Exploring the use of web searches for risk communication during COVID-19 in Germany

Public information needs and preferences on COVID-19: a cross-sectional study

BACKGROUND

Right from the beginning of the SARS-CoV-2 pandemic the general public faced the challenge to find reliable and understandable information in the overwhelming flood of information. To enhance informed decision-making, evidence-based information should be provided. Aim was to explore the general public's information needs and preferences on COVID-19 as well as the barriers to accessing evidence-based information.

METHODS

We performed a cross-sectional study. Nine hundred twenty-seven panel members were invited to an online survey (12/2020-02/2021). The HeReCa-online-panel is installed at the Martin Luther University Halle-Wittenberg to assess regularly the general public's view on health issues in five regions in Germany. The survey was set up in LimeSurvey, with nine items, multiple-choice and open-ended questions that allowed to gather qualitative data. Quantitative data were analysed descriptively and a content analysis was carried out to categorise the qualitative data.

RESULTS

Six hundred thirty-six panel members provided data; mean age 52 years, 56.2% female, and 64.9% with higher education qualifications. Asked about relevant topics related to COVID-19, most participants selected vaccination (63.8%), infection control (52%), and long-term effects (47.8%). The following 11 categories were derived from the qualitative analysis representing the topics of interest: vaccination, infection control, long-term effects, therapies, test methods, mental health, symptoms, structures for pandemic control, infrastructure in health care, research. Participants preferred traditional media (TV 70.6%; radio 58.5%; newspaper 32.7%) to social media, but also used the internet as sources of information, becoming aware of new information on websites (28.5%) or via email/newsletter (20.1%). The knowledge question (Which European country is most affected by the SARS-CoV-2 pandemic?) was correctly answered by 7.5% of participants. The Robert Koch Institute (93.7%) and the World Health Organization (78%) were well known, while other organisations providing health information were rarely known (< 10%). Barriers to accessing trustworthy information were lack of time (30.7%), little experience (23.1%), uncertainty about how to get access (22.2%), complexity and difficulties in understanding (23.9%), and a lack of target group orientation (15,3%).

CONCLUSIONS

There are extensive information needs regarding various aspects on COVID-19 among the general population. In addition, target-specific dissemination strategies are still needed to reach different groups.

Forecasting and Surveillance of COVID-19 Spread Using Google Trends: Literature Review

The probability of future Coronavirus Disease (COVID)-19 waves remains high, thus COVID-19 surveillance and forecasting remains important. Online search engines harvest vast amounts of data from the general population in real time and make these data publicly accessible via such tools as Google Trends (GT). Therefore, the aim of this study was to review the literature about possible use of GT for COVID-19 surveillance and prediction of its outbreaks. We collected and reviewed articles about the possible use of GT for COVID-19 surveillance published in the first 2 years of the pandemic. We resulted in 54 publications that were used in this review. The majority of the studies (83.3%) included in this review showed positive results of the possible use of GT for forecasting COVID-19 outbreaks. Most of the studies were performed in English-speaking countries (61.1%). The most frequently used keyword was "coronavirus" (53.7%), followed by "COVID-19" (31.5%) and "COVID" (20.4%). Many authors have made analyses in multiple countries (46.3%) and obtained the same results for the majority of them, thus showing the robustness of the chosen methods. Various methods including long short-term memory (3.7%), random forest regression (3.7%), Adaboost algorithm (1.9%), autoregressive integrated moving average, neural network autoregression (1.9%), and vector error correction modeling (1.9%) were used for the analysis. It was seen that most of the publications with positive results (72.2%) were using data from the first wave of the COVID-19 pandemic. Later, the search volumes reduced even though the incidence peaked. In most countries, the use of GT data showed to be beneficial for forecasting and surveillance of COVID-19 spread.

Website analytics for government user behavior during COVID-19 pandemic

Purpose

The present study aims to clarify the following two research objectives: (1) the user behavior of government websites during the coronavirus disease (COVID-19) period and (2) how the government improved government's website design during the COVID-19 period.

Design/methodology/approach

The authors used website analytics to examine usage patterns and behaviors of the government website via personal computer (PC) and mobile devices during the COVID-19 pandemic. In-depth interviews were conducted to understand the user experience of government website users and to gather users' opinions about how government websites should be redesigned.

Findings

With the rising of the COIVID-19 pandemic, most studies expect that the use of government websites through a mobile device will grow astonishingly. The authors uncovered that the COVID-19 pandemic did not increase the use of government websites. Instead, severe declines in website usage were observed for all device users with the declines being more pronounced in mobile device users than in PC users. This is an admonitory caveat that reveals public health and pandemic prevention information announced on government websites cannot be effectively transmitted to the general public through official online platforms.

Originality/value

The study highlights the gap in information behavior and usage patterns between PC and mobile device users when visiting government websites. Although mobile devices brought many new visitors, mobile devices are ineffective in retaining visitors and continuous long-term use. The results of localize experience is helpful in the improvement of government website evaluation worldwide.

From science to politics: COVID-19 information fatigue on YouTube

Objective

The COVID-19 pandemic is the first pandemic where social media platforms relayed information on a large scale, enabling an “infodemic” of conflicting information which undermined the global response to the pandemic. Understanding how the information circulated and evolved on social media platforms is essential for planning future public health campaigns. This study investigated what types of themes about COVID-19 were most viewed on YouTube during the first 8 months of the pandemic, and how COVID-19 themes progressed over this period.

Methods

We analyzed top-viewed YouTube COVID-19-related videos in English from December 1, 2019 to August 16, 2020 with an open inductive content analysis. We coded 536 videos associated with 1.1 billion views across the study period. East Asian countries were the first to report the virus, while most of the top-viewed videos in English were from the US. Videos from straight news outlets dominated the top-viewed videos throughout the outbreak, and public health authorities contributed the fewest. Although straight news was the dominant COVID-19 video source with various types of themes, its viewership per video was similar to that for entertainment news and YouTubers after March.

Results

We found, first, that collective public attention to the COVID-19 pandemic on YouTube peaked around March 2020, before the outbreak peaked, and flattened afterwards despite a spike in worldwide cases. Second, more videos focused on prevention early on, but videos with political themes increased through time. Third, regarding prevention and control measures, masking received much less attention than lockdown and social distancing in the study period.

Conclusion

Our study suggests that a transition of focus from science to politics on social media intensified the COVID-19 infodemic and may have weakened mitigation measures during the first waves of the COVID-19 pandemic. It is recommended that authorities should consider co-operating with reputable social media influencers to promote health campaigns and improve health literacy. In addition, given high levels of globalization of social platforms and polarization of users, tailoring communication towards different digital communities is likely to be essential.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12889-022-13151-7.

Classifications, Changes, and Challenges of Online Health Information Seekers during COVID-19

OBJECTIVES

The purpose of this study was to explore consumers' experiences before and during the COVID-19 outbreak to improve public health by providing effective consumer health information.

METHODS

Interviews were conducted with 20 health information consumers who were 18 or older until data saturation was reached. The selected participants were among users of the Korean National Health Insurance Service (NHIS). The data were collected before the COVID-19 outbreak (September 2014) and during the COVID-19 outbreak (October 2020) to describe experiences and changes before and during the pandemic. Data were analyzed according to the qualitative content analysis method.

RESULTS

As a result, 3 main domains and 10 subdomains were derived from classifications, changes, and challenges of online health information seekers.

CONCLUSIONS

The findings of this study guide the understanding of health information seekers for the development of consumer-tailored health information systems.

Information retrieval in an infodemic: the case of COVID-19 publications

Background

The COVID-19 global health crisis has led to an exponential surge in published scientific literature. In an attempt to tackle the pandemic, extremely large COVID-19–related corpora are being created, sometimes with inaccurate information, which is no longer at scale of human analyses.

Objective

In the context of searching for scientific evidence in the deluge of COVID-19–related literature, we present an information retrieval methodology for effective identification of relevant sources to answer biomedical queries posed using natural language.

Methods

Our multistage retrieval methodology combines probabilistic weighting models and reranking algorithms based on deep neural architectures to boost the ranking of relevant documents. Similarity of COVID-19 queries is compared to documents, and a series of postprocessing methods is applied to the initial ranking list to improve the match between the query and the biomedical information source and boost the position of relevant documents.

Results

The methodology was evaluated in the context of the TREC-COVID challenge, achieving competitive results with the top-ranking teams participating in the competition. Particularly, the combination of bag-of-words and deep neural language models significantly outperformed an Okapi Best Match 25–based baseline, retrieving on average, 83% of relevant documents in the top 20.

Conclusions

These results indicate that multistage retrieval supported by deep learning could enhance identification of literature for COVID-19–related questions posed using natural language.