I’ll leave analyzing these tweets as an exercise to the reader, but they certainly appear to prey on the hot button issues in a few places. Which tweets were the most amplified (likes, retweets) by language? SELECT language, content, updates FROM ( SELECT language, content, updates, RANK () OVER ( PARTITION BY language ORDER BY updates DESC ) AS tweet_rank FROM `silverlock-bigquery.public_datasets.fivethirtyeight_troll_tweets` GROUP BY language, updates, content ) troll_tweets WHERE tweet_rank = 1 GROUP BY language, content, updates ORDER BY updates DESC LIMIT 10 Which tweets were the most amplified in each language?.Was there a specific account with a non-negligible fraction of tweets?.OK, let’s take a quick look at the data to get you thinking about it. We don’t get details on the followers themselves however, which makes it hard to know how impactful the reach was: is it trolls/bots followed by other trolls, or members of the general Twitter populace? Analyzing It ![]() So with the data above, what can we do? We can look at how these tweets were amplified (updates), what language the tweet was posted in (what audience was it for?), and the direct audience of the account (followers). From there, you can inspect the table russian_troll_tweets, look at the schema (also pasted below), and see a preview of the data. You’ll see the fivethirtyeight_russian_troll_tweets table appear on the left-hand-side, in the Resource tab. We’re going to use the BigQuery web UI, so navigate to the BigQuery interface and select the project you want to access it from. # standardSQL SELECT author, COUNT ( * ) AS tweets, followers FROM `silverlock-bigquery.public_datasets.fivethirtyeight_troll_tweets` GROUP BY author, followers ORDER BY tweets DESC, followers DESCįor everyone else: read on.
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