Linking textual and contextual features for intelligent cyberbullying detection in social media

Nabi Rezvani, Amin Beheshti, Alireza Tabebordbar

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contributionpeer-review

Abstract

The rapid and vast adoption of social media and the 21st century technologies is changing the bullying process., i.e., deliberate misuse of power in relationships through social behavior that intends to cause social and psychological harm. In this context, Cyberbullying can be defined as the process of using technology to hurt someone else by sending hurtful messages, pictures, or comments. A successful Cyberbullying detection technique requires an effective combination of big data (generated from social, open, and private data islands), features, and algorithms. While many Cyberbullying detection techniques exist for working with machine learning data and algorithms, support for thinking of new features (to be extracted from images, images' metadata, and textual information generated around images) remains poor. In this paper, we present an intelligent Cyberbullying detection pipeline to: (i) extract features (from an image, image meta-data and textual content generate); (ii) contextualize the extracted features by developing a crowdsourced feedback loop and drawing on human knowledge; and (iii) combining features using a Neural Network to identify and build potentially useful features. We adopt a typical scenario for analyzing social media content generated on Instagram and Twitter to identify Cyberbullying activities. We discuss how the proposed approach significantly improves the quality of extracted knowledge compared to classical natural language analysis techniques.

Original languageEnglish
Title of host publication18th International Conference on Advances in Mobile Computing and Multimedia, MoMM2020 - Proceedings
EditorsPari Delir Haghighi, Ivan Luiz Salvadori, Matthias Steinbauer, Ismail Khalil, Gabriele Kotsis
Place of PublicationNew York, NY
PublisherAssociation for Computing Machinery, Inc
Pages3-10
Number of pages8
ISBN (Electronic)9781450389242
DOIs
Publication statusPublished - 2020
Event18th International Conference on Advances in Mobile Computing and Multimedia, MoMM 2020, in conjunction with the 22nd International Conference on Information Integration and Web-Based Applications and Services, iiWAS 2020 - Virtual, Online, Thailand
Duration: 30 Nov 20202 Dec 2020

Publication series

NameACM International Conference Proceeding Series

Conference

Conference18th International Conference on Advances in Mobile Computing and Multimedia, MoMM 2020, in conjunction with the 22nd International Conference on Information Integration and Web-Based Applications and Services, iiWAS 2020
CountryThailand
CityVirtual, Online
Period30/11/202/12/20

Keywords

  • cyberbullying detection
  • data curation
  • deep neural networks

Fingerprint Dive into the research topics of 'Linking textual and contextual features for intelligent cyberbullying detection in social media'. Together they form a unique fingerprint.

Cite this