# On Simulating the Propagation and Countermeasures of Hate Speech in Social Networks
## 5 Modelling Countermeasures
### Counter Activism  
Counterspeech is defined as a direct response to hateful or harmful speech which seeks to undermine it. Examples of counterspeech are the presentation of an alternative narrative, rebuking a person for inadequate expressions and convincing a person to change discourse [46]. Counterspeech can be organised by institutions or campaigns but can also be spontaneous, although counterspeech that is conducted by organised groups is associated with a more balanced discourse [7]. Overall, a large scale analysis of conversations related to current societal and political issues on German Twitter between 2013 and 2018 revealed a slight increase of counter tweets in recent years [7]. Additionally, investigations on the case of hate against the Roma Minority in Slovakia has shown that counterspeech can motivate other people to express their opinion against hate speech [47]. In addition to the expected positive effect of promoting anti-hate slogans and to spread positively influencing messages, counterspeech can have different outcomes [46]. On the one hand, extreme opinions of counter speakers tend to be rather ignored [35; 48]. On the other hand, the conversation can escalate the hate and counter speakers may become victims themselves. This is especially the cases when utterances are perceived as a threat to one's own social identity [5]. Thus, moderate counterspeech may be more effective. Indeed, experiences from the activist group Red Levadura ([[https://redlevadura.net](https://redlevadura.net)]([https://redlevadura.net](https://redlevadura.net)), accessed on 1 November 2021) reveal that emotional expression and empathy are the key factors for success of counterspeech [49]. In fact, counter speakers/activists have been identified as agreeable, altruistic, modest and sympathetic individuals [19]. Together, counter activists constitute a counter movement that act as the pole of '_the good_', which could subsume different counteractivities such as organised counterspeech or public awareness campaigns. In our model, counter activists spread positively influencing messages whose hate score are in the lowest values, so within the interval \([0,0.25)\) from the default Gamma distribution \(\Gamma(10,25)\) in this work--the launder (right-most) distribution depicted in Figure 5. As counter activists react against existing hate spreading groups, we implement them as a mid-term measure that starts during the opinion diffusion phase (see Section 4.2), where activists are sampled from the group of non-hateful persons with a probability \(p_{convince}\) (in the code, probability \(p_{convince}\) appears as \(p_{convince}\)_to_become_activist_), instead of from persons who are just joining the network. By default, their opinion is not fixed (however, activists' opinions can be fixed in the code by setting the \(stubborn\_activists\)? variable to true) and can change due to opinion diffusion. When it exceeds \(hate\ score\geq 0.25\) they change to normal activity, but keep previously created connections. Furthermore:  
* On becoming activist (denoted as \(a\)), a person spawns additional following connections \(c_{a}\) to the group of all activists (in the code, this \(c_{a}\) limit is set with the variable \(n\_following\_conn\_activist\_additional\)), which are answered with the probability \(p_{a\gets a}=0.9\) (in the code, probability \(p_{a\gets a}\) appears as \(p\_{activist\_back\_follows\_activist}\)). * Activists are as active as hateful users and, thus, they publish posts with the probability \(p_{a\_pub}=1\) at every round (in the code, probability \(p_{a\_pub}\) appears as \(p\_{publish\_post\_activist}\)). * The maximal amount of reposts that an activist can perform per round is set to \(m_{a}=6\) so that they promote non-hateful content frequently (in the code, \(m_{a}\) appears as \(max\_reposts\_by\_activists\)).