Scientists claim to be able to identify Twitter Trolls in 50 tweets using algorithms to that can detect 'distinctive' patterns of word repetition.
The algorithms identify linguistic patterns in tweets in order to distinguish deceptive 'troll' messages – which aim to achieve a specific purpose while also masking that purpose – from those posted by 'normal' Twitter users.
Surprisingly, Trolls start repeating words and word pairs later than normal users, due to their attempts to influence many different accounts at once.
While previous research has investigated distinguishing characteristics of troll tweets such as timing, hashtags, and geographical location, few studies have examined linguistic features of the tweets themselves.
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'Though troll writing is usually thought of as being permeated with recurrent messages, its most characteristic trait is an anomalous distribution of repeated words and word pairs,' said study author Sergei Monakhov at Friedrich Schiller University in Germany.
'Using the ratio of their proportions as a quantitative measure, one needs as few as 50 tweets for identifying internet troll accounts.'
Troll internet messages aim to influence particular people, spread fake news or sow discord, while also masking what they're doing.
In one high profile example of trolling in February 2018, the US government indicted 13 Russian nationals for interfering with the 2016 US presidential election through social media.
These individuals were accused of using false American personas to operate social media pages and groups designed to attract American audiences and cause discord by disparaging Democrat candidate Hilary Clinton.
The repercussions of this were far-reaching, almost leading to the impeachment of Trump, damaging the Russian economy due to imposed US sanctions and almost ruining relations between the two nations.
Trolls have a limited number of messages to convey, but must do so multiple times and with enough diversity of wording and topics to fool readers
'Taking into account the global scale of this scandal and its ever-widening ramifications for society, one can only wonder why the phenomenon of troll writing has not received to date any substantial scientific attention,' Monakhov says in his research paper.
For his study, Monakhov used a collection of tweets connected to the Internet Research Agency, a Russian government-owned ‘troll factory’, as Monakhov calls it.
Affectionately known as the Trolls from Olgino, the Internet Research Agency is a Saint Petersburg-based company engaged in online influence operations on behalf of Russian businesses.
Monakhov combined this dataset with a sample of 'genuine' or non-toll tweets from US congresspeople – accredited accounts.
He identified distinctive patterns of repeated words and word pairs that are different from linguistic patterns in tweets from the normal accredited Twitter users.
Monakhov showed that these troll-specific