A few days ago I published a little summary of online disinformation campaign for the Spanish far right .
I tried to highlight the emerging patterns of how these amplification machines operate. Shoving retweets distribution for users rt-ing one of the top-talker accounts.
To obtain the archive of tweets used for this stufy I compiled all the tweets from users that have retweeted the account @ldpsincomplejos.
Here is how I obtained the tweets for the account @ldpsincomplejos was obtained:
def get_tweets_search(key, client): tweets_endpoint = "https://api.twitter.com/1.1/search/tweets.json?q=" + key + "&count=500&include_entities=true&result_type=recent" response, data = client.request(tweets_endpoint) return json.loads(data)
This function will return 500 tweets, including both tweets by the account and RTs to the account. For each tweet I extracted the username and got the latest 200 tweets user timeline.
def get_timeline(username, client): timeline_endpoint = "https://api.twitter.com/1.1/statuses/user_timeline.json?screen_name=" + username + "&include_rts=true&count=200" response, data = client.request(timeline_endpoint) return json.loads(data)
Now I identified a few top-talkers and computed the distribution of RTs to Accounts.
Here are some results:
Here is the thing. If you look at the distribution of comments instead you get a different result.
This means in practice that there is a list of accounts RT-ing sources on the right and commenting on accounts from the left. The anticipation machines objective is in fact to spread division and hate.
Finally here is a visualization of the graph of accounts RTin OkDiario:
If we isolate the accounts doing more than 100 RT, we find a group of approximately 20 accounts doing most of the tweets.
Here is probably a team of accounts acting to promote the tweets from OkDiario and a group of other accounts.
You can have a look at the data used in this study.
If you get in touch I can give you access.