In prior posts here and here, I analyzed new data obtained from FDA through the Freedom of Information Act about FOIA requests. I looked at response times and then started to dive into the topics that requesters were asking about. This is the third and final post on this data set, and it builds on the last post by taking the topics identified there to explore success rates by topic. From there, I look at who is asking about those topics and how successful those individual companies are in their requests.
Continuing my three-part series on FOIA requests using a database of over 120,000 requests filed with FDA over 10 years (2013-22), this month’s post focuses on sorting the requests by topic and then using those topics to dive deeper into FDA response times. In the post last month, I looked at response times in general. This post uses topic modeling, a natural language processing algorithm I’ve used in previous blog posts, including here and here, to discern the major topics of these requests.
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