Social broadcasting networks facilitate the public exchange of information and contain a large amount of stock-related information. This data is increasingly analyzed by research and practice to predict stock market developments. Insights from social broadcasting networks are used to support the decision-making process of investors and are integrated into automatic trading algorithms to react quickly to broadcasted information. However, a comprehensive understanding about the influence of social broadcasting networks on stock markets is missing. In this study, we address this gap by conceptualizing and empirically testing a model incorporating three dimensions of social broadcasting networks: users, messages, and discussion. We analyze 1.84 million stock-related Twitter messages concerning the S&P 100 companies between January and April 2014 and corresponding intraday stock market data from NYSE and NASDAQ. Our research model is constructed applying factor analyses and tested using a fixed effects panel analysis. The results show that the influence of social broadcasting on stock markets is driven by the message and discussion dimensions whereas the user dimension has no significant influence. Specifically, the influence of user mentions, financial sentiment, discussion reach, and discussion volume has the largest impact and should carefully be considered by investors making trading decisions.