A novel strategy for quickly identifying twitter trolls
By accounting for distinctive patterns of repetition, as few as 50 tweets
are needed for detection

Date:
August 12, 2020
Source:
PLOS
Summary:
Two algorithms that account for distinctive use of repeated words
and word pairs require as few as 50 tweets to accurately distinguish
deceptive 'troll' messages from those posted by public figures.



FULL STORY ==========================================================================
Two algorithms that account for distinctive use of repeated words and
word pairs require as few as 50 tweets to accurately distinguish deceptive "troll" messages from those posted by public figures. Sergei Monakhov of Friedrich Schiller University in Jena, Germany, presents these findings
in the open- access journal PLOS ONE on August 12, 2020.


========================================================================== Troll internet messages aim to achieve a specific purpose, while also
masking that purpose. For instance, in 2018, 13 Russian nationals
were accused of using false personas to interfere with the 2016
U.S. presidential election via social media posts. 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.

Monakhov took a sociolinguistic approach, focusing on the idea that
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. Using a library of Russian troll tweets and genuine tweets
from U.S. congresspeople, Monakhov showed that these troll-specific restrictions result in distinctive patterns of repeated words and word
pairs that are different from patterns seen in genuine, non-troll tweets.

Then, Monakhov tested an algorithm that uses these distinctive patterns
to distinguish between genuine tweets and troll tweets. He found that
the algorithm required as few as 50 tweets for accurate identification of trolls versus congresspeople. He also found that the algorithm correctly distinguished troll tweets from tweets by Donald Trump -- which although provocative and "potentially misleading," according to Twitter, are not
crafted to hide his purpose.

This new strategy for quickly identifying troll tweets could help
inform efforts to combat hybrid warfare while preserving freedom of
speech. Further research will be needed to determine whether it can
accurately distinguish troll tweets from other types of messages that
are not posted by public figures.

Monakhov adds: "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. Using the ratio
of their proportions as a quantitative measure, one needs as few as 50
tweets for identifying internet troll accounts."

========================================================================== Story Source: Materials provided by PLOS. Note: Content may be edited
for style and length.


========================================================================== Journal Reference:
1. Sergei Monakhov. Early detection of internet trolls: Introducing an
algorithm based on word pairs / single words multiple repetition
ratio.

PLOS ONE, 2020; 15 (8): e0236832 DOI: 10.1371/journal.pone.0236832 ==========================================================================

Link to news story: https://www.sciencedaily.com/releases/2020/08/200812144149.htm

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