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
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2023
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Fernández-Zubieta, Ana, Juan Antonio Guevara, Rafael Caballero Roldan, and José Manuel Robles. 2023. "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" Social Sciences 12, no. 12: 676. https://doi.org/10.3390/socsci12120676
Abstract
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
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