Nearly two-thirds of the social media bots with political activity on Twitter before the 2016 U.S. presidential election supported Donald Trump. But all those Trump bots were far less effective at shifting people’s opinions than the smaller proportion of bots backing Hillary Clinton. As my recent research shows, a small number of highly active bots can significantly change people’s political opinions. The main factor was not how many bots there were – but rather, how many tweets each set of bots issued.
My work focuses on military and national security aspects of social networks, so naturally I was intrigued by concerns that bots might affect the outcome of the upcoming 2018 midterm elections. I began investigating what exactly bots did in 2016. There was plenty of rhetoric – but only one basic factual principle: If information warfare efforts using bots had succeeded, then voters’ opinions would have shifted.
I wanted to measure how much bots were – or weren’t – responsible for changes in humans’ political views. I had to find a way to identify social media bots and evaluate their activity. Then I needed to measure the opinions of social media users. Lastly, I had to find a way to estimate what those people’s opinions would have been if the bots had never existed.
Finding tweeters and bots
To narrow the research a bit, my students and I focused our analysis on the Twitter discussion around one event in the lead-up to the election: the second debate between Clinton and Trump. We collected 2.3 million tweets that contained keywords and hashtags related to the debate.
Then we made a list of the roughly 78,000 Twitter users who posted those tweets and constructed the network of who followed whom among those users. To identify the bots among them, we used an algorithm based on our observation that bots often retweeted humans but were not themselves frequently retweeted.
This method found 396 bots – or less than 1 percent of the active Twitter users. And just 10 percent of the accounts followed them. I felt good about that: It seemed unlikely that such a small number of relatively disconnected bots could have a major effect on people’s opinions.
A closer look at the people
Next we set out to measure the opinions of the people in our data set. We did this with a type of machine learning algorithm called a neural network, which in this case we set up to evaluate the content of each tweet, determining the extent to which it supported Clinton or Trump. Individuals’ opinions were calculated as the average of their tweets’ opinions.
Once we had assigned each human Twitter user in our data a score representing how strong a Clinton or Trump backer they were, the challenge was to measure how much the bots shifted people’s opinions – which meant calculating what their opinions would have been if the bots hadn’t existed.