Analyzing Right-wing YouTube Channels: Hate, Violence and Discrimination
Ottoni, R. et al (2018) investigated the differences between user interactions on far-right wing YouTube channels, compared to a baseline sample of channels. The study specifically searched for differences in lexical and topical between the two types of channels, as well as the instances and degree of implicant bias in the texts. The two main research questions were as follows:
- Is hateful vocabulary, violent content and discriminatory biases more, less, or equally prevalent in right-wing channels?
- Are video commenters more, less or equally aggravated than video hosts, and do they express more, less, or and equal amount of hate and discrimination?
Ottoni, R. et al (2018) hoped that the methods detailed in the study could be extended to any type of text. The following results were found:
- Right-wing channels tended to contain more “negative” semantics fields, such as “hate”, “kill”, “anger”, “sadness”, and “swearing”. This is opposed to “positive” semantics such as “joy”, “love”, “optimism”, and “politeness”.
- Typically approached topics related to war and terrorism.
- Demonstrated more discriminatory bias against Muslims and towards LGBT people.
I personally found this study to be fascinating, as well as some of the results. I disagree that results of a study have to be surprising to be worthwhile. We are very good at misleading ourselves (i.e. superstitious behaviors, etc.), thus I find merit in finding unsurprising results if they answer a research question that has not been pursued before. Having said that:
- I did not find the results of Ottoni, R. et al (2018) to be particularly surprising. While I am sure in group members with right-wing leanings likely have different opinions on the matter, I believe there has already been a lot of research on the emotions and language choices displayed my left- and right-wing leaning people. I personally conducted a badly designed study on the differences in emotion displayed by left- and right- wing leaning people when viewing internet memes (a study I would like to revisit eventually). Many of the results I found corresponded to the results found in this study.
- The study implemented a variety of analysis methods beyond lexical analysis. In the previous reflection, I made the point that perhaps lexical analysis on its own does not provide as much precision within the model as we would hope for. The inclusion of topical analysis and implicate bias analysis appears to cover a few additional facets to perceiving intent behind the text.
Work Cited:
Ottoni, R., Cunha, E., Magno, G., Bernardina, P., Jr., W. M., & Almeida, V. (2018). Analyzing Right-wing YouTube Channels. Proceedings of the 10th ACM Conference on Web Science – WebSci 18. doi:10.1145/3201064.3201081
