This paper scrutinizes a database of over 4900 YouTube videos to characterize financial market coverage. Financial market coverage generates a large number of videos. Therefore, watching these videos to derive actionable insights could be challenging and complex. In this paper, we leverage Whisper, a speech-to-text model from OpenAI, to generate a text corpus of market coverage videos from Bloomberg and Yahoo Finance. We employ natural language processing to extract insights regarding language use from the market coverage. Moreover, we examine the prominent presence of trending topics and their evolution over time, and the impacts that some individuals and organizations have on the financial market. Our characterization highlights the dynamics of the financial market coverage and provides valuable insights reflecting broad discussions regarding recent financial events and the world economy.

This paper addresses a new problem concerning the evolution of influence relationships between communities in dynamic social networks. A weighted temporal multigraph is employed to represent the dynamics of the social networks and analyze the influence relationships between communities over time. To ensure the interpretability of the knowledge discovered, evolution of the influence relationships is assessed by introducing the Granger causality. Through extensive experiments, we empirically demonstrate the suitability of our model for studying the evolution of influence between communities. Moreover, we empirically show how our model is able to accurately predict the influence of communities over time using random forest regression.