Sentiment Analysis of Cybersecurity Content on Twitter and Reddit

TL;DR

This study analyzes cybersecurity-related content on Twitter and Reddit to measure sentiment using NLP algorithms and human comparison, revealing predominantly positive or neutral opinions with moderate algorithm accuracy.

Contribution

It provides a comparative analysis of sentiment in cybersecurity content on social media platforms using both automated and human classification methods.

Findings

Cybersecurity content is mostly positive or neutral on both platforms.

VADER achieved 60% accuracy on Twitter and 70% on Reddit.

Positive sentiment significantly outweighs negative sentiment.

Abstract

Sentiment Analysis provides an opportunity to understand the subject(s), especially in the digital age, due to an abundance of public data and effective algorithms. Cybersecurity is a subject where opinions are plentiful and differing in the public domain. This descriptive research analyzed cybersecurity content on Twitter and Reddit to measure its sentiment, positive or negative, or neutral. The data from Twitter and Reddit was amassed via technology-specific APIs during a selected timeframe to create datasets, which were then analyzed individually for their sentiment by VADER, an NLP (Natural Language Processing) algorithm. A random sample of cybersecurity content (ten tweets and posts) was also classified for sentiments by twenty human annotators to evaluate the performance of VADER. Cybersecurity content on Twitter was at least 48% positive, and Reddit was at least 26.5% positive. The positive or neutral content far outweighed negative sentiments across both platforms. When compared to human classification, which was considered the standard or source of truth, VADER produced 60% accuracy for Twitter and 70% for Reddit in assessing the sentiment; in other words, some agreement between algorithm and human classifiers. Overall, the goal was to explore an uninhibited research topic about cybersecurity sentiment