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

Tizzoni, Michele; Panisson, André; Paolotti, Daniela; Cattuto, Ciro

doi:10.1101/346411

Cited by 11 publications

(19 citation statements)

References 40 publications

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“…This led many authors to suggest that high media activity can lead to abnormal Google search trends, possibly leading to estimation errors 17,20–24 . This “media effect” was also observed by others studying Zika 25–27 , and contributed to the disenchantment with the potential of such tools, particularly during such “extraordinary times”.…”

Section: Introductionsupporting

confidence: 64%

Learning from pandemics: using extraordinary events can improve disease now-casting models

Mesquita¹,

Vieira

Perfeito³

et al. 2021

Preprint

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Online searches have been used to study different health-related behaviours, including monitoring disease outbreaks. An obvious caveat is that several reasons can motivate individuals to seek online information and models that are blind to people’s motivations are of limited use and can even mislead. This is particularly true during extraordinary public health crisis, such as the ongoing pandemic, when fear, curiosity and many other reasons can lead individuals to search for health-related information, masking the disease-driven searches. However, health crisis can also offer an opportunity to disentangle between different drivers and learn about human behavior. Here, we focus on the two pandemics of the 21st century (2009-H1N1 flu and Covid-19) and propose a methodology to discriminate between search patterns linked to general information seeking (media driven) and search patterns possibly more associated with actual infection (disease driven). We show that by learning from such pandemic periods, with high anxiety and media hype, it is possible to select online searches and improve model performance both in pandemic and seasonal settings. Moreover, and despite the common claim that more data is always better, our results indicate that lower volume of the right data can be better than including large volumes of apparently similar data, especially in the long run. Our work provides a general framework that can be applied beyond specific events and diseases, and argues that algorithms can be improved simply by using less (better) data. This has important consequences, for example, to solve the accuracy-explainability trade-off in machine-learning.

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Section: Introductionsupporting

confidence: 64%

Learning from pandemics: using extraordinary events can improve disease now-casting models

Mesquita¹,

Vieira

Perfeito³

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…To more systematically explore the relationship between media coverage, public attention, and epidemic progression, we considered a linear regression model to nowcast the collective public attention for each country (quantified by the number of comments by geolocalized Reddit users or visits to relevant Wikipedia pages) using the volume of media coverage or the COVID-19 incidence as independent variables. We also included “memory effects” on the public attention by considering an exponential decaying term in the news time series [ 22 ]. We compared the three models, where the independent variables are the domestic incidence, the news volume, and the news volume plus a memory term, using the adjusted coefficient of determination ( R 2 ) [ 65 ].…”

Section: Resultsmentioning

confidence: 99%

“…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. To include “memory effects” in the public response to media coverage, we also considered a modified version of this simple model, in which we weight a cumulative news articles volume time series with an exponential decay term [ 22 ]. Formally, we define the new variable as:…”

Section: Methodsmentioning

confidence: 99%

“…With respect to past epidemics and pandemics, studies on traditional news coverage of the 2009 H1N1 pandemic highlighted the importance of framing and its effect on people’s perception, behaviors (eg, vaccination intent), and stigmatization of cultures at the epicenter of the outbreak, as well as how these factors differ across countries and cultures [ 16 - 21 ]. During the Zika virus epidemic in 2016, public attention was synchronized across US states, driven by news coverage about the outbreak and independent of the real local risk of infection [ 22 ]. With respect to the COVID-19 pandemic itself, a recent study clearly showed how Google searches for coronavirus in the United States spiked significantly immediately after the announcement of the first confirmed case in each state [ 23 ].…”

Section: Introductionmentioning

confidence: 99%

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

Gozzi¹,

Tizzani²,

Starnini³

et al. 2020

J Med Internet Res

Self Cite

138

124

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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.

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“…The real world also encompasses myriad information sources, individual experiences, and biases. Also, information from media will have a different effect on decision-making than having friends and family become ill [44]. Individuals who follow stay-home protocols have different interactions than those who interact in public spaces.…”

mentioning

confidence: 99%

The ‘Icarus effect’ of preventative health behaviors

Carrignon

Bentley²,

Silk

et al. 2020

Preprint

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Ongoing efforts to combat the global pandemic of COVID-19 via public health policy have revealed the critical importance of understanding how individuals understand and react to infection risks. We here present a model to explore 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 in populations with age-structure in both disease risk and social learning - two critical features of the current COVID-19 crisis. Our results concur with anecdotal observations of age-based 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.

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

Cited by 11 publications

References 40 publications

Learning from pandemics: using extraordinary events can improve disease now-casting models

Learning from pandemics: using extraordinary events can improve disease now-casting models

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

The ‘Icarus effect’ of preventative health behaviors

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