Cyberhate and cyberbullying are two specific types of cyberaggression which have detrimental effect on well-being of all involved actors, i.e., victims, perpetrators, and those exposed to these incidents (Keipi et al., 2019; Kowalski et al., 2012). Cyberhate refers to online hate speech and attacks people based on their group affiliation and collective identity (Hawdon et al., 2017). It is affected by the current socio-political events and context (Kaakinen et al., 2015). Cyberhate is a serious matter as it not only impacts on people but also deteriorates social cohesion. Cyberbullying is defined as a repeated intentionally harmful digital activity (Tokunaga, 2010). These two experiences share many similarities, as they are both acts of aggression which aim to harm, harass, or degrade someone, be it an individual or a group of people. Moreover, there is evidence of an existing overlap between these experiences (Görzig et al., 2019, Pyżalski, 2012). However, there are also crucial differences as cyberhate and cyberbullying are considered as distinct phenomena which need to be differentiated on the level of theory as well as prevention praxis (Blaya & Audrin, 2019; Wachs et al., 2019). Nonetheless, previous research has not sufficiently differentiated cyberhate and cyberbullying and treated them often as one phenomenon at the level of operationalization. This presents a challenge for current and future research. Thus, we aimed to fill this gap by investigating the distinct associations of cyberhate and cyberbullying and identify which children are involved in these experiences. Specifically, we explored associations of experiences with cyberhate and cyberbullying among children (N = 3856, aged 11-17, 52.1% girls) which participated in the EU Kids Online IV survey in 2017 and 2018 in Czechia, Poland, and Slovakia. First, we tested three alternative factor models of online aggression: one-factor, two-factor, and bifactor. We looked at general involvement in cyberbullying and cyberhate, including being a victim, perpetrator, or bystander. A bifactor model with two specific factors, cyberhate and cyberbullying, and a third underlying general risk factor showed the best fit. Then we examined known correlates of online aggression and tested whether found associations differed across countries. Several patterns, consistent across countries, emerged from the correlation analysis. Age, emotional problems, and time spent online were significantly associated with the general risk factor. Previous discrimination experience based on group characteristics was related to cyberhate, and discrimination based on individual characteristics was related to cyberbullying and the general risk factor. Gender and quality of relationships with peers associations were inconsistent and had negligible effect. Implications are discussed with regard to theoretical conceptualization of these risks and also prevention and intervention efforts.