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>> No.58811854 [View]
File: 67 KB, 1366x650, histogram.png [View same] [iqdb] [saucenao] [google]
58811854

>>58810976
lmao you reminded me of that time I made a script to download Hina's tweets to make a histogram of her tweeter activity. This was at the time where we suspected she might be living in Argentina and not in Japan. We used to be better schizos.

>> No.44404580 [View]
File: 67 KB, 1366x650, histogram.png [View same] [iqdb] [saucenao] [google]
44404580

>>44404473
>>44404152
I analyzed her twitter activity but I didn't think it was conclusive proof.However it made me think something was off. Look at the times. Green activity was after "moving to another house" and blue is before that.

>> No.34092895 [View]
File: 67 KB, 1366x650, histogram.png [View same] [iqdb] [saucenao] [google]
34092895

So this is the project I've been working on. I made a simple twitter scraper to download every tweet, retweet and reply from an account. It is very unfortunate that Twitter doesn't give you the timestamp for user likes because I believe those make up most of the user activity on the site. But I had to work with whatever information Twitter does make available.

Given around 3250 tweets, which span from April 29th to the present day, September 26th, I made a histogram. I made sure to separate tweets based on "before and after August 28th" criteria, which was the day Hina tweeted she had her last meal in her old house. I was hoping to find a discrepancy in the activity before and after that date but there really wasn't any. Still, it is hard to infer a lot from this very limited amount of information, but I had to try.

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