A Social-Aware Deep Learning Approach for Hate-Speech Detection

George C. Apostolopoulos, Panagiotis Liakos, Alex Delis

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Despite considerable efforts to automatically identify hate-speech in online social networks, users still face an uphill battle with toxic posts that seek to sow hatred. In this paper, we initially observe that there is a great deal of social properties transcending both hateful passages and respective authors. We then exploit this observation by i) developing deep learning neural networks that classify online posts as either hate or non-hate based on their content, and ii) proposing an architecture that may invigorate any such text-based classifier with the use of additional social features. Our combined approach considerably enhances the classification accuracy of previously proposed state-of-the-art models and our evaluation reveals social attributes that are the most helpful in our classification effort. We also contribute the first publicly-available dataset for hate-speech detection that features social properties.

Original languageEnglish (US)
Title of host publicationWeb and Big Data - 6th International Joint Conference, APWeb-WAIM 2022, Proceedings
EditorsBohan Li, Chuanqi Tao, Lin Yue, Xuming Han, Diego Calvanese, Toshiyuki Amagasa
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages9
ISBN (Print)9783031251573
StatePublished - 2023
Event6th International Joint Conference on Asia-Pacific Web (APWeb) and Web-Age Information Management (WAIM), APWeb-WAIM 2022 - Nanjing, China
Duration: Nov 25 2022Nov 27 2022

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume13421 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference6th International Joint Conference on Asia-Pacific Web (APWeb) and Web-Age Information Management (WAIM), APWeb-WAIM 2022


  • Deep learning
  • Social features
  • Twitter
  • User profile

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science


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