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MANDOLA: A Big-Data Processing and Visualization Platform for Monitoring and Detecting Online Hate Speech

Authors:

Demetris Paschalides,

Dimosthenis Stephanidis,

Andreas Andreou,

Kalia Orphanou,

Marios D. Dikaiakos,

Evangelos MarkatosAuthors Info & Claims

ACM Transactions on Internet Technology (TOIT), Volume 20, Issue 2

Article No.: 11, Pages 1 - 21

https://doi.org/10.1145/3371276

Published: 04 March 2020 Publication History

Abstract

In recent years, the increasing propagation of hate speech in online social networks and the need for effective counter-measures have drawn significant investment from social network companies and researchers. This has resulted in the development of many web platforms and mobile applications for reporting and monitoring online hate speech incidents. In this article, we present MANDOLA, a big-data processing system that monitors, detects, visualizes, and reports the spread and penetration of online hate-related speech using big-data approaches. MANDOLA consists of six individual components that intercommunicate to consume, process, store, and visualize statistical information regarding hate speech spread online. We also present a novel ensemble-based classification algorithm for hate speech detection that can significantly improve the performance of MANDOLA’s ability to detect hate speech. To present the functionality and usability of our system, we present a use case scenario of real-life event annotation and data correlation. As shown from the performance of the individual modules, as well as the usability and functionality of the whole system, MANDOLA is a powerful system for reporting and monitoring online hate speech.

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Published In

cover image ACM Transactions on Internet Technology

ACM Transactions on Internet Technology Volume 20, Issue 2

Special Section on Emotions in Conflictual Social Interactions and Regular Papers

May 2020

256 pages

ISSN:1533-5399

EISSN:1557-6051

DOI:10.1145/3386441

Editor:
Ling Liu
Georgia Institute of Technology, USA

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Copyright © 2020 ACM.

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Publication History

Published: 04 March 2020

Accepted: 01 November 2019

Revised: 01 September 2019

Received: 01 April 2019

Published in TOIT Volume 20, Issue 2

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