Phil 5.6.20


  • I looked at the COVID-19-TweetIDs GitHub project, and it is in fact lists of ids:
  • These can work by appending that number to the string “”, like this:
  • 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:


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


  • 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?