The impact of news exposure on collective attention in the United States during the 2016 Zika epidemic

Poor attention: The wealth and regional gaps in event attention and coverage on Wikipedia

Wikipedia is an important source of general knowledge covering a wide range of topics. Moreover, for many people around the world, it also serves as an essential news source for major events such as elections or disasters. Although Wikipedia covers many such events, some events are underrepresented and lack attention, despite their newsworthiness predicted from news value theory. In this paper, we analyze 17 490 event articles in four Wikipedia language editions and examine how the economic status and geographic region of the event location affects the attention and coverage it receives. We find that major Wikipedia language editions have a skewed focus, with more attention given to events in the world's more economically developed countries and less attention to events in less affluent regions. However, other factors, such as the number of deaths in a disaster, are also associated with the attention an event receives. Overall, this work provides a nuanced understanding of attention and coverage on Wikipedia through event articles and adds new empirical analysis to news value theory.

Crime, inequality and public health: a survey of emerging trends in urban data science

Urban agglomerations are constantly and rapidly evolving ecosystems, with globalization and increasing urbanization posing new challenges in sustainable urban development well summarized in the United Nations' Sustainable Development Goals (SDGs). The advent of the digital age generated by modern alternative data sources provides new tools to tackle these challenges with spatio-temporal scales that were previously unavailable with census statistics. In this review, we present how new digital data sources are employed to provide data-driven insights to study and track (i) urban crime and public safety; (ii) socioeconomic inequalities and segregation; and (iii) public health, with a particular focus on the city scale.

The value of public service broadcasting in Japan during COVID-19 pandemic: An analysis of WTP by Blinder-Oaxaca decomposition

The first case of COVID-19 in Japan was detected in January 2020, and the first outbreak occurred between March and May of that year. In this study, changes in the willingness to pay (WTP) for public service media (PSM) were analyzed using data from public opinion polls conducted before and after the period of the first outbreak. A comparison of the samples obtained via Blinder-Oaxaca decomposition revealed the importance of and satisfaction with the news that viewers felt, and their viewing frequency increased after the pandemic, as did WTP. The findings can be interpreted as an increase in the demand for news as the infection spread. The analysis also showed that the WTP for PSM decreased a little owing to the reduction in sports programs. The results demonstrate the importance of PSM in moments of crisis, such as pandemics.

Blacklists do not necessarily make people curious about invasive alien species. A case study with Bayesian structural time series and Wikipedia searches about invasive mammals in Italy

Blacklists of invasive alien species (IAS) are a popular tool for managing and preventing biological invasions. Moreover, blacklists also have the potential to make the general public more curious about biological invasions, usually by benefiting from media coverage and providing accessible examples of IAS.

We have tested if the implementation of the first List of IAS of Union concern by the European Union increased visits to Wikipedia pages on invasive alien mammals in Italy. We adopted causal impact analysis to quantify changes in the overall volume of visits to pages about invasive alien mammals that appeared on the list, by using pages about native mammals as a control. Following the publication of the first Union list, there was no increase in the amount of visits to Wikipedia pages on invasive mammals, regardless of their inclusion in the Union list. Rather, visits to Wikipedia were irregular in time, coinciding with media coverage of single, charismatic species.

Our results indicate that important policymaking initiatives do not necessarily increase curiosity about biological invasions, even when they are covered by generalist media and are relatively easy to understand. We would therefore emphasise that policymaking initiatives should be coupled with adequate communication campaigns and should adopt communication guidelines for generalist media.

How social learning shapes the efficacy of preventative health behaviors in an outbreak

The global pandemic of COVID-19 revealed the dynamic heterogeneity in how individuals respond to infection risks, government orders, and community-specific social norms. Here we demonstrate how both individual observation and social learning are likely to shape behavioral, and therefore epidemiological, dynamics over time. Efforts to delay and reduce infections can compromise their own success, especially when disease risk and social learning interact within sub-populations, as when people observe others who are (a) infected and/or (b) socially distancing to protect themselves from infection. Simulating socially-learning agents who observe effects of a contagious virus, our modelling results are consistent with with 2020 data on mask-wearing in the U.S. and also concur with general observations of cohort induced differences in reactions to public health recommendations. We show how shifting reliance on types of learning affect the course of an outbreak, and could therefore factor into policy-based interventions incorporating age-based cohort differences in response behavior.

Integrating digital and field surveillance as complementary efforts to manage epidemic diseases of livestock: African swine fever as a case study

SARS-CoV-2 has clearly shown that efficient management of infectious diseases requires a top-down approach which must be complemented with a bottom-up response to be effective. Here we investigate a novel approach to surveillance for transboundary animal diseases using African Swine (ASF) fever as a model. We collected data both at a population level and at the local level on information-seeking behavior respectively through digital data and targeted questionnaire-based surveys to relevant stakeholders such as pig farmers and veterinary authorities. Our study shows how information-seeking behavior and resulting public attention during an epidemic, can be identified through novel data streams from digital platforms such as Wikipedia. Leveraging attention in a critical moment can be key to providing the correct information at the right moment, especially to an interested cohort of people. We also bring evidence on how field surveys aimed at local workers and veterinary authorities remain a crucial tool to assess more in-depth preparedness and awareness among front-line actors. We conclude that these two tools should be used in combination to maximize the outcome of surveillance and prevention activities for selected transboundary animal diseases such as ASF.

Facebook and mosquito-borne disease outbreaks: An analysis of public responses to federal health agencies' posts about dengue and Zika in 2016

Responses of Facebook users to four United States federal health agencies' social media posts about dengue and Zika (mosquito-borne diseases), surveillance, and control during the Zika outbreak in 2016 were tracked. Official Facebook pages of health agencies were analyzed, and a qualitative analysis program was used to perform a thematic analysis of the data on public responses to health agency posts. Public sentiment analysis showed that Facebook users had a negative sentiment towards health information observed in this study. Themes were identified in the studied posts, giving insight into the nature of public discussions and responses to federal health agencies. Themes were assessed based on the way the agencies' mosquito-borne disease information was received by the public through the social media platform, Facebook. Results indicate that public perception/understanding of mosquito-borne disease outbreaks can be assessed by analyzing public interactions with health agencies on Facebook. The importance of maximizing effectiveness by addressing issues in sharing health education information, risk communication, and monitoring of public responses by health agencies through social media platforms is discussed.

COVID-19, media coverage of bats and related Web searches: a turning point for bat conservation?

SARS‐CoV‐2, the virus that caused the COVID‐19 pandemic, is genomically similar to a SARS‐like beta‐coronavirus found in Asian rhinolophid bats. This evolutionary relationship impressed the global media, which then emphasised bats as key actors in the spillover that resulted in the pandemic. In this study, we highlight changes in the traditional and new media coverage of bats and in Internet search volumes that occurred since the beginning of the COVID‐19 pandemic in 2020.

We analysed Google and Wikipedia searches for bats and coronaviruses in 21 countries and eight languages, as well as television broadcasts in the USA, some of which have global coverage, between January 2016 and December 2020. In January 2020, the amount of television news about bats boomed, and news associated with the term ‘bat’ shifted to COVID‐19‐related topics. A nearly identical pattern was observed in Google searches during 2020 at the global scale. The daily time series of television coverage and Internet search volumes on bats and coronavirus in the USA covaried in the first quarter of 2020, in line with the existence of a media bubble. Time‐series analysis revealed that both the Google Trends index and visits to Wikipedia pages about bats boomed in early 2020, despite the fact that this time of year is usually characterised by low search volumes.

Media coverage emphasised, correctly or not, the role of bats in the COVID‐19 pandemic and amplified public interest in bats worldwide. The public image of these mammals, in many cases threatened and important ecosystem service providers, was seriously compromised. We therefore recommend that policymakers and journalists prioritise scientifically accurate communication campaigns about bats, which would help counteract the surge in bat persecution, and leverage interest towards positive human–bat interactions.

The Framing of COVID-19 in Italian Media and Its Relationship with Community Mobility: A Mixed-Method Approach

Media framing of epidemics was found to influence public perceptions and behaviors in experiments, yet no research has been conducted on real-world behaviors during public health crises. We examined the relationship between Italian news media coverage of COVID-19 and compliance with stay-at-home orders, which could impact the spread of epidemics. We used a computational method for framing analysis (ANTMN) and combined it with Google's Community Mobility data. A time-series analysis using vector autoregressive models showed that the Italian media used media frames that were largely congruent with ones used by journalists in other countries: A scientific frame focusing on symptoms and health effects, a containment frame focusing on attempts to ameliorate risks, and a social frame, focusing on political and social impact. The prominence of different media frames over time was associated with changes in Italians' mobility patterns. Specifically, we found that the social frame was associated with increased mobility, whereas the containment frame was associated with decreased mobility. The results demonstrate that the ways the news media discuss epidemics can influence changes in community mobility, above and beyond the effect of the number of deaths per day.

The Dynamical Interplay of Collective Attention, Awareness and Epidemics Spreading in the Multiplex Social Networks During COVID-19

Leveraging social and communication technologies, we can digitally observe that the collective attention typically exhibits a heterogeneous structure. It shows that people's interests are organized in clusters around different topics, but the rising of an extraordinary emergency event, as the coronavirus disease epidemics, channels the people's attention into a more homogenized structure, shifting it as triggered by a non-random collective process. The connectedness of networked individuals, on multiple social levels, impacts on the attention, representing a tuning element of different behavioural outcomes, changing the awareness diffusion enough to produce effects on epidemics spreading. We propose a mathematical framework to model the interplay between the collective attention and the co-evolving processes of awareness diffusion, modelled as a social contagion phenomenon, and epidemic spreading on weighted multiplex networks. Our proposed modeling approach structures a systematically understanding as a social network marker of interdependent collective dynamics through the introduction of the multiplex dimension of both networked individuals and topics, quantifying the role of human-related factors, as homophily, network properties, and heterogeneity. We introduce a data-driven approach by integrating different types of data, digitally traced as user-generated data from Twitter and Google Trends, in response to an extraordinary emergency event as coronavirus disease. Our findings demonstrate how the proposed model allows us to quantify the reaction of the collective attention, proving that it can represent a social predictive marker of the awareness dynamics, unveiling the impact on epidemic spreading, for a timely crisis response planning. Simulations results shed light on the coherence between the data-driven approach and the proposed analytical model.

Collective Response to Media Coverage of the COVID-19 Pandemic on Reddit and Wikipedia: Mixed-Methods Analysis

BACKGROUND

The exposure and consumption of information during epidemic outbreaks may alter people's risk perception and trigger behavioral changes, which can ultimately affect the evolution of the disease. It is thus of utmost importance to map the dissemination of information by mainstream media outlets and the public response to this information. However, our understanding of this exposure-response dynamic during the COVID-19 pandemic is still limited.

OBJECTIVE

The goal of this study is to characterize the media coverage and collective internet response to the COVID-19 pandemic in four countries: Italy, the United Kingdom, the United States, and Canada.

METHODS

We collected a heterogeneous data set including 227,768 web-based news articles and 13,448 YouTube videos published by mainstream media outlets, 107,898 user posts and 3,829,309 comments on the social media platform Reddit, and 278,456,892 views of COVID-19-related Wikipedia pages. To analyze the relationship between media coverage, epidemic progression, and users' collective web-based response, we considered a linear regression model that predicts the public response for each country given the amount of news exposure. We also applied topic modelling to the data set using nonnegative matrix factorization.

RESULTS

Our results show that public attention, quantified as user activity on Reddit and active searches on Wikipedia pages, is mainly driven by media coverage; meanwhile, this activity declines rapidly while news exposure and COVID-19 incidence remain high. Furthermore, using an unsupervised, dynamic topic modeling approach, we show that while the levels of attention dedicated to different topics by media outlets and internet users are in good accordance, interesting deviations emerge in their temporal patterns.

CONCLUSIONS

Overall, our findings offer an additional key to interpret public perception and response to the current global health emergency and raise questions about the effects of attention saturation on people's collective awareness and risk perception and thus on their tendencies toward behavioral change.

Crowdsourcing Street View Imagery: A Comparison of Mapillary and OpenStreetCam

Over the last decade, Volunteered Geographic Information (VGI) has emerged as a viable source of information on cities. During this time, the nature of VGI has been evolving, with new types and sources of data continually being added. In light of this trend, this paper explores one such type of VGI data: Volunteered Street View Imagery (VSVI). Two VSVI sources, Mapillary and OpenStreetCam, were extracted and analyzed to study road coverage and contribution patterns for four US metropolitan areas. Results show that coverage patterns vary across sites, with most contributions occurring along local roads and in populated areas. We also found that a few users contributed most of the data. Moreover, the results suggest that most data are being collected during three distinct times of day (i.e., morning, lunch and late afternoon). The paper concludes with a discussion that while VSVI data is still relatively new, it has the potential to be a rich source of spatial and temporal information for monitoring cities.