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ABSTRACT
Recent work on digital political engagement has extensively shown that social media platforms enhance political participation and collective action. However, the idea that citizen voice through social media can give rise, under given conditions, to a specific digital force combining properties of social movements and public opinion has received less attention. We fill this gap by analysing the digital discussion around the Twitter hashtag #RefugeesWelcome as a case of ‘digital movement of opinion’ (DMO). When the refugee crisis erupted in 2015, an extraordinary wave of empathy characterized the publics’ reactions in key European hosting countries, especially as a result of viral images portraying refugee children as the main victims. Using a triangulation of network, content and metadata analysis, we find that this DMO was driven primarily by social media elites whose tweets were then echoed by masses of isolated users. We then test the post-DMO status of the hashtag-sphere after a potentially antithetical shock such as the November 2015 Paris terrorist attacks, which polarized the network public. Overall, we argue that the concept of DMO provides a heuristically useful tool for future research on new forms of digital citizen participation.
Disclosure statement
No potential conflict of interest was reported by the authors.
Notes on contributor
Mauro Barisione is Professor of Political sociology and director of the PhD programme in Sociology and methodology of social research at the University of Milan, Italy. His research focuses on public opinion and voting, political participation and social media, leadership and political communication. His recent publications include Social media and European politics. Rethinking power and legitimacy in the digital era (2017, with A. Michailidou) and articles in European Journal of Communication, International Journal of Public Opinion Research, Electoral Studies, Leadership, Journal of Experimental Political Science [email: [email protected]].
Asimina Michailidou is Senior Researcher at the ARENA Centre for European Studies, University of Oslo. Her research focuses on digital news and social media, the structure, actors and grammar of the European digital public sphere, Euroscepticism and crisis communication through digital media. Among her publications are Social media and European politics (2017, with M. Barisione), The Internet and European integration (2014; with H.J. Trenz and P. de Wilde) and Contesting Europe (2013, with P. de Wilde and H.J. Trenz). Her articles appear in the European Journal of Political Research, the Journal of Contemporary European Research, the European Journal of Communication Research, Journalism Practice, the Journal of European Public Policy, National Identities, International Political Science Review and Partecipazione e Conflitto [email: [email protected]].
Massimo Airoldi has completed his PhD in Sociology and Methodology of Social Research within the NASP (Network for the Advancement of Social and Political Studies) program at the University of Milan, Italy. He is currently a postdoctoral researcher at Lifestyle Research Center, Emlyon Business School. His main research interests are social media platforms, cultural sociology and digital methods [email: [email protected]].
Notes
1 The percentage of positive comments posted by Twitter users about refugees increased, for instance, by 14 points in France and by almost 40 points in Britain in the first week of September 2016 (Voices from the Blogs 2015), retrieved from http://sentimeter.corriere.it/2015/09/11/la-tragedia-di-aylan-scuote-le-coscienze-degli-inglesi-piu-che-degli-italiani/?refresh_ce-cp (last accessed 27 October 2016).
2 Given the inherent interdependence between mainstream and social media in the present ‘hybrid’ media environment, we do not consider as a strategic issue here the causal primacy of news media vs. social media in attributing to a topic the salience that is always necessary for the rise of a DMO.
3 Unlike social movements, moreover, a DMO could arise in a pre-election context in support of a political candidate, for example in a scenario where an outlier candidate is brought into the electoral scene thanks to momentum generated or facilitated by a DMO. But once this candidate is brought into the electoral race, his/her continuing support, now confronted with political oppositions, falls under the umbrella of institutionalized electoral competition and is no longer a DMO.
4 The patterns of interaction between a DMO and non-digital manifestations of the same movement of opinion are analysed in Barisione and Ceron (Citation2017).
6 We do not assume the second emotional shock to have a polarizing effect in abstracto. In fact, following the public opinion literature, this type of event may be compared to those (such as, typically, wars or ‘attacks to the nation’) that trigger a ‘rally around the flag’ effect, that is one of homogenization. But in this case the shock eliciting in-group solidarity follows a pre-existing pattern of solidarity toward out-groups. Therefore, a polarized pattern results from the clash between two conflicting messages (Zaller, Citation1992) triggering opposite ‘homogenizing’ emotional reactions, one positive toward refugees (early September images), and the other potentially negative. Of course, imputation of responsibility to refugees for the Paris attack requires a specific work in terms of framing on the part of the messenger (Tweet producer). This is what we seek to analyse in the second part of the analysis.
7 We define a Twitterpreneur as a Twitter user with over 1000 followers, whose Twitter profile highlights their expertise in social media communications. They may also have a blog, website, Facebook page and/or Instagram profile. A Twitterpreneur need not be a professional expert in a field. Their main qualification is their large number of followers due to them being recognized as influential actors within the Twittersphere.
8 Hashtagify.me accesses Twitter's REST API, with the parameter to download ‘all’ tweets relevant to a hashtag and not only the most popular ones (in order to avoid a bias in the data towards big influencers). Hashtagify.me then uses 100% of the tweets in the quantitative analysis, rather than a random sample (except for any possible errors in the API, in which case the sampling is at least >95% of all relevant tweets).
9 Given the 20 top hubs in each network, we qualitatively detected their tweets’ sentiment towards the issues raised by #RefugeesWelcome. In the monitored time spans, each hub authored either all pro- or all against-refugees tweets, this making the coding process straightforward. Then, we assumed that those Twitter users retweeting the hubs’ tweets tended to share a similar perspective on the topic.
10 Citizen: A Twitter user with less than 1000 followers, whose Twitter profile does not highlight any specific professional qualifications.
11 Social media entrepreneur: a Twitter user with over 1000 followers, whose Twitter profile highlights their expertise in social media communications. They may also have a blog, website, Facebook page and/or Instagram profile. A social media entrepreneur need not be a professional expert in a field. Their main qualification is their large number of followers due to them being recognized as influential actors in the public sphere.
12 News source: this category includes the Twitter profiles of news organizations/platforms, such as the official Twitter account of the BBC or Reuters, as well as the Twitter profiles of individual journalists.
13 Expert: A Twitter user with a professional profile, which identifies him/her as an expert in a particular field (e.g. economist, lawyer, political analyst).
14 This category was created after an initial exploratory coding of the dataset, whereby it transpired that Pope Francis's Twitter presence was among the most influential and re-tweeted within the #RefugeesWelcome Twittersphere. No other religious leader featured prominently in the sample dataset that we processed qualitatively.
15 Community detection consists in a network analysis technique aiming to identify sub-groups of nodes, known as ‘communities’ or ‘clusters’ (see Blondel, Guillaume, Lambiotte, & Lefebvre, Citation2008).
16 The fact that Obama is blue too is a consequence of a number of critical tweets sent by right-wing activists and mentioning Obama's profile.
17 Figures 3 and 4 were made using Yifan Hu layout in Gephi.