Moreno and Sepúlveda (2021)
Contents
Source Details
Moreno and Sepúlveda (2021) | |
Title: | Article 13 on social media and news media: disintermediation and reintermediation on the modern media landscape |
Author(s): | Moreno, J., Sepúlveda, R. |
Year: | 2021 |
Citation: | Moreno, J. and Sepúlveda, R. (2021) Article 13 on social media and news media: disintermediation and reintermediation on the modern media landscape. Communication & Society 34(2), pp 141-157 |
Link(s): | Open Access |
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About the Data | |
Data Description: | Social discourses surrounding Article 13 were identified drawing on data from: • Twitter, using text and data mining via the API; |
Data Type: | Primary and Secondary data |
Secondary Data Sources: | |
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Data Analysis Methods: | |
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Country(ies): | |
Cross Country Study?: | No |
Comparative Study?: | No |
Literature review?: | No |
Government or policy study?: | No |
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Abstract
“The former Article 13 (now Article 17) of the European directive on copyright and the internet (Directive EC2019/790) has been under negotiations since 2016 and was finally approved in 2019. In Portugal, however, the issue was mostly absent from public scrutiny and debate until November 2018. In that month, the issue arose to a prominent level, both in news media and in social media, following a wave of alerts issued by various young youtubers, incentivized by YouTube management. In this paper, we engage in the discussion concerning disintermediation, studying the way in which such alerts spread both in news media and social media, and understanding the role played by the users of social media platforms in modelling the social relevance and the social discourse of the issue of copyright and the internet. To do so, we used digital methods, collecting and analysing data from Twitter, YouTube and from online news media, mapping Article 13 discussions and identifying key actors in each field, as well as the connections between them. The results show that the ease of access provided by platforms such as Twitter or YouTube converts some users to prominent influencers and that, in some cases, those influencers are able to shift and model the public discourse about relevant collective issues.”
Main Results of the Study
• Both Twitter and YouTube are key mediators in triggering and influencing discourse by their users. Once their official positions have been made known, this stimulates users to respond and support this on the platform itself. In the case of Article 13, Twitter and YouTube’s opposition and tone of criticism towards the legislation was extended and mirrored in an audience of more dispersed users.
• Twitter plays a key role for temporal ‘outbursts’ of activity which in are later (but quickly) absorbed by YouTube. The two platforms often have a reciprocal relationship, with many tweets referring to YouTube videos to indicate support or opposition of Article 13. By contrast, news media outlets take far longer to be influenced by this discourse. The study finds that discussion on Article 13 in news media is entirely absent before Google’s oppositional appeal. After this point, the news media pays lots of attention to Article 13, but is more critical of the mobilisation of users on Twitter and YouTube by downplaying emotive fears (e.g. #saveyourinternet).
• The tone of Twitter users is mainly critical of Article 13, media and music industries, and more generally European politics. Similar commonalities of opinion are apparent on YouTube, even where videos are dispersed across different genres (e.g. comedy, gaming, music) and influencers. As such, Article 13 takes centrality as an issue, rather than being explained by e.g. audience size of a particular genre or influencer.
• Overall, the main narrative of Twitter and YouTube users supports the platform’s own position of (often emotional) opposition to Article 13.
Policy Implications as Stated By Author
The study does not make any explicit copyright policy recommendations.
Coverage of Study
Datasets
Sample size: | 292,299 |
Level of aggregation: | Tweets |
Period of material under study: | 1 October 2018 – 30 April 2019 |
Sample size: | 1,819 |
Level of aggregation: | Videos |
Period of material under study: | 1 October 2018 – 30 April 2019 |
Sample size: | 243 |
Level of aggregation: | News articles |
Period of material under study: | 1 October 2018 – 30 April 2019 |