Summary:
This paper was primarily about the differences between informational and conspiracy videos referring to the Zika virus during the most recent outbreak. Through analyzing the 35 most popular informational and conspiracy videos concerning the virus, comparisons were made between these types of videos based on a set of features. Some of the key conclusions drawn are listed below.
- A majority of the videos were classified as being informational videos (23 out of 35).
- There were no differences found in user activity or the sentiment of user responses between the two types of videos.
Reflection:
I have listed below a line that interested me in the paper.
“YouTube considers the number of views as the fundamental parameter of video popularity. Hence, collecting videos with the highest number of views, for our given search string, allows us to capture those videos that would be listed first by search engines”
I’m very hesitant to believe that views are the fundamental parameter of video popularity. Although views are the most obvious factor in portraying video popularity, they can easily be skewed. It’s impossible to determine if views are paid or organic, and if a user wants to get a point across with a video that likely won’t garnish much attention (i.e. a conspiracy theory), they may resort to this method. I think another potential factor to consider in choosing videos would be audience retention, as there are users who see a title and click on a video, but immediately stop watching after a few seconds.
Other Thoughts:
Overall, I was surprised by the similar sentiment found in both types of videos, as I had imagined there to be a drastic difference. One statistic that did not surprise me was the lower vaccination rates in states where misinformation was prevalent.
Automated Hate Speech Detection and the Problem of Offensive Language
Summary:
This paper was primarily about hate-speech detection, and differentiating between hate speech, offensive language, and neither. Through analyzing tweets from each of these categories, a crowd-sourced classifier was trained to classify between these groups. Some of the key conclusions drawn are listed below.
- Tweets with the highest probability of being hate speech usually contain racial or homophobic slurs.
- Lexical methods are effective ways to identify offensive language, but are inaccurate in determining hate speech.
Reflection:
I have listed below a line that interested me in the paper.
“While these tweets contain terms that can be considered racist and sexist it is apparent than many Twitter users use this type of language in their everyday communications.”
Classifying hate speech appears to be an arduous task, arguably more difficult than detecting fake news articles. This is especially due to the fact that many Twitter users use hate speech trigger words when they are not tweeting out hate speech (a common misclassification is with song lyrics). In order to better detect and classify hate speech, I believe that there needs to be a more consistent definition of what the term encompasses. More research also needs to be done on being able to factor in false words such as “love” that consistently fool hate speech detectors.
Other Thoughts:
Overall, I found myself pretty engaged in this topic. I think that the topic of targeting hateful content is interesting, and I’m heavily considering working on it for the semester project.
