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Journal articles on the topic 'Twitter bot detection'

Author: Grafiati

Published: 2 June 2025

Last updated: 31 July 2025

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1

Rovito, Luigi, Lorenzo Bonin, Luca Manzoni, and Andrea De Lorenzo. "An Evolutionary Computation Approach for Twitter Bot Detection." Applied Sciences 12, no. 12 (2022): 5915. http://dx.doi.org/10.3390/app12125915.

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Bot accounts are automated software programs that act as legitimate human profiles on social networks. Identifying these kinds of accounts is a challenging problem due to the high variety and heterogeneity that bot accounts exhibit. In this work, we use genetic algorithms and genetic programming to discover interpretable classification models for Twitter bot detection with competitive qualitative performance, high scalability, and good generalization capabilities. Specifically, we use a genetic programming method with a set of primitives that involves simple mathematical operators. This enable

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Ramalingaiah, A., S. Hussaini, and S. Chaudhari. "Twitter bot detection using supervised machine learning." Journal of Physics: Conference Series 1950, no. 1 (2021): 012006. http://dx.doi.org/10.1088/1742-6596/1950/1/012006.

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Daouadi, Kheir, Rim Rebaï, and Ikram Amous. "Real-Time Bot Detection from Twitter Using the Twitterbot+ Framework." JUCS - Journal of Universal Computer Science 26, no. 4 (2020): 496–507. http://dx.doi.org/10.3897/jucs.2020.026.

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Nowadays, bot detection from Twitter attracts the attention of several researchers around the world. Different bot detection approaches have been proposed as a result of these research efforts. Four of the main challenges faced in this context are the diversity of types of content propagated throughout Twitter, the problem inherent to the text, the lack of sufficient labeled datasets and the fact that the current bot detection approaches are not sufficient to detect bot activities accurately. We propose, Twitterbot+, a bot detection system that leveraged a minimal number of language-independen

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Daouadi, Kheir, Rim Rebaï, and Ikram Amous. "Real-Time Bot Detection from Twitter Using the Twitterbot+ Framework." JUCS - Journal of Universal Computer Science 26, no. (4) (2020): 496–507. https://doi.org/10.3897/jucs.2020.026.

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Nowadays, bot detection from Twitter attracts the attention of several researchers around the world. Different bot detection approaches have been proposed as a result of these research efforts. Four of the main challenges faced in this context are the diversity of types of content propagated throughout Twitter, the problem inherent to the text, the lack of sufficient labeled datasets and the fact that the current bot detection approaches are not sufficient to detect bot activities accurately. We propose, Twitterbot+, a bot detection system that leveraged a minimal number of language-independen

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Kislaia, A. G., L. E. Chala, and O. Y. Grynova. "Bot detection in social networks." Bionics of Intelligence 1, no. 90 (2018): 91–96. https://doi.org/10.30837/bi.2018.1(90).13.

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The analysis of types of social bots was performed, their influence on users was revealed. The signs by which it is possible to identify bots are described. The algorithms for the distribution of information by social networks are analyzed. A neural network architecture was proposed to identify bots, and the results of its work were provided for analyzing Twitter users and their tweets.

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Assenmacher, Dennis, Leon Fröhling, and Claudia Wagner. "You Are a Bot! – Studying the Development of Bot Accusations on Twitter." Proceedings of the International AAAI Conference on Web and Social Media 18 (May 28, 2024): 113–25. http://dx.doi.org/10.1609/icwsm.v18i1.31301.

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The characterization and detection of bots with their presumed ability to manipulate society on social media platforms have been subject to many research endeavors over the last decade. In the absence of ground truth data (i.e., accounts that are labeled as bots by experts or self-declare their automated nature), researchers interested in the characterization and detection of bots may want to tap into the wisdom of the crowd. But how many people need to accuse another user as a bot before we can assume that the account is most likely automated? And more importantly, are bot accusations on soci

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Varol, Onur, Emilio Ferrara, Clayton Davis, Filippo Menczer, and Alessandro Flammini. "Online Human-Bot Interactions: Detection, Estimation, and Characterization." Proceedings of the International AAAI Conference on Web and Social Media 11, no. 1 (2017): 280–89. http://dx.doi.org/10.1609/icwsm.v11i1.14871.

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Increasing evidence suggests that a growing amount of social media content is generated by autonomous entities known as social bots. In this work we present a framework to detect such entities on Twitter. We leverage more than a thousand features extracted from public data and meta-data about users: friends, tweet content and sentiment, network patterns, and activity time series. We benchmark the classification framework by using a publicly available dataset of Twitter bots. This training data is enriched by a manually annotated collection of active Twitter users that include both humans and b

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Feng, Shangbin, Zhaoxuan Tan, Rui Li, and Minnan Luo. "Heterogeneity-Aware Twitter Bot Detection with Relational Graph Transformers." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 4 (2022): 3977–85. http://dx.doi.org/10.1609/aaai.v36i4.20314.

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Twitter bot detection has become an important and challenging task to combat misinformation and protect the integrity of the online discourse. State-of-the-art approaches generally leverage the topological structure of the Twittersphere, while they neglect the heterogeneity of relations and influence among users. In this paper, we propose a novel bot detection framework to alleviate this problem, which leverages the topological structure of user-formed heterogeneous graphs and models varying influence intensity between users. Specifically, we construct a heterogeneous information network with

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Priyatno, Arif Mudi, Muhammad Mirza Muttaqi, Fahmi Syuhada, and Agus Zainal Arifin. "Deteksi Bot Spammer Twitter Berbasis Time Interval Entropy dan Global Vectors for Word Representations Tweet’s Hashtag." Register: Jurnal Ilmiah Teknologi Sistem Informasi 5, no. 1 (2019): 37. http://dx.doi.org/10.26594/register.v5i1.1382.

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Bot spammer merupakan penyalahgunaan user dalam menggunakan Twitter untuk menyebarkan pesan spam sesuai dengan keinginan user. Tujuan spam mencapai trending topik yang ingin dibuatnya. Penelitian ini mengusulkan deteksi bot spammer pada Twitter berbasis Time Interval Entropy dan global vectors for word representations (Glove). Time Interval Entropy digunakan untuk mengklasifikasi akun bot berdasarkan deret waktu pembuatan tweet. Glove digunakan untuk melihat co-occurrence kata tweet yang disertai Hashtag untuk proses klasifikasi menggunakan Convolutional Neural Network (CNN). Penelitian ini me

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Al-azawi, Raad, and Safaa O. AL-mamory. "Feature extractions and selection of bot detection on Twitter A systematic literature review." Inteligencia Artificial 25, no. 69 (2022): 57–86. http://dx.doi.org/10.4114/intartif.vol25iss69pp57-86.

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Abstract Automated or semiautomated computer programs that imitate humans and/or human behavior in online social networks are known as social bots. Users can be attacked by social bots to achieve several hidden aims, such as spreading information or influencing targets. While researchers develop a variety of methods to detect social media bot accounts, attackers adapt their bots to avoid detection. This field necessitates ongoing growth, particularly in the areas of feature selection and extraction. The study's purpose is to provide an overview of bot attacks on Twitter, shedding light on issu

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Samper-Escalante, Luis Daniel, Octavio Loyola-González, Raúl Monroy, and Miguel Angel Medina-Pérez. "Bot Datasets on Twitter: Analysis and Challenges." Applied Sciences 11, no. 9 (2021): 4105. http://dx.doi.org/10.3390/app11094105.

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The reach and influence of social networks over modern society and its functioning have created new challenges and opportunities to prevent the misuse or tampering of such powerful tools of social interaction. Twitter, a social networking service that specializes in online news and information exchange involving billions of users world-wide, has been infested by bots for several years. In this paper, we analyze both public and private databases from the literature of bot detection on Twitter. We summarize their advantages, disadvantages, and differences, recommending which is more suitable to

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Schuchard, Ross J., and Andrew T. Crooks. "Insights into elections: An ensemble bot detection coverage framework applied to the 2018 U.S. midterm elections." PLOS ONE 16, no. 1 (2021): e0244309. http://dx.doi.org/10.1371/journal.pone.0244309.

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The participation of automated software agents known as social bots within online social network (OSN) engagements continues to grow at an immense pace. Choruses of concern speculate as to the impact social bots have within online communications as evidence shows that an increasing number of individuals are turning to OSNs as a primary source for information. This automated interaction proliferation within OSNs has led to the emergence of social bot detection efforts to better understand the extent and behavior of social bots. While rapidly evolving and continually improving, current social bo

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Olagunju, Afeez Ayomide, and Iyabo Olukemi Awoyelu. "Performance Evaluation of Fake News Detection Models." International Journal of Information Technology and Computer Science 16, no. 6 (2024): 89–100. https://doi.org/10.5815/ijitcs.2024.06.07.

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The rapid spread of misinformation on social media platforms, especially Twitter, presents a challenge in the digital age. Traditional fact-checking struggles with the volume and speed of misinformation, while existing detection systems often focus solely on linguistic features, ignoring factors like source credibility, user interactions, and context. Current automated systems also lack the accuracy to differentiate between genuine and fake news, resulting in high rates of false positives and negatives. This study investigates the creation of a Twitter bot for detecting fake news using deep le

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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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Darem, Abdulbasit A., Asma A. Alhashmi, Meshari H. Alanazi, et al. "Cybersecurity in social networks: An ensemble model for Twitter bot detection." International Journal of ADVANCED AND APPLIED SCIENCES 11, no. 11 (2024): 130–41. http://dx.doi.org/10.21833/ijaas.2024.11.014.

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The increasing presence of bot accounts on social media platforms creates major challenges for ensuring truthful and reliable online communication. This study examines how well ensemble learning techniques can identify bot accounts on Twitter. Using a dataset from Kaggle, which provides detailed information about accounts and labels them as either bot or human, we applied and tested several machine learning methods, including logistic regression, decision trees, random forests, XGBoost, support vector machines, and multi-layer perceptrons. The ensemble model, which merges predictions from indi

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Al-azawi, Raad, and Safaa O. AL-mamory. "Unsupervised Machine Learning for Bot Detection on Twitter: Generating and Selecting Features for Accurate Clustering." Inteligencia Artificial 27, no. 73 (2024): 142–58. http://dx.doi.org/10.4114/intartif.vol27iss73pp142-158.

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Twitter is a popular social media platform that is widely used by individuals and businesses. However, it is vulnerable to bot attacks, which can have negative effects on society. Supervised machine learning techniques can detect bots but require labeled data to differentiate between human and bot users. Twitter generates a significant amount of unlabeled data, which can be expensive to label. Unsupervised machine learning techniques, specifically clustering algorithms, are crucial for managing this data and reducing computational complexity. Effective feature selection is necessary for cluste

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Alarfaj, Fawaz Khaled, Hassaan Ahmad, Hikmat Ullah Khan, Abdullah Mohammaed Alomair, Naif Almusallam, and Muzamil Ahmed. "Twitter Bot Detection Using Diverse Content Features and Applying Machine Learning Algorithms." Sustainability 15, no. 8 (2023): 6662. http://dx.doi.org/10.3390/su15086662.

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A social bot is an intelligent computer program that acts like a human and carries out various activities in a social network. A Twitter bot is one of the most common forms of social bots. The detection of Twitter bots has become imperative to draw lines between real and unreal Twitter users. In this research study, the main aim is to detect Twitter bots based on diverse content-specific feature sets and explore the use of state-of-the-art machine learning classifiers. The real-world data from Twitter is scrapped using Twitter API and is pre-processed using standard procedure. To analyze the c

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Fu, Chengqi, Shuhao Shi, Yuxin Zhang, et al. "SqueezeGCN: Adaptive Neighborhood Aggregation with Squeeze Module for Twitter Bot Detection Based on GCN." Electronics 13, no. 1 (2023): 56. http://dx.doi.org/10.3390/electronics13010056.

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Despite notable advancements in bot detection methods based on Graph Neural Networks (GNNs). The efficacy of Graph Neural Networks relies heavily on the homophily assumption, which posits that nodes with the same label are more likely to form connections between them. However, the latest social bots are capable of concealing themselves by extensively interacting with authentic user accounts, forging extensive connections on social graphs, and thus deviating from the homophily assumption. Consequently, conventional Graph Neural Network methods continue to face significant challenges in detectin

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Joseph, Jyothis. "Twitter Bot Detection Using Machine Learning and Deep Learning Techniques." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 05 (2025): 1–9. https://doi.org/10.55041/ijsrem47401.

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Abstract—The proliferation of Twitter bots poses a serious threat to the reliability of online conversations and results in disinformation, spam, and opinion manipulation. This paper presents a comprehensive examination of Twitter bot detection techniques with traditional machine learning (ML) algorithms contrasted with cutting-edge deep learning (DL) models. Key fea- tures like tweet frequency, follower-following ratios, user behavior patterns, and content features are investigated. We compare algorithms like Random Forest, Support Vector Machines (SVM), Logistic Regression, K-Nearest Neighbo

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Loyola-Gonzalez, Octavio, Raul Monroy, Jorge Rodriguez, Armando Lopez-Cuevas, and Javier Israel Mata-Sanchez. "Contrast Pattern-Based Classification for Bot Detection on Twitter." IEEE Access 7 (2019): 45800–45817. http://dx.doi.org/10.1109/access.2019.2904220.

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Hui, Pik-Mai, Kai-Cheng Yang, Christopher Torres-Lugo, et al. "BotSlayer: real-time detection of bot amplification on Twitter." Journal of Open Source Software 4, no. 42 (2019): 1706. http://dx.doi.org/10.21105/joss.01706.

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Dhanesh, Arya, Jyothika K, Malavika Jayaraj, Nevin Jose Antony, and Anu Treesa George. "Comprehensive Strategies for Identifying X(Twitter) Bots." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 04 (2025): 1–9. https://doi.org/10.55041/ijsrem43583.

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Twitter is a social network where users interact via text-based posts called tweets, using hashtags, mentions, shortened URLs, and retweets. The growing user base and open nature of Twitter have made it a target for automated programs, known as bots, which can be both beneficial and malicious. This research focuses on detecting and classifying Twitter accounts as human, bot, or cyborg. Given Twitter’s open nature, both helpful and harmful bots are prevalent, necessi- tating effective detection strategies. The study analyzes account behavior, content, and properties, introducing a classificatio

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Zahra, Aqilah Aini, Widyawan Widyawan, and Silmi Fauziati. "Development of Bot Detection Applications on Twitter Social Media Using Machine Learning with a Random Forest Classifier Algorithm." IJITEE (International Journal of Information Technology and Electrical Engineering) 4, no. 2 (2020): 66. http://dx.doi.org/10.22146/ijitee.56154.

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A Twitter bot is a Twitter account programmed to automatically do social activities by sending tweets through a scheduling program. Some bots intend to disseminate useful information such as earthquake and weather information. However, not a few bots have a negative influence, such as broadcasting false news, spam, or become a follower to increase an account's popularity. It can change public sentiments about an issue, decrease user confidence, or even change the social order. Therefore, an application is needed to distinguish between a bot and non-bot accounts. Based on these problems, this p

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Dimitriadis, Ilias, Konstantinos Georgiou, and Athena Vakali. "Social Botomics: A Systematic Ensemble ML Approach for Explainable and Multi-Class Bot Detection." Applied Sciences 11, no. 21 (2021): 9857. http://dx.doi.org/10.3390/app11219857.

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OSN platforms are under attack by intruders born and raised within their own ecosystems. These attacks have multiple scopes from mild critiques to violent offences targeting individual or community rights and opinions. Negative publicity on microblogging platforms, such as Twitter, is due to the infamous Twitter bots which highly impact posts’ circulation and virality. A wide and ongoing research effort has been devoted to develop appropriate countermeasures against emerging “armies of bots”. However, the battle against bots is still intense and unfortunately, it seems to lean on the bot-side.

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Alsubaei, Faisal S. "Detection of Inappropriate Tweets Linked to Fake Accounts on Twitter." Applied Sciences 13, no. 5 (2023): 3013. http://dx.doi.org/10.3390/app13053013.

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It is obvious that one of the most significant challenges posed by Twitter is the proliferation of fraudulent and fake accounts, as well as the challenge of identifying these accounts. As a result, the primary focus of this paper is on the identification of fraudulent accounts, fake information, and fake accounts on Twitter, in addition to the flow of content that these accounts post. The research utilized a design science methodological approach and developed a bot account referred to as “Fake Account Detector” that assists with the detection of inappropriate posts that are associated with fa

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Stoica, Stefania Elena. "Adapting Bot Detection Models for Romania’s Disinformation Ecosystem." European Conference on Cyber Warfare and Security 24, no. 1 (2025): 811–19. https://doi.org/10.34190/eccws.24.1.3573.

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The proliferation of social media bots and fake accounts has significantly disrupted information ecosystems, posing substantial challenges in detecting and mitigating disinformation. While machine learning and deep learning models have shown varying levels of success on platforms like Twitter and Facebook, they often fail to account for region-specific nuances critical for effective bot detection. Facebook and Twitter have been widely used in disinformation research due to their large user bases and historically open API access, facilitating large-scale data collection. This study addresses th

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Mr. S.V Hemanth, S Sneha Reddy, R Nithin, G Keerthi, and Shinde Vinayak Rao Patil. "Automated Bot Detection on Twitter UsingURL Patterns and Learning Automata." international journal of engineering technology and management sciences 8, no. 3 (2024): 205–10. http://dx.doi.org/10.46647/ijetms.2024.v08i03.025.

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The fight against fake news and propaganda on social media becomes increasinglydifficult as malicious bots impersonate real users. These imposters spread misinformation throughcompromised or inauthentic accounts, often tricking users with shortened URLs that contain virusesand lead to malicious websites. Therefore, distinguishing between these bots and genuine Twitterusers is crucial. Analyzing user interactions within the social network can be time-consuming. Thisresearch proposes a more efficient approach: LA-MSBD, an algorithm that relies on learningautomata. LA-MSBD focuses on URL-based fe

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Rodríguez-Ruiz, Jorge, Javier Israel Mata-Sánchez, Raúl Monroy, Octavio Loyola-González, and Armando López-Cuevas. "A one-class classification approach for bot detection on Twitter." Computers & Security 91 (April 2020): 101715. http://dx.doi.org/10.1016/j.cose.2020.101715.

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Ng, Lynnette Hui Xian, and Kathleen M. Carley. "BotBuster: Multi-Platform Bot Detection Using a Mixture of Experts." Proceedings of the International AAAI Conference on Web and Social Media 17 (June 2, 2023): 686–97. http://dx.doi.org/10.1609/icwsm.v17i1.22179.

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Despite rapid development, current bot detection models still face challenges in dealing with incomplete data and cross-platform applications. In this paper, we propose BotBuster, a social bot detector built with the concept of a mixture of experts approach. Each expert is trained to analyze a portion of account information, e.g. username, and are combined to estimate the probability that the account is a bot. Experiments on 10 Twitter datasets show that BotBuster outperforms popular bot-detection baselines (avg F1=73.54 vs avg F1=45.12). This is accompanied with F1=60.04 on a Reddit dataset a

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Shevtsov, Alexander, Christos Tzagkarakis, Despoina Antonakaki, and Sotiris Ioannidis. "Identification of Twitter Bots Based on an Explainable Machine Learning Framework: The US 2020 Elections Case Study." Proceedings of the International AAAI Conference on Web and Social Media 16 (May 31, 2022): 956–67. http://dx.doi.org/10.1609/icwsm.v16i1.19349.

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Twitter is one of the most popular social networks attracting millions of users, while a considerable proportion of online discourse is captured. It provides a simple usage framework with short messages and an efficient application programming interface (API) enabling the research community to study and analyze several aspects of this social network. However, the Twitter usage simplicity can lead to malicious handling by various bots. The malicious handling phenomenon expands in online discourse, especially during the electoral periods, where except the legitimate bots used for dissemination a

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Yang, Kai-Cheng, Onur Varol, Pik-Mai Hui, and Filippo Menczer. "Scalable and Generalizable Social Bot Detection through Data Selection." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 01 (2020): 1096–103. http://dx.doi.org/10.1609/aaai.v34i01.5460.

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Efficient and reliable social bot classification is crucial for detecting information manipulation on social media. Despite rapid development, state-of-the-art bot detection models still face generalization and scalability challenges, which greatly limit their applications. In this paper we propose a framework that uses minimal account metadata, enabling efficient analysis that scales up to handle the full stream of public tweets of Twitter in real time. To ensure model accuracy, we build a rich collection of labeled datasets for training and validation. We deploy a strict validation system so

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Yang, Zhou, Xingshu Chen, Haizhou Wang, Wenxian Wang, Zhenxiong Miao, and Tao Jiang. "A New Joint Approach with Temporal and Profile Information for Social Bot Detection." Security and Communication Networks 2022 (May 7, 2022): 1–14. http://dx.doi.org/10.1155/2022/9119388.

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With the increasing popularity of online social networks (OSNs), a huge number of social bots have emerged. Social bots are involved in various cybercrimes like cyberbullying and rumor dissemination, which have seriously affected the normal order of OSNs. Nowadays, existing studies in this field almost focus on English OSNs like Twitter and Facebook. However, it is difficult to directly apply these detection technologies to Sina Weibo, which is one of the largest Chinese microblogging services in the world. In addition, social bots are evolving rapidly and time-consuming feature engineering ma

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Kim, Taehyun, Hyomin Shin, Hyung Ju Hwang, and Seungwon Jeong. "Posting Bot Detection on Blockchain-based Social Media Platform using Machine Learning Techniques." Proceedings of the International AAAI Conference on Web and Social Media 15 (May 22, 2021): 303–14. http://dx.doi.org/10.1609/icwsm.v15i1.18062.

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Steemit is a blockchain-based social media platform, where authors can get author rewards in the form of cryptocurrencies called STEEM and SBD (Steem Blockchain Dollars) if their posts are upvoted. Interestingly, curators (or voters) can also get rewards by voting others' posts, which is called a curation reward. A reward is proportional to a curator's STEEM stakes. Throughout this process, Steemit hopes "good" content will be automatically discovered by users in a decentralized way, which is known as the Proof-of-Brain (PoB). However, there are many bot accounts programmed to post automatical

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Alothali, Eiman, Motamen Salih, Kadhim Hayawi, and Hany Alashwal. "Bot-MGAT: A Transfer Learning Model Based on a Multi-View Graph Attention Network to Detect Social Bots." Applied Sciences 12, no. 16 (2022): 8117. http://dx.doi.org/10.3390/app12168117.

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Twitter, as a popular social network, has been targeted by different bot attacks. Detecting social bots is a challenging task, due to their evolving capacity to avoid detection. Extensive research efforts have proposed different techniques and approaches to solving this problem. Due to the scarcity of recently updated labeled data, the performance of detection systems degrades when exposed to a new dataset. Therefore, semi-supervised learning (SSL) techniques can improve performance, using both labeled and unlabeled examples. In this paper, we propose a framework based on the multi-view graph

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Kaur Kochhar, Sarabjeet, and Chinmay Chahar. "Performing Stance Classification and Bot Detection on the Indian Farmers’ Protest – A Study to Unveil Hidden Perspectives." Advances in Artificial Intelligence and Machine Learning 03, no. 04 (2023): 1619–39. http://dx.doi.org/10.54364/aaiml.2023.1192.

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The presence of illegal, harmful content, rumors, misinformation, and Twitter bots has consistently brought the social media platforms such as Twitter into the spotlight. Therefore, it is advisable to exercise caution when analyzing tweets. To establish the credibility of any patterns and findings derived from tweets, it is essential to thoroughly investigate the source and authenticity of the tweets in question. This paper advances in this direction by introducing a novel approach involving bot detection and a comparative analysis of human and botgenerated tweets related to the farmers’ prote

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D, Miss Takale Jyoti. "Analysis and Detection of Bot performing Keylogging Activities." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 13, no. 6 (2014): 4549–55. http://dx.doi.org/10.24297/ijct.v13i6.2517.

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The focus on computer security has increased due to the ubiquitous use of Internet. Botnets are one of the biggest cyber threats. Botnet is a malware controlled by a Botmaster using Command and Control (C&C). Botnet is expanded with infecting fresh computers through social networking sites like facebook, twitter, etc. ZeuS is famous type of botnet for financial gain. It targets bank websites for stealing userâ€™s credentials like password, credit card information,etc. In this paper, an application framework is designed for analysis and detection of ZeuS bot residing on host victimâ€™s mach

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N. Ezhil Arasi, Dr. G Manikandan, Ms. S. Hemalatha, and Ms. Vilma Veronica. "Malicious Social Bot Using Twitter Network Analysis in Django." International Journal of Scientific Research in Science and Technology 11, no. 2 (2024): 114–13. http://dx.doi.org/10.32628/ijsrst52411222.

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Malicious social bots generate fake tweets and automate their social relationships either by pretending to be a followers or by creating multiple fake accounts with malicious activities. Moreover, malicious social bots post shortened malicious URLs in the tweets to redirect the requests of online social networking participants to some malicious servers. Hence, distinguishing malicious social bots from legitimate users is one of the most important tasks in the Twitter network. To detect malicious social bots, extracting URL-based features (such as URL redirection, frequency of shared URLs, and

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Alothali, Eiman, Kadhim Hayawi, and Hany Alashwal. "SEBD: A Stream Evolving Bot Detection Framework with Application of PAC Learning Approach to Maintain Accuracy and Confidence Levels." Applied Sciences 13, no. 7 (2023): 4443. http://dx.doi.org/10.3390/app13074443.

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A simple supervised learning model can predict a class from trained data based on the previous learning process. Trust in such a model can be gained through evaluation measures that ensure fewer misclassification errors in prediction results for different classes. This can be applied to supervised learning using a well-trained dataset that covers different data points and has no imbalance issues. This task is challenging when it integrates a semi-supervised learning approach with a dynamic data stream, such as social network data. In this paper, we propose a stream-based evolving bot detection

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Gera, Suruchi, and Adwitiya Sinha. "A machine learning-based malicious bot detection framework for trend-centric twitter stream." Journal of Discrete Mathematical Sciences and Cryptography 24, no. 5 (2021): 1337–48. http://dx.doi.org/10.1080/09720529.2021.1932923.

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Sumathi, Dr P., Dhakshinya Marudhavanan, M. Raghul, S. Rajarajan, and S. Sivasaamy. "An Enhanced System to Detect Cyberbullying and Automate Reporting on Twitter Using Text Based Pattern Recognition Technique." International Journal for Research in Applied Science and Engineering Technology 12, no. 4 (2024): 2730–34. http://dx.doi.org/10.22214/ijraset.2024.60284.

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Abstract: The increasing prevalence of cyberbullying on social media platforms necessitates effective detection and response mechanisms. This paper presents an enhanced system for detecting cyberbullying directed at politicians on Twitter and automating the reporting process. Utilizing advanced text-based pattern recognition techniques, the systePm identifies potentially harmful content and automatically reports it to a designated bot account for further action. We detail the system's architecture, the machine learning algorithms employed, and the performance of the system in terms of accuracy

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Taufik, Risqa, Risti Jimah, and Achmad Solichin. "Implementasi dan Analisis Model Machine Learning Decision Tree untuk Deteksi Akun Palsu di Twitter." JURNAL MEDIA INFORMATIKA BUDIDARMA 8, no. 2 (2024): 797. http://dx.doi.org/10.30865/mib.v8i2.7548.

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In this digital era, social media platforms have become an integral part of daily life, facilitating social interaction, information exchange, and participation in public discussions. However, the emergence of bot or fake accounts on social media, especially Twitter, has posed a new challenge. These accounts are often used to disseminate inaccurate or misleading information, which can negatively impact social and political dynamics. This research focuses on the problem of detecting fake accounts on Twitter. We conducted an in-depth analysis of user profiles and their posting behavior. We colle

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Zrar Ghafoor, Kayhan. "Social Bot Detection using Machine Learning Algorithms: A Survey and Research Challenges." Polytechnic Journal 12, no. 2 (2023): 219–28. https://doi.org/10.25156/ptj.v12n2y2022.pp219-228.

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In the past decade social media platforms growing rapidly and they are part of our routine life. Each platform has its own specification which uses for specific purposes. After this widely spread, those SMPs were targeted by the cybercriminals to cast their malicious activities. There are many different malicious activities in SMPs such as spamming, phishing, fake account. In these papers, Bots activities in SMPs one of those threats which include fake accounts, fake friends/followers, spreading misinformation by purpose, and many more. At the beginning of our work, we explain all terminology

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Chang, Ying-Ying, Wei-Yao Wang, and Wen-Chih Peng. "SeGA: Preference-Aware Self-Contrastive Learning with Prompts for Anomalous User Detection on Twitter." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 1 (2024): 30–37. http://dx.doi.org/10.1609/aaai.v38i1.27752.

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In the dynamic and rapidly evolving world of social media, detecting anomalous users has become a crucial task to address malicious activities such as misinformation and cyberbullying. As the increasing number of anomalous users improves the ability to mimic normal users and evade detection, existing methods only focusing on bot detection are ineffective in terms of capturing subtle distinctions between users. To address these challenges, we proposed SeGA, preference-aware self-contrastive learning for anomalous user detection, which leverages heterogeneous entities and their relations in the

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Qiao, Boyu, Kun Li, Wei Zhou, Shilong Li, Qianqian Lu, and Songlin Hu. "BotSim: LLM-Powered Malicious Social Botnet Simulation." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 13 (2025): 14377–85. https://doi.org/10.1609/aaai.v39i13.33575.

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Social media platforms like X(Twitter) and Reddit are vital to global communication. However, advancements in Large Language Model (LLM) technology give rise to social media bots with unprecedented intelligence. These bots adeptly simulate human profiles, conversations, and interactions, disseminating large amounts of false information and posing significant challenges to platform regulation. To better understand and counter these threats, we innovatively design BotSim, a malicious social botnet simulation powered by LLM. BotSim mimics the information dissemination patterns of real-world socia

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M Nithin, Meer Eshak Ahammad, Nichenametla Shashank, Shaik Hyder Ali, and K.Mudduswamy. "Detection of Malicious Social Bots Using Learning Automata with URL Features in Twitter Network." International Journal of Scientific Research in Computer Science, Engineering and Information Technology 11, no. 3 (2025): 261–66. https://doi.org/10.32628/cseit2511317.

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With the rapid growth of social media platforms like Twitter, malicious social bots have become a significant threat, capable of manipulating public opinion, spreading misinformation, and launching cyber-attacks. These bots often mimic human behavior, making their detection a challenging task. This project proposes a novel approach to detect malicious social bots on Twitter by leveraging Learning Automata in combination with URL-based features extracted from user-generated content. The methodology involves analyzing embedded URLs in tweets—such as domain reputation, frequency, and redirection

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Francisco, Moo-Mena, Robles-Sandoval Sofía, González-Magaña Karina, and Rodríguez-Adame Oliver. "Towards bots detection by analyzing the behavior of user data on Twitter." International Journal of Computer Science Issues 16, no. 1 (2019): 21–29. https://doi.org/10.5281/zenodo.2588241.

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Currently, social networks play an important role as a means of communication about various topics. In this way, this medium represents a very important source of data to know the opinions of its users on very diverse topics. However, the opinions expressed in this medium are exposed to the influence of specialized programs called bots. These bots are activated with the idea of influencing positively or negatively towards some point of view of the issues under discussion. When implemented through computer platforms accessible from any medium with Internet access, it is possible to access such

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Jyothis, Joseph, Binu Nandhitha, K. B. Vyshnavi, and Santhosh Nandana. "A Survey on Twitter Bot Detection: Comparative Study of Machine Learning and Deep Learning Techniques." Research and Reviews: Advancement in Robotics 8, no. 3 (2025): 1–11. https://doi.org/10.5281/zenodo.15515794.

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<em>X (formerly known as Twitter) has emerged as one of the most prominent social networking platforms, attracting diverse users, including individuals, influencers, businesses, and organizations. It allows users to share their content, opinions, news, and multimedia. Recently, there has been growing concern about the significant rise of malicious bots on social media platforms, especially on X. These bots can manipulate online discussions and spread misinformation, potentially exerting considerable influence on communities.</em> <em> </em> <em>This survey examines and conducts a comparat

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Shukla, Rachit, Adwitiya Sinha, and Ankit Chaudhary. "TweezBot: An AI-Driven Online Media Bot Identification Algorithm for Twitter Social Networks." Electronics 11, no. 5 (2022): 743. http://dx.doi.org/10.3390/electronics11050743.

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In the ultra-connected age of information, online social media platforms have become an indispensable part of our daily routines. Recently, this online public space is getting largely occupied by suspicious and manipulative social media bots. Such automated deceptive bots often attempt to distort ground realities and manipulate global trends, thus creating astroturfing attacks on the social media online portals. Moreover, these bots often tend to participate in duplicitous activities, including promotion of hidden agendas and indulgence in biased propagation meant for personal gain or scams. T

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Jeon and Cho. "Construction and Performance Analysis of Image Steganography-based Botnet in KakaoTalk Openchat." Computers 8, no. 3 (2019): 61. http://dx.doi.org/10.3390/computers8030061.

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Once a botnet is constructed over the network, a bot master and bots start communicating by periodically exchanging messages, which is known as botnet C&C communication, in order to send botnet commands to bots, collect critical information stored in bots, upgrade software functions of malwares installed in bots, and so on. For this reason, most existing botnet detection techniques focus on monitoring and capturing suspicious communications between the bot master and bots. Meanwhile, botnets continue to evolve to hide their C&C communication. Recently, a novel type of botnet using imag

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Wei, Chuancheng, Gang Liang, and Kexiang Yan. "BotGSL: Twitter Bot Detection with Graph Structure Learning." Computer Journal, March 2, 2024. http://dx.doi.org/10.1093/comjnl/bxae020.

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Abstract Twitter bot detection is an important and meaningful task. Existing methods can be bypassed by the latest bots that disguise themselves as genuine users and evade detection by mimicking them. These methods also fail to leverage the clustering tendencies of users, which is the most important feature for detecting bots at the community level. Moreover, they neglect the implicit relations between users that contain crucial clues for detection. Furthermore, the user relation graphs, which are essential for graph-based methods, may be unreliable due to noise and incompleteness in datasets.

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