The Affective Affordances of Social Media

The use of emotional language by major Italian news outlets on Twitter

Abstract

Journalism has a conflicting relationship with emotions and thus scholars are divided in the critique about the nexus between emotions and journalistic professional ideals. Whilst some authors argue that emotions ought to have no place in journalism, as they challenge established notions of objectivity and impartiality, others recognise their value and precise role in calling into question hidden power relations within the classic liberal journalism tradition. Yet the fast-changing digital media environment in which news media organisations must fight for news users’ attention on social media has redesigned news production and distribution, and made some authors proclaim an “emotional turn” in journalism (Wahl-Jorgensen & Pantti, 2021). Studies that link social media, news and emotions tend to focus on the audience-centric perspective to assess which types of emotion might increase the sharing potential of news stories and virality (Al-Rawi, 2019; Hasell, 2021), mostly in the UK and US contexts. In this paper, we build upon Sturm Wilkerson et al.’s (2021) notion of “affective affordances” to contribute to this discussion by extending the investigation to the Italian language and Twittersphere. Methodologically, we deployed machine learning techniques for sentiment and emotion analysis to almost half million tweets retrieved from the pages of eight major Italian news outlets on Twitter, such as Il Fatto Quotidiano, Corriere della Sera, Il Post, Fanpage, La Gazzetta dello Sport, Il Sole24 Ore, La Repubblica, HuffPost Italia, to address the core questions: How do different news outlets use emotional language in tweets? Which types of emotion downplay or amplify their visibility and engagement with news users on Twitter? Our results indicate that Italian news outlets leverage emotions to garner more engagement on Twitter, which are in line with previous studies that link social media, journalism and emotion. Specifically, ’anger’ is the most used emotion by far, proved to be effective in engaging users due to anger-based tweets being the most retweeted posts. The trend in using hashtags also shows that soft news threads (e.g., sports and music) call for joyful tweet messages in opposition to hashtags related to hard news. Yet, negative tweets leveraging emotions like ‘fear’ and ‘anger’ can also be seen in tweets posted by newspapers, such as ’Fanpage’ and ’La Gazzetta’ even though these news outlets are more on the bright side of the news spectrum. Our findings open up new avenues for further investigations about journalists’ intentionality and strategies for crafting the affective composition of news content.