SenTopX: Benchmark for User Sentiment on Various Topics

TL;DR

This paper introduces SenTopX, a large longitudinal Twitter dataset of 143K users over 14 years, enabling in-depth analysis of toxic sentiment patterns across topics and user behaviors.

Contribution

It provides the first longitudinal dataset of Twitter users with topic modeling and toxicity scores, facilitating new insights into toxic user behavior over time.

Findings

Longitudinal analysis reveals distinct toxicity patterns across user groups.

Topic-based categorization helps understand user toxicity profiles.

Dataset enables future research on platform moderation and user behavior.

Abstract

Toxic sentiment analysis on Twitter (X) often focuses on specific topics and events such as politics and elections. Datasets of toxic users in such research are typically gathered through lexicon-based techniques, providing only a cross-sectional view. his approach has a tight confine for studying toxic user behavior and effective platform moderation. To identify users consistently spreading toxicity, a longitudinal analysis of their tweets is essential. However, such datasets currently do not exist. This study addresses this gap by collecting a longitudinal dataset from 143K Twitter users, covering the period from 2007 to 2021, amounting to a total of 293 million tweets. Using topic modeling, we extract all topics discussed by each user and categorize users into eight groups based on the predominant topic in their timelines. We then analyze the sentiments of each group using 16 toxic scores. Our research demonstrates that examining users longitudinally reveals a distinct perspective on their comprehensive personality traits and their overall impact on the platform. Our comprehensive dataset is accessible to researchers for additional analysis.