How Twitter bots helped in Hillary Clinton's demise
The polls had her winning. Prediction models had her winning. Financial markets had her winning. But Twitter had Hillary Clinton losing the US presidential election, with a steady downtrend in sentiment that foreshadowed her stunning defeat to Donald Trump.
The 2016 campaign featured more than 300 million tweets mentioning Clinton or Trump, with the Republican commanding a dominant two-to-one share of that conversation. Both candidates faced unprecedented unfavorable ratings among voters, but sentiment on Twitter gave Trump an edge. From his June 16, 2015, campaign announcement through Election Day, tweets mentioning Trump were 51pc positive (+1). That compares to 51pc negative (-1) for Clinton since her announcement on April 12, 2015, according to data from social media analytics firm Brandwatch.
Clinton's Twitter sentiment wasn't just net-negative throughout the campaign.
Amid relentless attacks from Trump and repeated blows from leaks of her staff's e-mails and investigations into her own, it also became more and more negative.
A 200-day moving average of those scores - smoothing the volatility of day-to-day swings -reveals a near-constant decline in sentiment toward Clinton.
Social media analysts call real-time Twitter data "the firehose" because it flows so thick and fast.
Their challenge is to build algorithms that collect millions of tweets about a topic, and then code those tweets as positive or negative-even to the level of individual phrases-so a tweet like "Hillary Clinton is wonderful but Donald Trump is terrible" is rated as a positive mention for Clinton and a negative one for Trump.
Of the hundreds of millions of tweets mentioning either candidate's name or a selection of candidate-specific hashtags like #ImWithHer or #TrumpTrain, about 20pc passed through Brandwatch's filter to be coded as positive or negative, while the rest are marked neutral. The trends in those positive and negative mentions are what corporate clients like Wal-Mart and Dell use to gauge the mood of their customers - and what can help reveal the mood of voters toward Clinton and Trump.
Some of these tweets come from users that aren't voters, aren't Americans, or aren't even humans at all. The proliferation of "bots," automated accounts that blast out hundreds of pro- or anti-candidate tweets per day, has muddied the social media waters. According to researchers at the University of Southern California, bots could account for up to a fifth of the 2016 presidential election conversation on Twitter, and it disproportionately came from Trump's side.
A study by Oxford University's Project on Computational Propaganda found that pro-Trump bots out-tweeted pro-Clinton bots at a seven-to-one rate during the final presidential debate on October 19. However, bot activity wasn't enough to move the data in this analysis - Clinton scored +5 on October 19, while Trump scored -7 - potentially because those automated tweets are less likely to contain the kind of natural human language that sentiment algorithms like Brandwatch's are looking for.
Overall, 17 of the 19 fateful days between the final October 19 debate and the November 8 election were net-positive for Trump on Twitter. For Clinton, only five of those 19 days were net-positive, as last-minute revelations about the investigation into her private e-mail server buffeted the Democrat's campaign.
Pollsters fear that their historic miss in the 2016 election was because some voters refused to admit their allegiance to Trump. For those voters, Twitter may have served as their confessional. (Bloomberg)