<p>Social media has become an essential part of human life, providing people with a means of communication and information dissemination. However, the rise of antisocial behavior on these platforms can create a sense of anonymity within various groups, which may have negative consequences for society as a whole. At the individual level, aggressive behavior on social networks has become a concern, as it can lead to negative consequences, such as mental harm. This study aims to understand whether exposure to aggressive content can make users more aggressive. To investigate this, we analyzed a large dataset of Twitter posts, consisting of 15 million tweets from 1.5 million users. We manually annotated 6,000 English posts and designed a neural network based aggression detection model with 81\% accuracy. We then measure users’ aggressive behavior using a novel metric, ``user aggression intensity''. This metric takes into account the overall aggressive activity of users. By utilizing this metric, we profile users as aggressive or non-aggressive. The analysis of aggressive tweets of users and their feeds suggests that the content of one's social media feeds can influence aggressive behavior, and event-specific feeds can lead to increased aggression. Furthermore, the study revealed that users tend to support and encourage aggressive content on social media, which can contribute to the proliferation of antisocial behavior. Understanding the underlying causes of such behavior can help design effective strategies to mitigate its negative effects and promote a healthy social media environment.</p>