[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125590-en":3,"doc-seo-125590-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},125590,687197100911,"Himbo","https://ap-avatar.wpscdn.com/avatar/a000239b6f1da00475?x-image-process=image/resize,m_fixed,w_180,h_180&k=1785132997149421697",8,"Research & Report","BotArtist - Twitter bot detection - A semi-automatic machine learning pipeline","Twitter is a leading social network that is vulnerable to bots and fake accounts, which can manipulate discussion and disseminate false information. The study assembles a challenging multilingual dataset from 9M users around the Russo-Ukrainian war to detect bot accounts and related conversations. Ground truth comes from Twitter API suspended accounts (about 343K bots, 8M normal users) and Botometer-V3 data, plus additional labeled datasets. An XGBoost-based model is built and combined with suspension-labeled tweets, outperforming Botometer.","BotArtist: Twitter bot detection Machine Learning model based on Twitter  \nsuspension  \nAlexander Shevtsov, 123 Despoina Antonakaki, 1 2 Ioannis Lamprou, 1 Polyvios Pratikakis, 2 3  \nSotiris Ioannidis 12  \n1 School of Electrical and Computer Engineering, Technical University of Crete.  \n2Institute of Computer Science (ICS) of the Foundation for Research and Technology-Hellas (FORTH)  \n2 Computer Science Department-University of Crete  \narXiv :2306 .00037v2 [ cs . SI] 2 Jun 2023  \nAbstract  \nTwitter as one of the most popular social networks, offers a means for communication and online discourse, which unfortunately has been the target of bots and fake accounts, leading to the manipulation and spreading of false information. Towards this end, we gather a challenging, multilingual dataset of social discourse on Twitter, originating from 9M users regarding the recent Russo-Ukrainian war, in order to detect the bot accounts and the conversation involving them. We collect the ground truth for our dataset through the Twitter API suspended accounts collection, containing approximately 343K of bot accounts and 8M of normal users. Additionally, we use a dataset provided by Botometer-V3 with 1,777 Varol, 483 German accounts, and 1,321 US accounts. Besides the publicly available datasets, we also manage to collect 2 independent datasets around popular discussion topics of the 2022 energy crisis and the 2022 conspiracy discussions. Both of the datasets were labeled according to the Twitter suspension mechanism. We build a novel ML model for bot detection using the state-of-the-art XGBoost model. We combine the model with a high volume of labeled tweets according to the Twitter suspension mechanism ground truth. This requires a limited set of profile features allowing labeling of the dataset in different time periods from the collection, as it is independent of the Twitter API. In comparison with Botometer our methodology achieves an average 11% higher ROC-AUC score over two real-case scenario datasets.  \nIntroduction  \nOnline social media has become an essential part of everyday life. During the past decade, online social platforms have managed to transform the communication routine of our daily life. Due to their growing popularity, online social media gained millions of daily active users that not only consume the information but also create a space for content creators. The main reason behind online social media is real-time access to unlimited information, where registered users are able to share their comments and personal opinion about popular topics. Such high interest in online social phenomena generates the opportunity and the need for different categories of human interactions and contentsharing platforms. Such opportunities lead to the creation of  \nCopyright © 2022, Association for the Advancement of Artificial Intelligence ([www.aaai.org](www.aaai.org)). All rights reserved.  \ndifferent online social platforms where each of which provides a unique user experience, with a very similar goal of real-time human communication and content sharing. Twitter, one of the most popular social networks, with millions of active users, is used for news dissemination, political discussions, and social interactions. However, the platform has also been plagued by the presence of bots and fake accounts used to manipulate and spread false information. According to the research community, the usage of manipulation techniques implemented with the use of bot accounts is registered during diverse popular topic discussions. More specifically, studies show that bot accounts are involved in discussions around the 2016 and 2020 US Presidential elections (Golovchenko et al. 2020; Badawy, Ferrara, and Lerman 2018; Howard, Kollanyi, and Woolley 2016; Shevtsovet al. 2022, 2023) . Besides the US elections a high bot activity with spreading of misleading information is also detected during election periods (presidential/parliamentary/state) in countries like ","cbCaiqlE10wVfUOp","https://ap.wps.com/l/cbCaiqlE10wVfUOp","pdf",846160,1,10,"English","en",105,"# Abstract\n## Dataset construction and ground truth\n## Machine learning approach (XGBoost)\n## Evaluation versus Botometer\n## Explainability with SHAP\n# Introduction\n## Motivation: bots in social media\n## Related work and application domains","[{\"question\":\"What problem does BotArtist target on Twitter?\",\"answer\":\"BotArtist targets the identification of bot accounts and the conversations involving them, which can manipulate discussion and spread false information.\"},{\"question\":\"How is ground truth data obtained for training and evaluation?\",\"answer\":\"Ground truth is collected from Twitter API suspended accounts and complemented with Botometer-V3 datasets, along with additional independently collected and suspension-labeled datasets.\"},{\"question\":\"What model is used in BotArtist and how does it compare with Botometer?\",\"answer\":\"BotArtist uses an XGBoost-based machine learning model combined with labeled tweets from the suspension mechanism. It achieves an average 11% higher ROC-AUC score across two real-world datasets.\"}]","BotArtist - Twitter bot detection - A semi-automatic machine learning pipeline | PDF",1785900098,25,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"botartist-twitter-bot-detection-a-semi-automatic-machine-learning-pipeline","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/botartist-twitter-bot-detection-a-semi-automatic-machine-learning-pipeline/125590/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-05",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What problem does BotArtist target on Twitter?","Question",{"text":75,"@type":76},"BotArtist targets the identification of bot accounts and the conversations involving them, which can manipulate discussion and spread false information.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How is ground truth data obtained for training and evaluation?",{"text":80,"@type":76},"Ground truth is collected from Twitter API suspended accounts and complemented with Botometer-V3 datasets, along with additional independently collected and suspension-labeled datasets.",{"name":82,"@type":73,"acceptedAnswer":83},"What model is used in BotArtist and how does it compare with Botometer?",{"text":84,"@type":76},"BotArtist uses an XGBoost-based machine learning model combined with labeled tweets from the suspension mechanism. 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