Disrupting networks of hate: Characterising hateful networks and removing critical nodes

Alorainy, Wafa, Burnap, Peter ORCID: https://orcid.org/0000-0003-0396-633X, Liu, Han, Williams, Matthew ORCID: https://orcid.org/0000-0003-2566-6063 and Giommoni, Luca ORCID: https://orcid.org/0000-0002-3127-654X 2022. Disrupting networks of hate: Characterising hateful networks and removing critical nodes. Social Network Analysis and Mining 12 , 27. 10.1007/s13278-021-00818-z

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Abstract

Hateful individuals and groups have increasingly been using the Internet to express their ideas, spread their beliefs, and recruit new members. Under- standing the network characteristics of these hateful groups could help understand individuals’ exposure to hate and derive intervention strategies to mitigate the dangers of such networks by disrupting communications. This article analyses two hateful followers net- works and three hateful retweet networks of Twitter users who post content subsequently classified by hu- man annotators as containing hateful content. Our analysis shows similar connectivity characteristics between the hateful followers networks and likewise between the hateful retweet networks. The study shows that the hateful networks exhibit higher connectivity characteristics when compared to other ”risky” networks, which can be seen as a risk in terms of the likelihood of expo- sure to, and propagation of, online hate. Three network performance metrics are used to quantify the hateful content exposure and contagion: giant component (GC) size, density and average shortest path. In order to efficiently identify nodes whose removal reduced the flow of hate in a network, we propose a range of structured node-removal strategies and test their effectiveness. Results show that removing users with a high degree is most effective in reducing the hateful followers network connectivity (GC, size and density), and therefore reducing the risk of exposure to cyberhate and stemming its propagation.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Social Sciences (Includes Criminology and Education) Computer Science & Informatics
Publisher:	Springer
ISSN:	1869-5469
Funders:	ESRC
Date of First Compliant Deposit:	20 September 2021
Date of Acceptance:	7 September 2021
Last Modified:	05 Jan 2024 06:17
URI:	https://orca.cardiff.ac.uk/id/eprint/144126

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