Social Media Perceptions of 51% Attacks on Proof-of-Work Cryptocurrencies: A Natural Language Processing Approach

TL;DR

This study analyzes social media sentiments and emotions related to 51% attacks on proof-of-work cryptocurrencies, revealing commonality and nuanced reactions through NLP-based data analysis.

Contribution

First comprehensive NLP-based analysis of social media perceptions of 51% attacks, including data collection, sentiment profiling, and temporal analysis across multiple cryptocurrencies.

Findings

Attacks are more common than perceived.

Sentiment and emotion profiles vary during attacks.

Reactions include expected and surprising insights.

Abstract

This work is the first study on the effects of attacks on cryptocurrencies as expressed in the sentiments and emotions of social media users. Our goals are to design the methodologies for the study including data collection, conduct volumetric and temporal analyses of the data, and profile the sentiments and emotions that emerge from the data. As a first step, we have created a first-of-its-kind comprehensive list of 31 events of 51% attacks on various PoW cryptocurrencies, showing that these events are quite common contrary to the general perception. We have gathered Twitter data on the events as well as benchmark data during normal times for comparison. We have defined parameters for profiling the datasets based on their sentiments and emotions. We have studied the variation of these sentiment and emotion profiles when a cryptocurrency is under attack and the benchmark otherwise, between multiple attack events of the same cryptocurrency, and between different cryptocurrencies. Our results confirm some expected overall behaviour and reactions while providing nuanced insights that may not be obvious or may even be considered surprising. Our code and datasets are publicly accessible.