BotArtist: Twitter bot detection Machine Learning model based on Twitter suspension

Twitter 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

Discovery and classification of Twitter bots

A very large number of people use Online Social Networks daily. Such platforms thus become attractive targets for agents that seek to gain access to the attention of large audiences, and influence perceptions or opinions. Botnets, collections of automated accounts controlled by a single agent, are a common mechanism for exerting maximum influence. Botnets may be used to better infiltrate the social graph over time and to create an illusion of community behavior, amplifying their message and increasing persuasion. This paper investigates Twitter botnets, their behavior, their interaction with user communities and their evolution over time. We analyzed a dense crawl of a subset of Twitter traffic, amounting to nearly all interactions by Greek-speaking Twitter users for a period of 36 months. We detected over a million events where seemingly unrelated accounts tweeted nearly identical content at nearly the same time. We filtered these concurrent content injection events and detected a set of 1,850 accounts that repeatedly exhibit this pattern of behavior, suggesting that they are fully or in part controlled and orchestrated by the same software. We found botnets that appear for brief intervals and disappear, as well as botnets that evolve and grow, spanning the duration of our dataset. We analyze statistical differences between bot accounts and human users, as well as botnet interaction with user communities and Twitter trending topics

Optic atrophy, necrotizing anterior scleritis and keratitis presenting in association with Streptococcal Toxic Shock Syndrome: a case report

<p>Abstract</p> <p>Introduction</p> <p>We report a case of optic atrophy, necrotizing anterior scleritis and keratitis presenting in a patient with Streptococcal Toxic Shock Syndrome.</p> <p>Case presentation</p> <p>A 43-year-old woman developed streptococcal toxic shock syndrome secondary to septic arthritis of her right ankle. Streptococcus pyogenes (b-haemolyticus Group A) was isolated from blood cultures and joint aspirate. She was referred for ophthalmology review as her right eye became injected and the pupil had become unresponsive to light whilst she was in the Intensive Therapy Unit (ITU). The iris appeared atrophic and was mid-dilated with no direct or consensual response to light. Three zones of sub-epithelial opacification where noted in the cornea. There where extensive posterior synechiae. Indirect ophthalmoscopy showed a pale right disc. The vision was reduced to hand movements (HM). A diagnosis of optic atrophy was made secondary to post-streptococcal uveitis. She subsequently developed a necrotizing anterior scleritis.</p> <p>Conclusion</p> <p>This case illustrates a previously unreported association of optic atrophy, necrotizing anterior scleritis and keratitis in a patient with post-streptococcal uveitis. This patient had developed Streptococcal Toxic Shock Syndrome secondary to septic arthritis. We recommend increased awareness of the potential risks of these patients developing severe ocular involvement.</p