Browsing by Author "Mendoza Rocha, Marcelo"

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    A Study on Information Disorders on Social Networks during the Chilean Social Outbreak and COVID-19 Pandemic
    (2023) Mendoza Rocha, Marcelo; Valenzuela Leighton, Sebastián Andrés; Núñez-Mussa, Enrique; Padilla Arenas, Fabián; Providel, Eliana; Campos, Sebastián; Bassi, Renato; Riquelme, Andrea; Aldana, Valeria; López, Claudia
    Information disorders on social media can have a significant impact on citizens’ participation in democratic processes. To better understand the spread of false and inaccurate information online, this research analyzed data from Twitter, Facebook, and Instagram. The data were collected and verified by professional fact-checkers in Chile between October 2019 and October 2021, a period marked by political and health crises. The study found that false information spreads faster and reaches more users than true information on Twitter and Facebook. Instagram, on the other hand, seemed to be less affected by this phenomenon. False information was also more likely to be shared by users with lower reading comprehension skills. True information, on the other hand, tended to be less verbose and generate less interest among audiences. This research provides valuable insights into the characteristics of misinformation and how it spreads online. By recognizing the patterns of how false information diffuses and how users interact with it, we can identify the circumstances in which false and inaccurate messages are prone to becoming widespread. This knowledge can help us to develop strategies to counter the spread of misinformation and protect the integrity of democratic processes.
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    Detection and impact estimation of social bots in the Chilean Twitter network
    (2024) Mendoza Rocha, Marcelo; Providel, Eliana; Santos, Marcelo; Valenzuela, Sebastián
    The rise of bots that mimic human behavior represents one of the most pressing threats to healthy information environments on social media. Many bots are designed to increase the visibility of low-quality content, spread misinformation, and artificially boost the reach of brands and politicians. These bots can also disrupt civic action coordination, such as by flooding a hashtag with spam and undermining political mobilization. Social media platforms have recognized these malicious bots’ risks and implemented strict policies and protocols to block automated accounts. However, effective bot detection methods for Spanish are still in their early stages. Many studies and tools used for Spanish are based on English-language models and lack performance evaluations in Spanish. In response to this need, we have developed a method for detecting bots in Spanish called Botcheck. Botcheck was trained on a collection of Spanish-language accounts annotated in Twibot-20, a large-scale dataset featuring thousands of accounts annotated by humans in various languages. We evaluated Botcheck’s performance on a large set of labeled accounts and found that it outperforms other competitive methods, including deep learning-based methods. As a case study, we used Botcheck to analyze the 2021 Chilean Presidential elections and discovered evidence of bot account intervention during the electoral term. In addition, we conducted an external validation of the accounts detected by Botcheck in the case study and found our method to be highly effective. We have also observed differences in behavior among the bots that are following the social media accounts of official presidential candidates.
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    Learning to cluster urban areas: two competitive approaches and an empirical validation
    (2022) Vera Villa, Camila; Lucchini Wortzman, Francesca; Bro, Naim; Mendoza Rocha, Marcelo; Löbel Díaz, Hans-Albert; Gutiérrez, Felipe; Dimter, Jan; Cuchacovic, Gabriel; Reyes, Axel; Valdivieso López, Hernán Felipe; Alvarado Monardez, Nicolás; Toro, Sergio
    Urban clustering detects geographical units that are internally homogeneous and distinct from their surroundings. It has applications in urban planning, but few studies compare the effectiveness of different methods. We study two techniques that represent two families of urban clustering algorithms: Gaussian Mixture Models (GMMs), which operate on spatially distributed data, and Deep Modularity Networks (DMONs), which work on attributed graphs of proximal nodes. To explore the strengths and limitations of these techniques, we studied their parametric sensitivity under different conditions, considering the spatial resolution, granularity of representation, and the number of descriptive attributes, among other relevant factors. To validate the methods, we asked residents of Santiago, Chile, to respond to a survey comparing city clustering solutions produced using the different methods. Our study shows that DMON is slightly preferred over GMM and that social features seem to be the most important ones to cluster urban areas.