#COVID

• I looked at the COVID-19-TweetIDs GitHub project, and it is in fact lists of ids:

  1219755883690774529
  1219755875407224832
  1219755707001659393
  1219755610494861312
  1219755586272813057
  1219755378428338181
  1219755293397012480
  1219755288988798981
  1219755197645279233
  1219755157438828545

• These can work by appending that number to the string “twitter.com/anyuser/status/”, like this: twitter.com/anyuser/status/1219755883690774529
• The way to get the text in Python appears to be tweepy. This snippet from stackoverflow appears to show how to do it, but I haven’t verified yet.

  import tweepy
  consumer_key = xxxx
  consumer_secret = xxxx
  access_token = xxxx
  access_token_secret = xxxx
  
  auth = tweepy.OAuthHandler(consumer_key, consumer_secret)
  auth.set_access_token(access_token, access_token_secret)
  
  api = tweepy.API(auth)
  
  tweets = api.statuses_lookup(id_list) # id_list is the list of tweet ids
  tweet_txt = []
  for i in tweets:
      tweet_txt.append(i.text)

   

GPT-2 Agents

• Continuing with PGNtoEnglish
  • Figuring out how to parse the moves text, using the wonderful regex101 site
• 4:30 meeting
  • We set up an Overleaf project with the goal to submit to the Harvard/Kennedy Misinformation Review
  • We talked about the GPT-2 as a way of clustering tweets. Going to try finetuning with some Arabic novels first to see if it can work in that language

GOES

• Continuing with the MLP sequence-to-sequence NN
  • Getting the data to fit into nice, rectangular arrays, which is no straightforward, since the time window of the query can return a varying number of results. So I have to run the query, then trim the arrays down so that they are all the length of the shortest. Here’s the results:

• I’ve got the training and prediction working pretty well. Stopping for the day

• Tomorrow I’ll get the models to write out and read in
• 2:00 status meeting
  • Two weeks to getting the sim running?