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Relevant bibliographies by topics / Twitter bot detection

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Academic literature on the topic 'Twitter bot detection'

Author: Grafiati

Published: 2 June 2025

Last updated: 31 July 2025

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Journal articles on the topic "Twitter bot detection"

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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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Dissertations / Theses on the topic "Twitter bot detection"

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Shell, Joshua L. "Bots and Political Discourse: System Requirements and Proposed Methods of Bot Detection and Political Affiliation via Browser Plugin." University of Cincinnati / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1592136507505369.

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Teljstedt, Erik Christopher. "Separating Tweets from Croaks : Detecting Automated Twitter Accounts with Supervised Learning and Synthetically Constructed Training Data." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-192656.

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In this thesis, we have studied the problem of detecting automated Twitter accounts related to the Ukraine conflict using supervised learning. A striking problem with the collected data set is that it was initially lacking a ground truth. Traditionally, supervised learning approaches rely on manual annotation of training sets, but it incurs tedious work and becomes expensive for large and constantly changing collections. We present a novel approach to synthetically generate large amounts of labeled Twitter accounts for detection of automation using a rule-based classifier. It significantly red

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Valipour, Saeideh. "Language IndependentDetector for Auto GeneratedTweets." Thesis, Linnéuniversitetet, Institutionen för datavetenskap (DV), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-97837.

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The cross-disciplinary Nordic Tweet Stream (NTS) is a project aiming at creating a multilingual text corpus consisting of tweets published in the five Nordic countries. The NTS linguists are explicitly interested in tweets having a text formulated by a human where each tweet is a personal statement, not in Tweets generated by bots and other programs or apps since they might skew the results. NTS consists of multiple parts and the part we are responsible for is a language-independent approach, using supervised machine learning, to classify every single tweet as auto-generated (AGT) or human-gen

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"Types of Bots: Categorization of Accounts Using Unsupervised Machine Learning." Master's thesis, 2019. http://hdl.handle.net/2286/R.I.55528.

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abstract: Social media bot detection has been a signature challenge in recent years in online social networks. Many scholars agree that the bot detection problem has become an "arms race" between malicious actors, who seek to create bots to influence opinion on these networks, and the social media platforms to remove these accounts. Despite this acknowledged issue, bot presence continues to remain on social media networks. So, it has now become necessary to monitor different bots over time to identify changes in their activities or domain. Since monitoring individual accounts is not feasible,

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Book chapters on the topic "Twitter bot detection"

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Lopez-Joya, Salvador, J. Angel Diaz-Garcia, M. Dolores Ruiz, and Maria J. Martin-Bautista. "Bot Detection in Twitter: An Overview." In Flexible Query Answering Systems. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-42935-4_11.

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Yoon, Hyun Seop, Se Yeon Yang, Dong Hyuk Yoo, et al. "Korean Twitter Bot Detection via Deep Learning." In Advances in Computer Science and Ubiquitous Computing. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-1252-0_35.

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Bhosale, Sanmesh, and Fabio Di Troia. "Twitter Bots’ Detection with Benford’s Law and Machine Learning." In Silicon Valley Cybersecurity Conference. Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-24049-2_3.

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AbstractOnline Social Networks (OSNs) have grown exponentially in terms of active users and have now become an influential factor in the formation of public opinions. For this reason, the use of bots and botnets for spreading misinformation on OSNs has become a widespread concern. Identifying bots and botnets on Twitter can require complex statistical methods to score a profile based on multiple features. Benford’s Law, or the Law of Anomalous Numbers, states that, in any naturally occurring sequence of numbers, the First Significant Leading Digit (FSLD) frequency follows a particular pattern

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Wang, Weiguang, Qi Wang, Tianning Zang, et al. "BotRGA: Neighborhood-Aware Twitter Bot Detection with Relational Graph Aggregation." In Computational Science – ICCS 2024. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-63783-4_13.

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Rajkumar, Anupriya, C. Rakesh, M. Kalaivani, and G. Arun. "Twitter Bot Detection Using One-Class Classifier and Topic Analysis." In Inventive Systems and Control. Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-1012-8_56.

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Sethurajan, Monikka Reshmi, and K. Natarajan. "Twitter Bot Detection to Deter Bogus News Using Machine Learning Algorithms." In Algorithms for Intelligent Systems. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-3485-0_63.

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Nikumbh, Deepti, Anuradha Thakare, and Deep Nandu. "Analyzing User Profiles for Bot Account Detection on Twitter via Machine Learning Approach." In ICT: Smart Systems and Technologies. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-99-9489-2_10.

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Beskow, David M., and Kathleen M. Carley. "You Are Known by Your Friends: Leveraging Network Metrics for Bot Detection in Twitter." In Lecture Notes in Social Networks. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-41251-7_3.

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Qi, SiHua, Lulwah AlKulaib, and David A. Broniatowski. "Detecting and Characterizing Bot-Like Behavior on Twitter." In Social, Cultural, and Behavioral Modeling. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-93372-6_26.

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Fonseca Abreu, Jefferson Viana, Célia Ghedini Ralha, and Jöao José Costa Gondim. "A Multi-agent Approach for Online Twitter Bot Detection." In Investigación en Ciberseguridad. Ediciones de la Universidad de Castilla-La Mancha, 2021. http://dx.doi.org/10.18239/jornadas_2021.34.03.

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Conference papers on the topic "Twitter bot detection"

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Lin, Xiezhuo, Qingfeng Wu, and Yurui Huang. "DMIBot: Dynamic Multimodal Interaction for Twitter Bot Detection." In ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2025. https://doi.org/10.1109/icassp49660.2025.10889631.

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Bhale, Kanchan, and Thirupurasundari D. R. "Malicious Social Bot Detection in Twitter: A Review." In 2024 4th International Conference on Computer, Communication, Control & Information Technology (C3IT). IEEE, 2024. https://doi.org/10.1109/c3it60531.2024.10829441.

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Qiang, Liu, Lu Jiazhong, Huang Yuanyuan, Zhang Weisha, and Lv Jun. "Twitter Bot Detection with Multi-Head Attention and Supervised Contrastive Learning." In 2024 21st International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP). IEEE, 2024. https://doi.org/10.1109/iccwamtip64812.2024.10873626.

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Patel, Mitul, D. Karthick Rajan, Adusupalle Muni Raju, and Pravin Dange. "Twitter Bot Detection Using GCN with Adaptive Neighborhood Aggregation and Squeeze Module." In 2025 International Conference on Knowledge Engineering and Communication Systems (ICKECS). IEEE, 2025. https://doi.org/10.1109/ickecs65700.2025.11035956.

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Knauth, Jürgen. "Language-Agnostic Twitter Bot Detection." In Recent Advances in Natural Language Processing. Incoma Ltd., Shoumen, Bulgaria, 2019. http://dx.doi.org/10.26615/978-954-452-056-4_065.

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Kantepe, Mucahit, and Murat Can Ganiz. "Preprocessing framework for Twitter bot detection." In 2017 International Conference on Computer Science and Engineering (UBMK). IEEE, 2017. http://dx.doi.org/10.1109/ubmk.2017.8093483.

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Fonseca Abreu, Jefferson Viana, Celia Ghedini Ralha, and Joao Jose Costa Gondim. "Twitter Bot Detection with Reduced Feature Set." In 2020 IEEE International Conference on Intelligence and Security Informatics (ISI). IEEE, 2020. http://dx.doi.org/10.1109/isi49825.2020.9280525.

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Rao, Koppula Srinivas, Neeraja Koppula, and B. Veera Sekhar Reddy. "Real time malicious bot detection for Twitter." In PROCEEDINGS OF THE 1ST INTERNATIONAL CONFERENCE ON FRONTIER OF DIGITAL TECHNOLOGY TOWARDS A SUSTAINABLE SOCIETY. AIP Publishing, 2023. http://dx.doi.org/10.1063/5.0113369.

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Chavoshi, Nikan, Hossein Hamooni, and Abdullah Mueen. "DeBot: Twitter Bot Detection via Warped Correlation." In 2016 IEEE 16th International Conference on Data Mining (ICDM). IEEE, 2016. http://dx.doi.org/10.1109/icdm.2016.0096.

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Bui, Thi, and Katerina Potika. "Twitter Bot Detection using Social Network Analysis." In 2022 Fourth International Conference on Transdisciplinary AI (TransAI). IEEE, 2022. http://dx.doi.org/10.1109/transai54797.2022.00022.

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Reports on the topic "Twitter bot detection"

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Graham, Timothy, and Katherine M. FitzGerald. Bots, Fake News and Election Conspiracies: Disinformation During the Republican Primary Debate and the Trump Interview. Queensland University of Technology, 2023. http://dx.doi.org/10.5204/rep.eprints.242533.

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We used Alexandria Digital, a world leading disinformation detection technology, to analyse almost a million posts on X (formerly known as Twitter) and Reddit comments during the first Republican primary debate and counterprogrammed Tucker Carlson and Donald Trump interview on the 23rd of August. What we did: • Collected 949,259 posts from the platform X, formerly known as Twitter. These posts were collected if they contained one of 11 relevant hashtags or keywords and were posted between 8:45pm and 11:15pm EST on 23rd August 2023. • Collected 20,549 comments from two separate Reddit threads.

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