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Part III. Digital Media and Storytelling in Research | 115 the digital environment as a natural social medium which allows for the representation of the self and the interaction of users, thus allowing for the study of identity constructs and behavior. Such research poses a major methodological challenge for researchers, but despite its limitations, the ethnographic approach has a number of advantages. The main advantage of ethnographic research is that the data are easily accessible and are not produced for research purposes, but are present in their natural environment. While data mining seems simple, the data noice can be massive - due to factors such as data feeds from artificial intelligence bots - and the researcher has to expend considerable effort on data cleaning. At the same time, the amount of data can vary from minute to minute, so the researcher is forced to set a specific time period for the analysis. The resulting data corpus can be huge and often requires the use of artificial intelligence software to analyze it. Beyond the data collection, another challenge is the choice of the method used to analyze the data. Netnography, that is, the study of content on the Internet, cannot be considered a research method in itself, as it is always determined by the research paradigm and the research objective that fits the discipline. Sources can be approached from an anthropological paradigm and used to explore cultural content and ritual behavioral patterns. Human behavior can also investigated from a socio-psychological or even from an economic perspective. Braga (2021) suggests that face-to-face ethnographic research methods are necessarily adaptable to an online environment. An example is participant observation, which cannot be fully realized due to the time gap between the publication of online content (e.g., writing a post or a comment) and the researcher's reception of it, as the interactions are not taking place in realtime as in the case of face-to-face observation. However, one advantage is that users’ online activities can be tracked using the logged data from platforms, which allows the researcher to analyze the data quantitatively. However, statistical correlations calculated from logged data may lead to biased results, as participation in online platforms does not necessarily imply activity. It can be concluded that content analysis is the ideal research method to analyze the visual, verbal and audiovisual micro-narratives of the Internet. In order to maintain the validity of the research, content analysis should be coded using two independent coders and methodological triangulation should be employed. Braga (2021) suggests the use of face-to-face interviews to complement netnographic content analysis. Bastiaensens et al. (2019) analyzed a chat stream of 937 chat posts from online forums created by youth workers to support victims of cyberbullying. The interactions were generated in their own natural medium and were not research-related, so data bias was minimal. Participants posted anonymously behind an avatar user profile. The conversation revealed patterns of bullying on online platforms (e.g., blackmail with naked pictures, shaming public posts with pictures and text, or hate speech messages), as well as coping
