[1] King, Gary, Jennifer Pan, and Margaret E. Roberts. “Reverse-engineering censorship in China: Randomized experimentation and participant observation.” Science 345.6199 (2014): 1251722.
[2] Hiruncharoenvate, Chaya, Zhiyuan Lin, and Eric Gilbert. “Algorithmically Bypassing Censorship on Sina Weibo with Nondeterministic Homophone Substitutions.” ICWSM. 2015.
Reflection #1
Paper 1 by King et al., have presented an interesting approach to reverse-engineering censorship in China. The experiment performed by the author looks more like a secret operation to analyze how censorship works in China. King et al., created accounts on various social media websites and submitted posts from them to analyze whether they get censored or not. The authors even created their own website and conducted interviews. Their approach was unique and interesting. However, I was not convinced why the authors only considered posts from all over the world between 8 AM and 8PM China time. How about the content being posted before 8 AM and after 8 PM? What I found interesting in the paper is the action hypothesis vs state critique hypothesis. Non-familiarity with the language is a major drawback in understanding it. The authors reported that Chinese social media organizations will hire 50,000 – 70,000 people who will act as human censors which is quite interesting and too less looking at the number of internet users in China.
Reflection #2
Paper 2 by Hiruncharoenvate et al., presents a non-deterministic algorithm for generating homophones that create a large number of false positives for censors. They claim that homophone-transformed weibos posted Sina Weibo remain on site three times longer than their previously censored counterparts. The authors have conducted two experiments – first where they posted original posts and homophone-transformed posts and found that although both the posts eventually were deleted, the homophone-transformed posts stayed 3 times longer. second, they analyze that native Chinese speakers on AMT were able to understand these homophone-transformed weibos. I wonder how this homophone-transformed approach will work in other languages? The dataset used consists of 11 million weibos which was collected from Freeweibo. Out of all the social science papers, we have read so far I found this paper most interesting and their approach well structured. It would be interesting to implement this approach in other languages as well.
