A much-discussed research paper out of Oxford this month concluded that millions of tweets about the presidential election are generated by highly automated Twitter accounts. According to the authors’ analysis, about a third of pro-Trump traffic, and one fifth of pro-Clinton tweets, is “driven by bots and highly automated accounts.”

The Oxford study pegged Twitter accounts as highly automated if they posted at least 50 times a day using any one of a group of election hashtags—such as #MAGA, #TrumpTrain, #ImWithHer, and #StrongerTogether—over a three-day period.

The paper conceded that “extremely active” humans might post 50 or more times per day on one of the 52 hashtags they selected, “especially if they are simply retweeting the content they find in their social media feed.”

At the Electome, a project of the Media Lab at MIT, we use complex machine learning algorithms to analyze the election conversation on Twitter. The Oxford paper made us curious about the possibility of spotting bots in the dashboard we recently built for journalists covering the election.

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Bot detection can be challenging, partly because they come in different varieties. Some are purely automated accounts, while others layer some manual curation on top of automated tweets.

Last week, we noticed a spike while searching our Twitter data on the keyword “rigged.”

In early September, the “rigged” discussion on Twitter, which previously had revolved around a variety of issues including economic inequality and the electoral process, shifted suddenly toward immigration—that is, tweets containing the word “rigged” also used terms connected to immigration.

Digging into the data, we found one verbatim tweet showing up across a dozen or so handles, each of which posted the same message over and over each day: “Immigration Policy is RIGGED against American Workers #Trump2016 #FeelTheBern.”

Beyond using identical phrasing—including idiosyncratic capitalization—the tweets coming from these accounts all linked to the same video, which compares statements by Donald Trump and Bernie Sanders about immigration policy. Each video, in turn, linked to the same anti-Clinton Twitter account.

Although the accounts don’t have the telltale bot profile image—the egg—based on their characteristics and activity, including breakneck output of strikingly similar content, these are clearly spam handles, and apparently at least somewhat automated.

Wading in further, we found that each account puts out a stream of photos and GIFs on a given theme, on top of a common rotation of anti-Clinton videos and memes.

The bots follow the same playbook: Publicly they tweet the same innocuous content fitting their theme, while simultaneously flooding the replies of public figures and media outlets—essentially piggybacking on famous tweets to influence users who see those tweets' replies—with campaign-driven videos and memes.

One apparent bot account has pumped out more than 27,000 tweets since its creation in March, with content that tends to mix videos of Clinton advisor John Podesta with memes from the 1970s film A Clockwork Orange: