Browsing by Author "Providel, Eliana"

Now showing 1 - 6 of 6
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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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    CLNews: The First Dataset of the Chilean Social Outbreak for Disinformation Analysis
    (Association for Computing Machinery, 2022) Providel, Eliana; Toro, Daniel; Riquelme, Fabián; Mendoza Rocha, Marcelo Gabriel; Puraivan, E.
    Disinformation is one of the main threats that loom on social networks. Detecting disinformation is not trivial and requires training and maintaining fact-checking teams, which is labor-intensive. Recent studies show that the propagation structure of claims and user messages allows a better understanding of rumor dynamics. Despite these findings, the availability of verified claims and structural propagation data is low. This paper presents a new dataset with Twitter claims verified by fact-checkers along with the propagation structure of retweets and replies. The dataset contains verified claims checked during the Chilean social outbreak, which allows for studying the phenomenon of disinformation during this crisis. We study propagation patterns of verified content in CLNews, showing differences between false rumors and other types of content. Our results show that false rumors are more persistent than the rest of verified contents, reaching more people than truthful news and presenting low barriers of readability to users. The dataset is fully available and helps understand the phenomenon of disinformation during social crises being one of the first of its kind to be released.
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    Cross-Lingual Cross-Domain Transfer Learning for Rumor Detection
    (2024) Providel, Eliana; Mendoza Rocha, Marcelo Gabriel; Solar, Mauricio
    This study introduces a novel method that merges propagation-based transfer learning with word embeddings for rumor detection. This approach aims to utilize data from languages with abundant resources to enhance performance in languages with limited availability of annotated corpora in this task. Furthermore, we augment our rumor detection framework with two supplementary tasks -stance classification and bot detection- to reinforce the primary task of rumor detection. Utilizing our proposed multi-task system, we generate several pretrained models that are subsequently fine-tuned for rumor detection in English. The results indicate significant improvements over baselines, thereby empirically validating the efficacy of our proposed approach. Although a direct metric comparison is difficult, given the different datasets and techniques used in the state-of-the-art, we compare our proposal with 20 other works that target rumor detection in English. By combining stance classification and bot detection as auxiliary tasks, we achieve a Macro-F1 of 0.914. On the other hand, we achieve a Macro-F1 of 0.804 for the Spanish language. In both cases, we beat baseline results, evidencing the proposed approach's usefulness
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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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    Simulating conversations on social media with generative agent-based models
    (2025) Jeon, Min Soo; Mendoza Rocha, Marcelo; Fernández Pizarro, Miguel; Providel, Eliana; Rodríguez Bórquez, Felipe; Espina Quilodrán, Nicolás Gonzalo; Carvallo, Andrés; Abeliuk, Andrés
    Large Language Models (LLMs) can generate realistic text resembling human-produced content. However, the ability of these models to simulate conversations on social media is still less explored. To investigate the potential and limitations of simulated text in this domain, we introduce network-simulator, a system to simulate conversations on social media. First, we simulate the macro structure of a conversation using Agent-Based Modeling (ABM). The generated structure defines who interacts with whom, the type of interaction, and the agent’s stance on the topic of the conversation. Subsequently, using the simulated interaction structure, our system generates prompts conditioned on the simulation variables, producing texts that are conditioned on the parameters of the predefined structure, guiding a micro simulation process. We compare human conversations with those simulated by our system. Based on stylistic and model-based metrics, we found that our system can simulate conversations on social media in various dimensions. However, we detected differences in metrics related to the predictability of text production. Furthermore, we examine the effect of true and false framings within simulated conversations, revealing that simulated discussions surrounding false information exhibit a more negative collective sentiment bias than those based on true content.
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    The Threat of Misinformation on Journalism’s Epistemology: Exploring the Gap between Journalist’s and Audience’s Expectations when Facing Fake Content
    (2024) Núñez Mussa, Enrique; Riquelme, Andrea; Valenzuela Leighton, Sebastián Andrés; Aldana, Valeria; Padilla, Fabián; Bassi, Renato; Campos, Sebastián; Providel, Eliana; Mendoza, Marcelo
    This study analyzes the discourse of reporters, editors and audiences in focus groups and in-depth interviews, examining the expectations on journalists when facing misinformation. While both groups agree that journalistic information is critical, how this expectation is met varies. On the one hand, the audience’s way of knowing involves diverse assessments regarding valuable information; also, they are dubious about journalists’ intentions. On the other hand, journalists exhibit a limited understanding of the audience’s informational needs and encounter practical challenges in rigorously fact-checking, affecting their authority in knowledge generation. The study proposes a discussion on acknowledging their complex epistemologies to benefit mutual understanding. Doing this can establish structural support for journalistic information, contributing to trust in journalism when challenged by sources spreading misinformation.