This assignment started off by reading 6 different articles surrounding the topic of cyberbullying. After this, we had to chat with our chat group, discussing 6 different things. These six things were the methods we learned about in the Bazelon, Munger, and Kal Penn articles, that have been tried to stop cyberbullying, the suggestion made in Reynolds’ article to stop cyberbullying, the suggestions made in the Pew Research Center’s survey, the methods we learned in Cunningham’s abstract that other university students believe will be effective in stopping cyberbullying, which of these methods my Chat Group thinks would be most and least effective in stopping cyberbullying, and design an effective system to deter, if not eliminate, cyberbullying.
The first article discussed how experts are trying to find a solution to stop cyberbullying, and talked about some of the difficulties involved with this. The second article talked about utilizing online bots to reduce racial harassment. The 3rd article describes how a man raised 5oo,000 dollars for Syrian refugees in response to a troll.
The fourth article conveyed that cyberbullying is actually very uncommon, and mostly experienced by those who also experience other forms of bullying. The fifth described 4 possible themes that could describe the future of the online social climate, including positive and negative ideas. The 6th article was an abstract that described the anti-cyberbullying preferences of University students.
In our chat, for the Bazelon, Munger, and Kal Penn articles, we determined the methods they applied to reduce cyberbullying. Kal Penn raised money through a fundraising page, trying to help the victims rather than necessarily going after the bullies. Bazelon tried to visit Silicon Valley headquarters of Facebook, then to a lab at MIT, and finally to the hacker group Anonymous. Specifically, a tool that Facebook use is called “social reporting”, which aims to improve the process of users reporting content they don’t like. Henry Lieberman also developed an algorithm in MIT that can catch troublesome material before it gets posted. Another example from the Bazelon article is sending evidence of the abuse to principals and superintendents.
For Reynolds’ article, an important finding is that “Cyberbullying is comparatively rare and most young people who report cyberbullying are also bullied in other ways”, which suggests that we should first try to prevent regular bullying. We thought this suggestion was very practical and we shouldn’t specifically target cyberbullying.
The Pew survey shows four major themes about future online climate, and some people believe technology will improve the online climate, such AI while some people disagree, thinking to troll is to be human.
Cunningham et al. suggest that ads with famous people talking about the effects of cyberbullying could be impactful and they also talked about teaching kids students to prevent cyberbullying like encouraging them to report incidents and terminating the internet privileges of students involved as perpetrators. And we thought the questionnaire method they used is also interesting – “Adaptive Choice-Based Conjoint Analysis”.
Then, we discussed which method is the most and the least effective. We both agreed that the most effective one is the algorithm developed by Lieberman because it can track bad posts before they are actually posted and is able to successfully flag 4 of 5 trolls. For the least effective one, we thought it could be Kal Penn’s method that raising money for Syrian refugees. It was awesome for sure but it was more like a campaign rather than a method to reduce cyberbullying.
For the system we designed, we try to combine most of the methods we read in the articles – we would like to apply the Lieberman algorithm first and Facebook and other sites can continue to review flagged and reported posts. And then we can use some of the students’ suggestions, like showcasing advertisements with famous people talking about cyberbullying. Additionally, social media can also put some tips or suggestions in their ads position, as another way to educate people that trolls hurt.
Reflecting on the assignment, it was good to read more about cyberbullying, because this connected to the most recent previous assignment. The last assignment focused more on what it is and why people did it, but this assignment focused more on ways to stop it, as well as how prominent of a problem it is. Learning about different aspects of the same topic was valuable so I could gain a greater and fuller understanding of cyberbullying as a whole.
It was very cool to learn about some of the potential ideas to counteract cyberbullying. For example, one that stuck out to me was the algorithm that can catch troublesome posts before they’re sent out. I thought that was fascinating, because I did not know that was possible!
It was also surprising for me to learn that cyberbullying is such an uncommon activity. Since I have seen so many campaigns and commercials against it, I assumed that it was a very prevalent issue. If I wasn’t in this course, I probably wouldn’t have learned about this, and would have kept believing that it affects so many people. However, even though it is not common, it still exists, and is very horrible, so I definitely support all attempts to stop it!
Also, once again, it was nice to chat with Weiyan. Discussing with another person can be valuable when you are required to read so many articles. We talked about many of the ideas and concepts together, which allowed us to better understand the topics we read about!
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