  In the pursuit of bolstering user safety, social media platforms deploy
active moderation strategies, including content removal and user suspension.
These measures target users engaged in discussions marked by hate speech or
toxicity, often linked to specific keywords or hashtags. Nonetheless, the
increasing prevalence of toxicity indicates that certain users adeptly
circumvent these measures. This study examines consistently toxic users on
Twitter (rebranded as X) Rather than relying on traditional methods based on
specific topics or hashtags, we employ a novel approach based on patterns of
toxic tweets, yielding deeper insights into their behavior. We analyzed 38
million tweets from the timelines of 12,148 Twitter users and identified the
top 1,457 users who consistently exhibit toxic behavior, relying on metrics
like the Gini index and Toxicity score. By comparing their posting patterns to
those of non-consistently toxic users, we have uncovered distinctive temporal
patterns, including contiguous activity spans, inter-tweet intervals (referred
to as 'Burstiness'), and churn analysis. These findings provide strong evidence
for the existence of a unique tweeting pattern associated with toxic behavior
on Twitter. Crucially, our methodology transcends Twitter and can be adapted to
various social media platforms, facilitating the identification of consistently
toxic users based on their posting behavior. This research contributes to
ongoing efforts to combat online toxicity and offers insights for refining
moderation strategies in the digital realm. We are committed to open research
and will provide our code and data to the research community.
