In recent years, so-called big data research has become a hot topic in the social sciences. This paper explores the possibilities of big data-based research within the field of music psychology. We illustrate one methodological approach by studying involuntary musical imagery, or earworms in the social networking service Twitter. Our goal was to collect a large naturalistic and culturally diverse database of discussions and to classify the encountered expressions. We describe our method and present results from automatic data classification and sentiment analyses. Over six months, we collected over 80,000 tweets from 173 locations around the world to obtain the most diverse dataset collated to date related to involuntary musical imagery. Automated classifications categorized 51% of all tweets gathered, with over 90% accuracy in each category. The most prominent categories of discussion concerned reporting earworm experiences, hyperlinks to music, spreading general information about the phenomenon, and communicating thankfulness (sincerely or ironically) about receiving earworms. Sentiment analysis revealed a balance towards negative emotional expressions in comparison to reference data. This is the first study to show this negative appraisal tendency and to demonstrate the ‘earworm’ phenomenon on a global scale. We discuss our findings in relation to previous literature and highlight the opportunities and challenges of big data research.