Understanding Troll Writing as a Linguistic Phenomenon

TL;DR

This study explores the linguistic features of troll tweets, developing a neural network classifier with 91% accuracy and analyzing sociolinguistic factors influencing troll writing.

Contribution

It introduces a neural network model for classifying troll tweets and provides a sociolinguistic analysis of features characteristic of troll discourse.

Findings

Neural network achieved 91% accuracy in classifying troll tweets.

Identified sociolinguistic features that make troll tweets distinguishable.

Found distributional anomalies in topics and vocabulary of troll messages.

Abstract

The current study yielded a number of important findings. We managed to build a neural network that achieved an accuracy score of 91 per cent in classifying troll and genuine tweets. By means of regression analysis, we identified a number of features that make a tweet more susceptible to correct labelling and found that they are inherently present in troll tweets as a special type of discourse. We hypothesised that those features are grounded in the sociolinguistic limitations of troll writing, which can be best described as a combination of two factors: speaking with a purpose and trying to mask the purpose of speaking. Next, we contended that the orthogonal nature of these factors must necessarily result in the skewed distribution of many different language parameters of troll messages. Having chosen as an example distribution of the topics and vocabulary associated with those topics, we showed some very pronounced distributional anomalies, thus confirming our prediction.