Controversy encompasses content that draws diverse perspectives, along with positive and negative feedback on a specific event, resulting in the formation of distinct user communities. we explore the explainability of controversy through the lens of SHAP (SHapley Additive exPlanations) method, aiming to provide a fair assessment of the individual contributions of different text features of tweets to controversy detection. We conduct an analysis of topic discussions on Twitter from a community perspective, investigating the role of text in accurately classifying tweets into their respective communities. To achieve this, we introduce a SHAP-based pipeline designed to quantify the influence of impactful text features on the predictions of three tweet classifiers. Text content alone offers interesting controversy detection accuracy. It can contain predictive features for controversy detection. For instance, negative connotations, pejorative tendencies and positive qualifying adjectives tend to impact the controversy model detection.