Automated Hate Speech Detection and the Problem of Offensive Language
Summary:
In this article the authors attempted to use crowd sourcing to label a sample of tweets into three categories. These categories included hate speech, only offensive language, and those with neither. They did this by training a multi-class classifier to distinguish between these different categories. One of the key challenges is separating hate speech from other instances of offensive language.
Reflection:
Something that was very interesting to me was that both Facebook and twitter have gotten a lot of criticism for not doing enough to prevent hate speech on their sites. They responded to this by instituting policies to prohibit attacking people based on characteristics like a race, ethnicity, gender, and sexual orientation. Now I know a major way of getting these tweets or Facebook posts taken down is based off of user input, like reporting a post. I wonder if either of these platforms will take an autonomous route and remove posts automatically if they’ve reached the arbitrary quota for hate speech or offensive language. Something that would concern me about implementing this feature is if it takes it too far. For example a game that I play, Rainbow six siege, has very strict text limitations for in game chat. You can get banned for saying words that are not offensive but register as hate speech or offensive language to the machine.
Overall this is one of the articles that was very insightful, looking at it now I realize that distinguishing hate speech from offensive language is difficult but it’s not something that I would’ve thought about before reading the article.
Future Work:
There are two major things that I think would be contributing factors to distinguishing between hate speech, offensive language, and those containing neither. One would be seeing if the language was quoted from lyrics and if it was offensive or if it was just someone appreciating a song. The second would be if the text was just cultural difference that triggers some of the offensive language or hate speech words.
Early Public Responses to the Zika-Virus on YouTube: Prevalence of and Differences Between Conspiracy Theory and Informational Videos
Summary:
In this paper the authors wanted to examine the extent to which informational and conspiracy theories deferred in terms of user activity. They also wanted to check the sentiment and content of the user responses. They collected their data from the most popular videos posted on YouTube in the first phase of the Zika-virus outbreak in 2016. The results show that 12 out of the 35 videos in the data set focus on conspiracy theories however there were no statistical differences between the two types of videos.
Reflection:
Upon first reading, this paper seems fairly insignificant. Many times the authors said that there were no statistically significant findings. Something interesting the author said was YouTube videos often have misinformation on health related issues. I’d be curious to see what percentage of you youtubers that released videos on the Zika-virus were actually informed enough to make a video for the general public. I know the author wanted to see if this could be generalized towards other youtube video categories as well and I believe that there’ll be a lot of similar issues at least within the health field. Speaking from my sample set of videos I watch, there’s so much misinformation when it comes to videos related to powerlifting or bodybuilding. Often times there aren’t enough facts to back a statement, it just comes down to personal preference and what works best for you as an individual. When it comes to information that can be varied from user to user, I wonder how they would label what “misleading” exactly is.
Future Work:
Something that can be done to further research is to have a greater sample size than 35 videos. Also seeing if there are any more features that I can help to distinguish between informational videos and misleading videos.
