"Digital Activism Masked-The Fridays for Future Movement and the "Global Day of Climate Action

Testing Social Function and Framing Typologies of Claims on Twitter

Bibliographic Data

ID5934278
AuthorsAna Fernández Zubieta (0000-0002-1743-2331, Universidad Complutense de Madrid, corresponding author), Juan Antonio Guevara (0000-0003-3946-3910, Universidad Complutense de Madrid), Rafael Caballero Roldan (0000-0001-8181-5532, Universidad Complutense de Madrid), Josep M Robles (0000-0003-1092-3864, Universidad Complutense de Madrid), José Manuel Robles
Year2023
Volume12
Issue12
Pages676
Publication date2023-12-06
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueSocial Sciences (JOURNAL)
Journal identifiersISSN: 2076-0760 • E-ISSN: 2076-0760
PublisherMDPI AG (PUBLISHER • IT)
DOI10.3390/socsci12120676
OpenAlexW4389438429
LanguageEN
Citations received2
References cited32

This article analyzed the Fridays for Future (FFF) movement and its online mobilization around the Global Day of Climate Action on 25 September 2020. Due to the COVID-19 pandemic, this event is a unique opportunity to study digital activism as marchers were considered not appropriate. Using Twitter's API with keywords "#climateStrike", and "#FridaysForFuture", we collected 111,844 unique tweets and retweets from 47,892 unique users. We used two typologies based on social media activism and framing literature to understand the main function of tweets (information opinion, mobilization, and blame) and their framing (diagnosis, prognosis, and motivational). We also analyzed its relationship and tested its automated classification potential. To do so we manually coded a randomly selected sample of 950 tweets that were used as input for the automated classification process (SVM algorithm with balancing classification techniques). We found that the automated classification of the COVID-19 pandemic appeared to not increase the mobilization function of tweets, as the frequencies of mobilization tweets were low. We also found a balanced diversity of framing tasks, with an important number of tweets that envisaged solutions to legislation and policy changes. COVID-related tweets were less frequently prognostically framed. We found that both typologies were not independent. Tweets with a blaming function tended to be framed in a prognostic way and therefore were related to possible solutions. The automated data classification model performed well, especially across social function typology and the "other" category. This indicated that these tools could help researchers working with social media data to process the information across categories that are currently mainly processed manually

Blame · Framing (construction · Geography · Political science · Politics · Social media · Social movement · Sociology · Typology · World Wide Web · Climate Change Communication and Perception · Computer Science · Misinformation and Its Impacts · Psychology · Social Media and Politics · Social Psychology · Artificial Intelligence

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