# On Simulating the Propagation and Countermeasures of Hate Speech in Social Networks
## 7 Conclusions and Future Work  
This paper proposes a multi-agent model of the spread of hatred within social networks. We base our modelling on insights from previous research and take these as reference to successfully validate the resulting model, the so-called baseline model. Then, we enrich it by adding three countermeasures--education, deferring hateful content and center activism--and conduct a series of experiments to assess their effectiveness in containing the spread of hatred. As a result, we conclude that: (i) Education proves to be very successful long-term countermeasure, although it still cannot eliminate hatred completely; (ii) Deferring hateful content shows a similar (lower) positive effect, but it also has the advantage of being a short-term countermeasure; (iii) Cyber counteractivism needs to be carefully articulated, as it can increase the society polarisation. Additionally, our simulations seem to indicate that hate cores--which are responsible for hate spread--in real-world social networks might be even more densely connected than reported by current statistics. As future work, we plan to further refine our model to dive deeper in the study of counteractivism and the other implemented countermeasures. In addition, we find it particularly interesting to model the effects on the content receivers by differentiating passive listeners from the victims of the hateful content. Moreover, we also plan to incorporate additional countermeasures such as awareness campaigns or tight community management. Conceptualization, M.L.-S.; methodology, M.L.-S.; software, A.M.; validation, A.M. and M.L.-S.; formal analysis, A.M.; investigation, A.M.; resources, A.M.; writing--original draft preparation, M.L.-S. and A.M.; writing--review and editing, M.L.-S.; visualization, A.M.; supervision, M.L.-S.; project administration, M.L.-S.; funding acquisition, M.L.-S. All authors have read and agreed to the published version of the manuscript. This research was funded by projects: 2017 SGR code 341 from the Agencia de Gestio d'Ajuts Universitats i de Recerca (AGAUR) from the Generalitat de Catalunya; MISMIS (Desinformacion y agresividad en Social Media: Analizando el lenguaje, code PGC2018-096212-B-C33) from the Spanish Ministerio de Ciencia, Innovacion y Universidades; Crowd45 DG (Citizen Science for Monitoring Climate Impacts and Achieving Climate Resilience, code H2020-872944) from the European Union; CI-SUSTAIN (Advanced Computational Intelligence Techniques for Reaching, code PID2019-104156 GB-I00) from the Spanish Ministerio de Ciencia e Innovacion; COREDEM (The Influence of Complex Reward Computation and Working Memory Load onto Decision-Making: A combined Theoretical, Human and Non-human primate approach code SGA2H2020-785907 within the Human Brain Project) from the European Union; and nanoMOOC (Nou format audiovisual amb funcionalitats recnologiques avancades per a l'apprenentage code COMRDI18-1-0010-02) from Agencia de Support a l'Empresa Catalana. Not applicable. **Data Availability Statement:** Simulations in the paper were conducted with a NetLogo model developed by the authors that is publicly available from [[http://www.maia.ub.es/~maite/Students.html](http://www.maia.ub.es/~maite/Students.html)]([http://www.maia.ub.es/~maite/Students.html](http://www.maia.ub.es/~maite/Students.html)) or directly at [[http://www.maia.ub.es/~maite/thesis/network_growth.nlogo](http://www.maia.ub.es/~maite/thesis/network_growth.nlogo)]([http://www.maia.ub.es/~maite/thesis/network_growth.nlogo](http://www.maia.ub.es/~maite/thesis/network_growth.nlogo)). The data reported in the paper was generated during the study by using that simulation. Data and associated tests are also publicly available at [[https://github.com/agrizli/simulation_countering_hate_speech](https://github.com/agrizli/simulation_countering_hate_speech)]([https://github.com/agrizli/simulation_countering_hate_speech](https://github.com/agrizli/simulation_countering_hate_speech)), all accessed on 1 November 2021.