Mapping the Dutch vaccination debate on Twitter: Identifying communities, narratives, and interactions

Abstract

Highlights Analysis of the Dutch Twitter debate on vaccination using digital methods. Identification of online communities and mapping their perceptions and interactions. Communities include (but not limited to) health professionals and anti-establishment. Anti-establishment most negative about vaccination; health info hardly reaches them. Scripts to retrieve, process, and analyze Twitter data available for future research.

Asymmetric participation of defenders and critics of vaccines to debates on French-speaking Twitter

“…Because each social mediahas its specificities (practices and publics), it is possible that the types of contents and the flow of information differs radically on Facebook or Instagram for instance. However, we judge this to be unlikely as our results are coherent with the data available on vaccine hesitancy and vaccine-related controversies in France 29 and with data pertaining to discussions of vaccines on Twitter in other European countries 26 . Further research comparing the structure of the flow of information on vaccines on each social media and in different countries would contribute to current reflexions on the best ways to curtail the spread of misinformation on the Internet.…”

“…Menczner and Hui also found a very high degree of segregation between pro and anti-vaccine Twitter users 27 . However, Lutkenhaus et al did not find evidence of polarization in discussions of vaccination that took place during the second half of the year 2017 between dutch-speaking Twitter users 26 . The anti-vaccine community was largely connected to several pro-vaccine communities and pro and anti-vaccine users interacted regularly.…”

“…But in the past years, these calls for action seem to have been heard: several online platforms have taken measures to decrease the virality of vaccine-critical contents 3 , national authorities and researchers have developed and applied new online communication tools 5,23 , and pro-vaccine social movements have emerged in several countries with citizens choosing to devote part of their free time to convince hesitant parents or to defend science more generally 24,25 . Recent studies suggest that, thanks to these mobilizations, the internet is not the realm of vaccine critics anymore 15,21,26 and, in some regions, pro-vaccine messages may be gaining the upper hand 27 .…”

Beliefs in Conspiracy Theories and Misinformation About COVID-19: Comparative Perspectives on the Role of Anxiety, Depression and Exposure to and Trust in Information Sources

“…For example, in reaction to the conspiracy theories that claim that 5G cellular network is the cause of the disease 1 , over 200 incidents have been reported of attacks against telecom workers in the U.K. (Vincent, 2020), and numerous mobile telecom masts were set on fire in the Netherlands (Wassens, 2020). Furthermore, previous studies have shown that exposure to disease-related conspiracy theories is associated with lower vaccination intentions (Jolley and Douglas, 2014), lower levels of trust in governmental and health institutions (Lutkenhaus et al, 2019), and less willingness to follow restrictive measures to curtail further propagation of the disease (Imhoff and Lamberty, 2020). Evidence from England also shows that COVID-19-related conspiracy thinking is associated with less adherence to all government guidelines and less willingness to take diagnostic or antibody tests or to be vaccinated (Freeman et al, 2020).…”

Toward spreadable entertainment-education: leveraging social influence in online networks

“…Furthermore, online communities often comprise and attract individuals with shared interests and views, increasing the likelihood of audiences confirming their pre-existing beliefs through mutual interactions. This phenomenon is referred to as the echo chamber and is often associated with increasing polarization on controversial topics ( Colleoni et al , 2014 ; Barberá et al., 2015b ), including health topics such as vaccination ( Lutkenhaus et al , 2019b ). Moreover, algorithmic recommender systems aggravate this effect: online platforms and social media sites algorithmically personalize their content suggestions to match the supposed media preferences of their users, leading to ‘filter bubbles’ that selectively expose people with similar media patterns to similar content ( Pariser, 2012 ).…”