Summary:
Automated Hate Speech Detection: This study is about differentiating hate speech from other forms of offensive language. This study uses crowd-sourcing to create a dataset of tweets classified into three categories: hate speech, offensive language, and neither. Multiple models were tested: logistic regression, naïve Bayes, decision trees, random forests, and linear SVMs. The results show that 40% of hate speech was misclassified as offensive or neither.
Early Public Responses: This paper studies the presence of conspiracy theories on YouTube, specifically in Zika virus-related videos. The study looks for differences between user activity, sentiment, and content of two classifications of videos: informational and conspiracy. It is found that user activity and sentiment are similar between the two classifications, but content is different.
Reflection:
Accuracy of CrowdFlower: I wonder how similar is the labeling of different CrowdFlower workers. This could be tested as follows: Let all the workers classify the same set of 100 tweets. For each tweet, calculate the variance in the classifications made by the workers. High variances mean that the workers are classifying the same tweets differently.
Flagged posts: I think it would be interesting to investigate patterns in posts that get flagged for review or reported as inappropriate. Do people tend to flag posts excessively, or not enough? I think these results might affect how social media sites, such as Facebook and Twitter, develop policies on hate speech.
Hate speech in retweets and replies: The paper didn’t mention if the dataset they studied contained only original tweets, or also retweets and replies. I think it would be interesting to study how hate speech differs between these types of tweets. Where is hate speech most prevalent on Twitter?
I think the conclusion of the “Automated Hate Speech Detection” study can be improved. The significance of the findings and future work should both be a lot clearer and concrete.
In the “Early Public Responses,” I think the data could be presented better. Bar graphs would probably be easier to understand than tables.
Small sample size: The sample size is very small (n=23 for information and n=12 for conspiracy). I think the paper should have talked more about how this might affect their results.
Conspiracy theories on other types of social media platforms: I think the same type of study can be done on social media platforms other than YouTube. For example, we can study the prevalence of conspiracy theories in Twitter. Number of views would be replaced by number of retweets, and replies and likes would stay the same.
