritative health information and for better communication to build understanding and trust.more » « less
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Risk perception and risk averting behaviors of public agencies in the emergence and spread of COVID-19 can be retrieved through online social media (Twitter), and such interactions can be echoed in other information outlets. This study collected time-sensitive online social media data and analyzed patterns of health risk communication of public health and emergency agencies in the emergence and spread of novel coronavirus using data-driven methods. The major focus is toward understanding how policy-making agencies communicate risk and response information through social media during a pandemic and influence community response—ie, timing of lockdown, timing of reopening, etc.—and disease outbreak indicators—ie, number of confirmed cases and number of deaths. Twitter data of six major public organizations (1,000-4,500 tweets per organization) are collected from February 21, 2020 to June 6, 2020. Several machine learning algorithms, including dynamic topic model and sentiment analysis, are applied over time to identify the topic dynamics over the specific timeline of the pandemic. Organizations emphasized on various topics—eg, importance of wearing face mask, home quarantine, understanding the symptoms, social distancing and contact tracing, emerging community transmission, lack of personal protective equipment, COVID-19 testing and medical supplies, effect of tobacco, pandemic stress management, increasing hospitalization rate, upcoming hurricane season, use of convalescent plasma for COVID-19 treatment, maintaining hygiene, and the role of healthcare podcast in different timeline. The findings can benefit emergency management, policymakers, and public health agencies to identify targeted information dissemination policies for public with diverse needs based on how local, federal, and international agencies reacted to COVID-19.more » « less
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.3390/e24111557
