This paper presents the results of the WASSA 2024 shared task on predicting empathy, emotion, and personality in conversations and reactions to news articles. Participating teams were given access to a new, unpublished extension of the WASSA 2023 shared task dataset. This task is both multi-level and multi-modal: data is available at the person, essay, dialog, and dialog-turn levels and includes formal (news articles) and informal text (essays and dialogs), self-report data (personality and distress), and third-party annotations (empathy and emotion). The shared task included a new focus on conversations between humans and LLM-based virtual agents which occur immediately after reading and reacting to the news articles. Participants were encouraged to explore the multilevel and multi-modal nature of this data. Participation was encouraged in four tracks: (i) predicting the perceived empathy at the dialog level, (ii) predicting turn-level empathy, emotion polarity, and emotion intensity in conversations, (iii) predicting state empathy and distress scores, and (iv) predicting personality. In total, 14 teams participated in the shared task. We summarize the methods and resources used by the participating teams.