Person:
Caballero Roldán, Rafael

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First Name
Rafael
Last Name
Caballero Roldán
Affiliation
Universidad Complutense de Madrid
Faculty / Institute
Informática
Department
Sistemas Informáticos y Computación
Area
Lenguajes y Sistemas Informáticos
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UCM identifierORCIDScopus Author IDDialnet IDGoogle Scholar ID

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  • Publication
    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
    (2022) Fernández-Zubieta, Ana; Guevara Gil, Juan Antonio; Caballero Roldán, Rafael; Robles Morales, José Manuel
    This article analyses the Fridays for Future (FFF) movement and their online mobilization around the Global Day of Climate Action on September 25th, 2020. Due to the Covid-19 pandemic this event is a unique opportunity to study digital activism as marchers were considered not appropriate. Using the Twitter’s API with keywords “#climateStrike”, “#FridaysForFuture”, we collected 111,844 unique tweets and retweets from 47,892 unique users. We use two typologies based on social media activism and framing literature to understand the main function of tweets —information, opinion, mobilization and blame— and frames —diagnosis, prognosis, motivational. We also analyze its relationship and test its automated-classification potential. To do so we manually coded a randomly selected sample of 950 tweets that are used as input for the automated-classification process (SVMs algorithm with balancing classification techniques). We find that the Covid-19 pandemic appears not to have increased the mobilization function of tweets, as the frequencies of mobilization tweets were low. We also find a balanced diversity of framing tasks, with an important number of tweets that envisaged solution on legislation and policy changes. We find that both typologies are not independent. The automated data classification model performed well, especially across social function typology and the “other” category. This indicates that these tools could help researchers working with social media data to process the information across categories that are currently mainly processed manually, enlarging their final sample sizes