# On Simulating the Propagation and Countermeasures of Hate Speech in Social Networks
###### Abstract  
Hate speech expresses prejudice and discrimination based on actual or perceived innate characteristics such as gender, race, religion, ethnicity, colour, national origin, disability or sexual orientation. Research has proven that the amount of hateful messages increases inevitably on online social media. Although hate propagators constitute a tiny minority--with less than 1% participants--they create an unproportionally high amount of hate motivated content. Thus, if not countered properly, hate speech can propagate through the whole society. In this paper we apply agent-based modelling to reproduce how the hate speech phenomenon spreads within social networks. We reuse insights from the research literature to construct and validate a baseline model for the propagation of hate speech. From this, three countermeasures are modelled and simulated to investigate their effectiveness in containing the spread of hatred: Education, deferring hateful content, and cyber activism. Our simulations suggest that: (1) Education consititutes a very successful countermeasure, but it is long term and still cannot eliminate hatred completely; (2) Deferring hateful content has a similar--although lower--positive effect than education, and it has the advantage of being a short-term countermeasure; (3) In our simulations, extreme cyber activism against hatred shows the poorest performance as a countermeasure, since it seems to increase the likelihood of resulting in highly polarised societies. hate speech; hate spread; countermeasures; social networks; opinion diffusion; education; deferring hate content; cyber activism +
Footnote †: 2021: 10 December 2021  
To counter this problem researchers and politicians have proposed several measures with different temporal horizons. General long-term measures emphasise on education of democratic values and tolerance [9] as well as developing critical thinking [10]. They are proposed in order to introduce positive bias into the society, thus inhibiting the propagation of hate speech. Awareness campaigns by governmental organisations focus on mid-term effects and try to prevent forming negative prejudices against out-groups and minorities. Others propose expensive manual community management (i.e., moderation) or intrinsically motivated counter speech [11]. In contrast, the short-term measure of automatic message filtering (or blocking of hateful users) is criticised as, for some cases, it could be used against the freedom of expression human right. Moreover, this countermeasure bears hidden risks of having hateful users being just displaced to other platforms and not really eliminated [12]. In fact, despite the risks associated with banning users, there are some successful experiences that support it. For example, Reddit performed a massive banning of hateful groups in 2015 that did not lead to the displacement of haters to other subreddits/groups [13]. Additionally, the Twitter's user purge in 2017 neither lead to direct migration of haters to Gab--a more radical platform--since they were already there [6]. Exhaustive evaluations of these countermeasures, however, are still lacking and it is difficult to assess the effectiveness of these initiatives and, much less, to compare them among each other. However, real social networks are too complex to experiment on, and therefore, this paper is devoted to propose an agent-based model [14] as a virtual experiment for the simulation and comparison of countermeasures against the spread of hatred (note that this paper is an extended version of [15]). Although multi-agent based simulations encompass several simplifications, they are definitely useful to conduct what-if analysis to assess the system's behaviour under various situations [16; 17; 18], which in our case correspond to different countermeasures. Specifically, we first build a hate speech propagation model based on current research insights on the behaviours of hateful users in social networks.