Analyzing Right-wing YouTube Channels:
Hate, Violence and Discrimination S
Summary:
The paper investigates the occurrence of hateful content and discriminatory bias in right-wing channels. First, the authors examine the similarities and dissimilarities between comments and video content on YouTube channels to see whether content creators express less, equally, or more hatred and discrimination than their audience. In addition, the results of right-wing channels are compared to those of baseline channels to check if extreme content is common on social media platforms or more prominent in right-wing channels.
The dataset consists of videos and comments from 12 right-wing channels and 10 most subscribed channels on YouTube. The authors conduct a three-layered analysis through which they evaluate lexicon, topics and discriminatory bias in videos and comments from the collected channels.
The results suggest that right-wing channels’ topics are more concentrated, focusing on terrorism and war. Moreover, these channels have a higher percentage of negative word categories, such as aggression and violence compared to baseline channels. Surprisingly, the level of bias against immigrants and LGBT people is comparable between right-wing and baseline channels. However, the negative bias against the Muslim community is more noticeable in right-wing channels. The analysis also shows that right-wing video hosts use negative words as often as commenters, yet the actual semantic fields vary. On the other hand, compared to the content creators, the viewers of right-wing channels express a higher bias against LGBT people and lower bias against Muslims in the comment section.
Reflection:
It is obvious that the research, as a whole, is partial and flawed. Not only that the title and the research questions reflect the authors’ strong presumption of the conservative section but also the authors’ choice of data is questionable. Why did they choose the website InfoWars and Social Blade as a seed? Furthermore, the phrase “according to our understanding” suggests their description of right-wing is not only ambiguous but also potentially biased. The confusion carries over to their selection of baseline channels. Despite being selected from the “news and politics” category, the fact that the majority of the comments are related to gaming (RiceGum, PewDiePie, Minecraft) means those channels cover a much wider range of topics. One of the channels is even called “DramaAlert”, which contributes more than one-third of the total number of comments of baseline channels. Hence, there is no surprise that entertaining videos contain more positive words than those discussing serious, controversial topics. Rather, the research should have studied the extremist behavior in political predominant YouTube channels. It will be interesting to see if right-wing channels can be separated from the mix.
Even though the authors’ bias discredit many of their findings, some analysis might still provide some insight and inspiration for future research. One worth noticing observation is that Muslims and terrorism overshadow other topics such as LGBT, illegal immigrants, anti-abortion, etc. Is terrorism always the prominent focus? Has this topic gained more attention because of the series of recent attacks and the President’s election campaign? It might be interesting to see if the change in the trend of topics and people opinion through time, especially after a major incident.
Another surprising finding is the dissimilarity between the video hosts and the commenters. Despite the comparable level of negativity, the variance in the semantic fields present in the caption and in the comments in right-wing channels varies greatly across videos. In another word, the viewers’ response to the conservative videos is more unpredictable and less affected by the sentiment of the hosts. The authors mentioned previous studies show hateful users “more negative, more profane and, counter-intuitively, use fewer words associated with topics … ” Does this suggest right-wing channels’ audience is more hateful and aggressive? Do their comments remain as offensive and negative in other videos? Does a user’s tendency to leave a hateful comment correlate to his/her subscription?
