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New research from Emilio Ferrara, the University of Southern California academic who exposed the role of bots in the 2016 US election, shows that many Trump bots went dark and later turned into MacronLeaks bots. This, Ferrara wrote in a new paper posted to the arXiv preprint server this week (which is currently being peer-reviewed), suggests that there may be a "black market" for right-wing political bot that can lay dormant for months before being activated to promote the next conservative demagogue.
"These accounts were tweeting their support for Trump for about a week in the run-up to the 2016 election and then they went dark for a very long time," Ferrara said. "These same accounts picked up again and some even started tweeting in French—but the alt-right narrative was the same."
"To see a set of bots which had been pro-Trump in November targeting Macron in May could indicate a black market and that the use of those bots has been hired out to political actors," Nimmo said. "It could also be that the same actor who supported Trump in November decided to start supporting Le Pen."
According to both Nimmo and Ferrara, more work is needed to investigate the true scope and scale of political bots supporting right-wing candidates around the globe. To that end, the algorithm developed by Ferrara to analyze millions of tweets and pick out a few bots could be a huge help to researchers.
"It's shown that you can do a credible and accurate analysis on a very large amount of traffic," Nimmo said. "That's a technique worth using many times over, because there are lots of possible botnets out there."
Change people's minds via social engineering and psychology/disinformation? That has a whole lot more "plausible deniability" to it.
originally posted by: DanteGaland
a reply to: theantediluvian
I think MANY may actually know the BOTS helped....but don't care.
They won. Period.
They don't care HOW they won...just that they did WIN.
So you don't believe that people can be manipulated?
In other words, to what degree a person is assumed vulnerable to influence is completely a matter of political expediency.
It would hardly be a stretch to call this presidential race the Twitter election. The social media site has played a critical role in the campaigns of both Trump and Clinton as a medium for voter engagement and personal embarrassment, their feeds an endless stream of 140-character quotables that are endlessly debated by the Twittersphere's armchair analysts.
Bot detection: Determining whether either human or a bot controls a social media account has proven a very challenging task (Ferrara, et al., 2016; Subrahmanian, et al., 2016). Our prior efforts produced an openly accessible solution called BotOrNot (Davis, et al., 2016), consisting of both a public Web site (truthy.indiana.edu...) and a Python API (github.com...), which allow for making this determination. BotOrNot is a machine-learning framework that extracts and analyses a set of over one thousand features, spanning content and network structure, temporal activity, user profile data, and sentiment analysis to produce a score that suggests the likelihood that the inspected account is indeed a social bot. Extensive analysis revealed that the two most important classes of feature to detect bots are, maybe unsurprisingly, the metadata and usage statistics associated with the user accounts.