Analyzing Right-wing YouTube Channels: Hate, Violence and Discrimination
Summary:
This paper wanted to “observe issues related to hate, violence and discriminatory bias” of different types of videos. The researchers partitioned their data set into right-winged and baseline videos. In order to compare the two different partitions that they assigned, the researchers used a three-layered approach. The tree layers include lexicon, topics, and implicit bias. The research questions that they wanted to answer were:
1. Is the presence of hateful vocabulary, violent content and discriminatory biases more, less or equally accentuated in right-wing channels?
2. Are, in general, commentators more, less or equally exacerbated than video hosts in an effort to express hate and discrimination?
The study found that “right-wing channels are more specific in their content, discussing topics such as terrorism and war, and also present a higher percentage of negative word categories” and that they were able to capture a bias against Muslim communities.
Reflection:
I liked this paper, it brought up a lot of interesting practices on how to analyze different types of words such as Word Embedding Association Test (WEAT). I did not think it was possible for the Implicit Association Test, or at least some version of it, to be encoded in an algorithm. I am a little skeptical about how accurate WEAT is since the authors make it sound like a simple cosine similarity between words that share a common context. I also would like to see how the WEAT was applied to the Wikipedia pages that were on topics like Baye’s Rule. Also, would the results change if a different site was used as a starting point? For example, if Twitter or the Encyclopedia Britannica was used I would have to imagine the results would be somewhat different.
There are a lot of sites that would meet the criteria for why they choose Wikipedia. I wish there was more discussion about why they choose the twenty categories they selected from the 194 Empath categories. Also, why did they not choose the same amount of positive and negative categories? It kind of feels like they were only trying to find things in the negative category and the positives category was tacked in as an afterthought.
I was surprised to find that a lot of hate and toxicity was found on general videos as well as the right-winged videos. I wonder if the same would be true on other platforms such as Reddit, Twitter, or 4chan.
Future Work and Questions:
Can commentator’s activity on other videos be tracked to see what they usually watch and respond too? There might be a difference between how people in different groups interact, or don’t interact, with other groups.
With a lot of sites now banning right-winged content, I would be interested to see how the audience and content creators act on different sites. Do they progressivly get more hateful the more the are banned from websites?
