Overview
This paper analyses a selection of right-wing YouTube channels aiming to ascertain if the presence of hateful vocabulary, violent content and discriminatory biases are accentuated more in right-wing channels and if commentators are more or less exacerbated than videos hosts. The authors first selected a sample of YouTube channels they identified as right-wing and a baseline selection of the top ten YouTube channels in the news and politics category. The analyzed the comments of the videos and the videos themselves for hate, violence, and bias using a threefold method of lexical, topic, and implicit bias analysis. From their analysis and data, the authors made several conclusions: that right-wing channel are more specific in their content, have a negative bias towards Muslims, and that commenters on right-wing YouTube channels are more exacerbated than the hosts of the videos.
Reflection
- The paper focuses on “right-wing” content on YouTube. Their definition of right-wing seems to be based off of their personal opinions and connection InfoWars, “a right-wing news site founded by…” Their definition seems lacking to me and the view of their paper seems one sided. They also state, “It is valuable to investigate whether behaviors connected to hate, violence, and discriminatory bias come into sight in right-wing videos”. The authors do not offer what value they are talking about with this “value” or what conclusions they look to draw. I would instead choose to inquire as to the presence of hate, violence, and discriminatory bias in partisan YouTube channels.
- The paper creates its sample of right-wing YouTube channels by examining channels connected to “Alex Jones” which the authors said they “visited and confirmed that, according to our understanding, all of them published mainly right –wing content”. In total, the researchers collected 13 YouTube channels. I think this method of sample selection introduces a huge amount of bias to the study as it relies on the researchers opinions and verification of what is right-wing.
- The researchers lexical analysis made use of semantic fields to categorize words in the caption or comments as positive or negative. They selected a collection of categories and grouped them as positive or negative, if a word fit a semantic category in the positive or negative grouping it added towards the corresponding word count for that source. The researchers seemed to choose far more categories for the negative category than for the positive category and it seems possible that this may have skewed their results. From 194 total categories to choose from the researchers chose “15 categories related to hate violence, discrimination and negative feelings, and (b) 5 categories related to positive matters in general” It seems to me that they cherry picked these categories to support what they are trying to showcase in this article instead of using a representative sampling of semantic fields. I think a lexical analysis of a wide variety of political news YouTube channels using all of the Empath semantic field categories could provide insight into implicit bias or sentiments embedded in our political process.”
- The authors point out that some of the top ranked topics for right-wing videos had to do with war and terrorism which the authors categorizes as negative. However, these issues are hallmarks of the right-wing platform and their very presence does not indicate hate, violence, or bias. Instead I think such topic analysis across all news sources could give some insight into the use of fear in media to persuade, influence, or simply sell more media.
