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Journal articles on the topic 'Botometer'

Author: Grafiati

Published: 4 June 2025

Last updated: 1 August 2025

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1

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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2

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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3

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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4

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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6

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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9

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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10

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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11

Ahmed, Wasim, Francesc López Seguí, Josep Vidal-Alaball, and Matthew S. Katz. "COVID-19 and the “Film Your Hospital” Conspiracy Theory: Social Network Analysis of Twitter Data." Journal of Medical Internet Research 22, no. 10 (2020): e22374. http://dx.doi.org/10.2196/22374.

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Background During the COVID-19 pandemic, a number of conspiracy theories have emerged. A popular theory posits that the pandemic is a hoax and suggests that certain hospitals are “empty.” Research has shown that accepting conspiracy theories increases the likelihood that an individual may ignore government advice about social distancing and other public health interventions. Due to the possibility of a second wave and future pandemics, it is important to gain an understanding of the drivers of misinformation and strategies to mitigate it. Objective This study set out to evaluate the #FilmYourH

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12

Müller, Antje. "Unentschlossen." merz | medien + erziehung 61, no. 5 (2017): 96. http://dx.doi.org/10.21240/merz/2017.5.33.

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Die Wahl ist geschafft. Demokratisch, frei und natürlich geheim – ohne Frage! Stetig begleitet von einer leise fordernden, digitalen Stimme. Gestützt durch eine nicht abreißende Informationsflut. Versorgt mit hitzigem Austausch. Nützliche ‚Informantinnen‘ und ‚Informanten‘ sind immer ganz nah, hauchen zuverlässig Meinungen und wispern visionäre Vorhaben. Spielend beantworten sie Fragen, streuen Informationen, basteln Profile, wirken sympathisch. Sie, die Anderen, wirken in unserem kleinen digital-privaten Raum vertrauensvoll. Sie geben, was wir denken gerade zu brauchen: Aufmerksamkeit, Likes,

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13

Najari, Shaghayegh, Davood Rafiei, Mostafa Salehi, and Reza Farahbakhsh. "Adversarial botometer: adversarial analysis for social bot detection." Social Network Analysis and Mining 14, no. 1 (2024). http://dx.doi.org/10.1007/s13278-024-01387-7.

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14

Yang, Kai-Cheng, Emilio Ferrara, and Filippo Menczer. "Botometer 101: social bot practicum for computational social scientists." Journal of Computational Social Science, August 20, 2022. http://dx.doi.org/10.1007/s42001-022-00177-5.

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15

Münch, Felix Victor, Cornelius Puschmann, Ben Thies, and Axel Bruns. "BOTS AMONG US: PREVALENCE, INFLUENCE, AND ROLES OF AUTOMATED ACCOUNTS IN THE GERMAN TWITTER FOLLOW NETWORK." AoIR Selected Papers of Internet Research 2019 (October 31, 2019). http://dx.doi.org/10.5210/spir.v2019i0.11011.

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Social bots are undermining trust in social media. They spread low-credibility content, fake news, and spam. However, most research is based on bots that actively share links or keywords, rather than assessing the longer-term presence of bots as an integral part of platforms. To address this gap, we present what to our knowledge is the first study that assesses the prevalence, influence, and roles of automated accounts in a Twitter follow network on a national scale. This allows us to analyse the potential impact of bots beyond the context of single events and topics. To collect a follow

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16

Haile, Yirgalem A. "The theoretical wedding of computational propaganda and information operations: Unraveling digital manipulation in conflict zones." New Media & Society, December 22, 2024. https://doi.org/10.1177/14614448241302319.

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This study explores the theoretical fusion of computational propaganda and information operations in the Tigray war, centering on algorithmic manipulation techniques. Utilizing theoretical frameworks of agenda-setting theory, framing, and information ecology, the study formulates three hypotheses. Employing a multidisciplinary approach, it integrates qualitative and quantitative methods, leveraging tools such as Twitter API (X), twerc, NVivo, Botometer, and Rstat within the Netnographic method. The analysis reveals temporal dynamics of new account infiltrations on Twitter during war, emphasizi

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17

Dehghan, Ehsan, Brenda Moon, Tobias Keller, Timothy Graham, Axel Bruns, and Daniel Angus. "INVESTIGATING BOTS AND COORDINATED INFLUENCE CAMPAIGNS IN TWITTER DISCUSSIONS OF THE 2019-20 IRAN PROTESTS." AoIR Selected Papers of Internet Research, October 5, 2020. http://dx.doi.org/10.5210/spir.v2020i0.11199.

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Twitter has been a vital platform for organizing, coordinating, and amplifying voices during protests, especially in non-democratic countries. Although it is a globally used platform for protest movements, many studies focus on English-speaking and democratic countries. We overcome this research gap by investigating Persian, English, and Arabic tweets during the 2019-2020 protests in Iran. In this work-in-progress paper, we collected approximately 5,500,000 tweets, and apply social network analysis, Botometer and qualitative analysis, to map the Twittersphere revolving around Iran protests (RQ

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18

Unlu, Ali, Sophie Truong, Nitin Sawhney, Jonas Sivelä, and Tuukka Tammi. "Long-term assessment of social amplification of risk during COVID-19: challenges to public health agencies amid misinformation and vaccine stance." Journal of Computational Social Science, March 22, 2024. http://dx.doi.org/10.1007/s42001-024-00257-8.

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AbstractThis study employs the Social Amplification of Risk Framework to investigate the stance on COVID-19 vaccines and the spread of misinformation on Twitter in Finland. Analyzing over 1.6 million tweets and manually annotating 4150 samples, the research highlights the challenges faced by the Finnish Institute for Health and Welfare (THL) in steering online vaccination communication. Using BERT models, Botometer, and additional computational methods, the study classifies text, identifies bot-like accounts, and detects malicious bots. Social network analysis further uncovers the underlying s

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Unlu, Ali, Sophie Truong, Nitin Sawhney, and Tuukka Tammi. "Unveiling the Veiled Threat: The Impact of Bots on COVID-19 Health Communication." Social Science Computer Review, September 8, 2024. http://dx.doi.org/10.1177/08944393241275641.

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This article presents the results of a comprehensive study examining the influence of bots on the dissemination of COVID-19 misinformation and negative vaccine stance on Twitter over a period of three years. The research employed a tripartite methodology: text classification, topic modeling, and network analysis to explore this phenomenon. Text classification, leveraging the Turku University FinBERT pre-trained embeddings model, differentiated between misinformation and vaccine stance detection. Bot-like Twitter accounts were identified using the Botometer software, and further analysis was im

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20

Miftahuddin, Yusup, and Muhammad Haydar Al-Ghifary. "X Bot Detection Using One-Class Classification Methods with Isolation Forest Algorithm." International Journal on Advanced Science, Engineering and Information Technology 14, no. 4 (2024). http://dx.doi.org/10.18517/ijaseit.14.4.19364.

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X bots pose a significant issue in the social media landscape, with many shared links originating from bot-like accounts. This study introduces the application of the Isolation Forest algorithm, aimed explicitly at identifying anomalies such as bots by analyzing X account details. This study utilizes a dataset that merges data from Botometer with supplementary metrics like ‘average tweets per day’ and ‘account age in days’, contributed by David Martín Gutiérrez. This approach was adopted due to the increasing difficulties accessing the X API. The dataset comprises 37,438 instances, with 25,013

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21

Antenore, Marzia, Jose Manuel Camacho Rodriguez, and Emanuele Panizzi. "A Comparative Study of Bot Detection Techniques With an Application in Twitter Covid-19 Discourse." Social Science Computer Review, April 5, 2022, 089443932110737. http://dx.doi.org/10.1177/08944393211073733.

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Bot Detection is crucial in a world where Online Social Networks (OSNs) play a pivotal role in our lives as public communication channels. This task becomes highly relevant in crises like the Covid-19 pandemic when there is a growing risk of proliferation of automated accounts designed to produce misinformation content. To address this issue, we first introduce a comparison between supervised Bot Detection models using Data Selection. The techniques used to develop the bot detection models use features such as the tweets’ metadata or accounts’ Digital Fingerprint. The techniques implemented in

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22

Suarez-Lledo, V., B. Ramos-Fiol, ME Ortega-Martin, J. Carretero-Bravo, and J. Alvarez-Galvez. "Use of Hashtags related to Covid-19 infodemics by bot accounts." European Journal of Public Health 32, Supplement_3 (2022). http://dx.doi.org/10.1093/eurpub/ckac131.172.

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Abstract Background Along with the Covid-19 pandemic we need to fight an ‘infodemic'. Some of the most widespread social media platforms such as Facebook, Instagram and Twitter have implemented policies to combat the spread of misinformation about Covid. However, the online ecosystem is still full of health myths, hoaxes, and fake news that-either consciously or unconsciously-is propagated by social media users with different purposes, messages that can lead to attitudinal and behavioral changes which might result in inadequate health decision making Methods We use Twitter Stream API to collec

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23

Ollivier, Boldrini, Passarella, and Marco. "Structural invariants and semantic fingerprints in the "ego network" of words." January 31, 2022. https://doi.org/10.5281/zenodo.5914652.

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The plos_one folder contains code and datasets necessary to reproduce the results obtained in the paper. The code is embedded in a Jupyter notebook (PLOS_Open_Data.ipynb). plos_one/ ├─ bert_vec/ ├─ botometer/ ├─ egonets/ ├─ egonets/ ├─ soft_cluster/ ├─ tokens/ ├─ topics_distrib/ ├─ topics_merge_steps/ ├─ tweets/ ├─ tweets_tokens/ ├─ PLOS_Open_Data.ipynb ├─ tokenize_tools.py ├─ README.md Four datasets were extracted from Twitter: - Journalists from the NYT - Science writers - Random users #1 - Random users #2 Data files with content derivated from these four datasets are generally prefixed resp

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24

Kilian, Ollivier, Boldrini Chiara, Passarella Andrea, and Conti Marco. "Structural invariants and semantic fingerprints in the "ego network" of words." January 31, 2022. https://doi.org/10.5281/zenodo.5932713.

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The plos_one folder contains code and datasets necessary to reproduce the results obtained in the paper. The code is embedded in a Jupyter notebook (PLOS_Open_Data.ipynb). plos_one/ ├─ bert_vec/ ├─ botometer/ ├─ egonets/ ├─ egonets/ ├─ soft_cluster/ ├─ tokens/ ├─ topics_distrib/ ├─ topics_merge_steps/ ├─ tweets/ ├─ tweets_tokens/ ├─ PLOS_Open_Data.ipynb ├─ tokenize_tools.py ├─ README.md Four datasets were extracted from Twitter: - Journalists from the NYT - Science writers - Random users #1 - Random users #2 Data files with content derivated from these four datasets are generally prefixed resp

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25

Rahman, Ashiqur, Ehsan Mohammadi, and Hamed Alhoori. "Public interest in science or bots? Selective amplification of scientific articles on Twitter." Aslib Journal of Information Management, September 26, 2024. http://dx.doi.org/10.1108/ajim-01-2024-0050.

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PurposeWith the remarkable capability to reach the public instantly, social media has become integral in sharing scholarly articles to measure public response. Since spamming by bots on social media can steer the conversation and present a false public interest in given research, affecting policies impacting the public’s lives in the real world, this topic warrants critical study and attention.Design/methodology/approachWe used the Altmetric dataset in combination with data collected through the Twitter Application Programming Interface (API) and the Botometer API. We combined the data into an

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