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Relevant bibliographies by topics / Botometer

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Academic literature on the topic 'Botometer'

Author: Grafiati

Published: 4 June 2025

Last updated: 1 August 2025

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Journal articles on the topic "Botometer"

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Tian, Jiawen, Yiting Huang, and Dingyuan Zhang. "Detection Technology of Social Robot: Based on the Interpretation of Botometer Model." Journal of Computer Science and Technology Studies 4, no. 2 (2022): 39–49. http://dx.doi.org/10.32996/jcsts.2022.4.2.6.

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In the era of Web 2.0, social media have been a significant place for democratic conversation about social or political issues. While in many major public events like the Russia-Ukraine war or U.S. Presidential election, enormous social bots were found on Twitter and Facebook, putting forward public opinion warfare. By creating the illusion of grassroots support for a certain opinion, this kind of artificial intelligence can be exploited to spread misinformation, change the public perception of political entities or even promote terrorist propaganda. As a result of that, exploiting detection t

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Rincón-Martínez, Maria-Paula. "Desinformación y bodegas en Twitter. El hallazgo de falsos positivos en Dabeiba, Colombia." Revista Mediterránea de Comunicación 13, no. 2 (2022): 171. http://dx.doi.org/10.14198/medcom.21818.

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La presente investigación analiza el contenido de la campaña de desinformación y de deslegitimización que se generó en Twitter, alrededor del hallazgo de los Falsos Positivos (ejecuciones extrajudiciales cometidas por miembros del Ejército colombiano) enterrados en el cementerio Las Mercedes en Dabeiba, Colombia y su posible relación con bodegas, mejor conocidas como tropas cibernéticas. Para ello, se realizó un análisis cualitativo a 602 publicaciones a través de las herramientas MAXQDA, Leingmotif y Botometer; lo que permitió identificar la narrativa, las categorías argumentativas, el sentim

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Alipour, Sanaz Adel, Rita Orji, and Nur Zincir-Heywood. "Behaviour and Bot Analysis on Online Social Networks." International Journal of Technology and Human Interaction 19, no. 1 (2023): 1–19. http://dx.doi.org/10.4018/ijthi.327789.

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The internet is home to a multitude of social networks that provide users with a sense of community and connection across the world. Among these, Twitter and Reddit are two of the most popular. While Twitter users follow and interact with other users (tweets), Reddit users follow and interact with communities known as subreddits. In addition to mainstream social networks, alternative platforms such as Parler exist for users who prefer less moderated online environments. However, there are also malicious users, such as bots and trolls, who exploit social networks for malicious purposes. Therefo

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Lourenço, Ramon Fernandes. "Bots sociais: Uma controvérsia sociotécnica." V!RUS Journal 2, no. 29 (2024): 63–71. https://doi.org/10.11606/2175-974x.virus.v29.229585.

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The rise of far-right groups in Latin American and Caribbean countries reveals the use of strategies to manipulate public discussions, including the massive use of fake profiles on social media. Therefore, using case study methodology, this article aims to analyze the concept of social bots by describing the main initiatives for detecting automated profiles on X, formerly Twitter. By using the Actor-Network Theory, it was possible to uncover the complexity involved in defining a computerized profile, pointing to the need to establish an umbrella concept that encompasses practices such as autom

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5

Martini, Franziska, Paul Samula, Tobias R. Keller, and Ulrike Klinger. "Bot, or not? Comparing three methods for detecting social bots in five political discourses." Big Data & Society 8, no. 2 (2021): 205395172110335. http://dx.doi.org/10.1177/20539517211033566.

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Social bots – partially or fully automated accounts on social media platforms – have not only been widely discussed, but have also entered political, media and research agendas. However, bot detection is not an exact science. Quantitative estimates of bot prevalence vary considerably and comparative research is rare. We show that findings on the prevalence and activity of bots on Twitter depend strongly on the methods used to identify automated accounts. We search for bots in political discourses on Twitter, using three different bot detection methods: Botometer, Tweetbotornot and “heavy autom

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Zhang, Yaming, Wenjie Song, Jiang Shao, et al. "Social Bots’ Role in the COVID-19 Pandemic Discussion on Twitter." International Journal of Environmental Research and Public Health 20, no. 4 (2023): 3284. http://dx.doi.org/10.3390/ijerph20043284.

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Social bots have already infiltrated social media platforms, such as Twitter, Facebook, and so on. Exploring the role of social bots in discussions of the COVID-19 pandemic, as well as comparing the behavioral differences between social bots and humans, is an important foundation for studying public health opinion dissemination. We collected data on Twitter and used Botometer to classify users into social bots and humans. Machine learning methods were used to analyze the characteristics of topic semantics, sentiment attributes, dissemination intentions, and interaction patterns of humans and s

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7

Ruiz-Núñez, Carlos, Sergio Segado-Fernández, Beatriz Jiménez-Gómez, et al. "Bots’ Activity on COVID-19 Pro and Anti-Vaccination Networks: Analysis of Spanish-Written Messages on Twitter." Vaccines 10, no. 8 (2022): 1240. http://dx.doi.org/10.3390/vaccines10081240.

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This study aims to analyze the role of bots in the dissemination of health information, both in favor of and opposing vaccination against COVID-19. Study design: An observational, retrospective, time-limited study was proposed, in which activity on the social network Twitter was analyzed. Methods: Data related to pro-vaccination and anti-vaccination networks were compiled from 24 December 2020 to 30 April 2021 and analyzed using the software NodeXL and Botometer. The analyzed tweets were written in Spanish, including keywords that allow identifying the message and focusing on bots’ activity an

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8

Castillo, Cárdenas Sandra Paulina, and Camacho Francisco Jacob Avila. "Redes Neuronales Recurrentes para la detección de noticias falsas cuentas Bot en Twitter." RICT Revista de Investigación Científica, Tecnológica e Innovación 2, no. 4 (2024): 64–71. https://doi.org/10.5281/zenodo.14194198.

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Este artículo presenta el desarrollo e implementación de un modelo de Inteligencia Artificial (IA) para la detección de cuentas bot y noticias falsas en Twitter (ahora X). El modelo combina técnicas de Análisis de Sentimiento (AS), Procesamiento de Lenguaje Natural (PLN), Redes Neuronales Simples (RNS) y Redes Neuronales Recurrentes (RNR), diseñadas para identificar bots a nivel de tweet y diferenciar entre noticias verdaderas y falsas. El objetivo principal es proporcionar un sistema capaz de detectar cuentas operadas por bots de manera rápida

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ABBOOD, A. M. W. "AI JOURNALISM TOOLS IN COUNTERACTING FAKE REALITIES." Sign problematic field in mediaeducation 54, no. 4 (2024): 31–38. https://doi.org/10.47475/2070-0695-2024-54-4-31-38.

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The article examines the role of journalism in countering fake reality through the use of artificial intelligence (AI) technologies. It analyzes the current state of AI tools and technologies that enable the automation of the fact-checking process and improve the quality of news content in the media. A brief overview of domestic and international publications on this topic is provided. The article indicates such Internet services as ClaimBuster, Snopes, PolitiFact, Factmata, Google Fact Check Tools, Botometer, DeepFact, Project Debater and others. Many of these have already become effective as

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S, Varsha, P. S. Prathiba, N. Deepika, Janghel Neha, and V. Ashoka D. "Performance Analysis of Machine Learning Algorithms in SMP: A Case Study of Twitter." Journal of Computer Science Engineering and Software Testing 5, no. 2 (2019): 17–22. https://doi.org/10.5281/zenodo.3268439.

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<em>The number of people using Social Media Platform (SMP) is increasing day by day. A few users may hide their identity with malicious intentions. Previous research has detected fake accounts created by bots using machine learning concepts. These ML concepts used engineered features such as the ‘following-to-followers ratio’ which is generally available in their accounts. In previous studies these similarly clustered features were applied to the machine learning models for detection of fake and real accounts. In the recent research the behavioural features like the sentient of the

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Book chapters on the topic "Botometer"

1

Gallwitz, Florian, and Michael Kreil. "Investigating the Validity of Botometer-Based Social Bot Studies." In Disinformation in Open Online Media. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-18253-2_5.

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Conference papers on the topic "Botometer"

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Torusdag, M. Bugra, Mucahid Kutlu, and Ali Aydin Selcuk. "Are We Secure from Bots? Investigating Vulnerabilities of Botometer." In 2020 5th International Conference on Computer Science and Engineering (UBMK). IEEE, 2020. http://dx.doi.org/10.1109/ubmk50275.2020.9219433.

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