# A Unified Deep Learning Architecture for Abuse Detection Source: [A Unified Deep Learning Architecture for Abuse Detection]() Collection index: [llms.txt]() Data updated: 2026-01-04T02:57:52.440Z Authors: Antigoni-Maria Founta, Despoina Chatzakou, Nicolas Kourtellis, Jeremy Blackburn, Athena Vakali, Ilias Leontiadis Published: 2018-02-01T16:48:39.000Z ## Abstract Hate speech, offensive language, sexism, racism and other types of abusive behavior have become a common phenomenon in many online social media platforms. In recent years, such diverse abusive behaviors have been manifesting with increased frequency and levels of intensity. This is due to the openness and willingness of popular media platforms, such as Twitter and Facebook, to host content of sensitive or controversial topics. However, these platforms have not adequately addressed the problem of online abusive behavior, and their responsiveness to the effective detection and blocking of such inappropriate behavior remains limited. In the present paper, we study this complex problem by following a more holistic approach, which considers the various aspects of abusive behavior. To make the approach tangible, we focus on Twitter data and analyze user and textual properties from different angles of abusive posting behavior. We propose a deep learning architecture, which utilizes a wide variety of available metadata, and combines it with automatically-extracted hidden patterns within the text of the tweets, to detect multiple abusive behavioral norms which are highly inter-related. We apply this unified architecture in a seamless, transparent fashion to detect different types of abusive behavior (hate speech, sexism vs. racism, bullying, sarcasm, etc.) without the need for any tuning of the model architecture for each task. We test the proposed approach with multiple datasets addressing different and multiple abusive behaviors on Twitter. Our results demonstrate that it largely outperforms the state-of-art methods (between 21 and 45\\% improvement in AUC, depending on the dataset). ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Abuse Detection on Cyberbullying Dataset 3 classes (tweet-level)](): Abuse Detection; Cyberbullying Dataset 3 classes (tweet-level); AUC: 96. - [Abuse Detection on Offensive Dataset](): Abuse Detection; Offensive Dataset; Accuracy: 87. - [Abuse Detection on Hate Dataset](): Abuse Detection; Hate Dataset; AUC: 92. - [Abuse Detection on Sarcasm Dataset](): Abuse Detection; Sarcasm Dataset; AUC: 98. ## Original sources and implementation [Original paper]() [PDF]()