Interpersonal musical entrainment—temporal synchronization and coordination between individuals in musical contexts—is a ubiquitous phenomenon related to music’s social functions of promoting group bonding and cohesion. Mechanisms other than sensorimotor synchronization are rarely discussed, while little is known about cultural variability or about how and why entrainment has social effects. In order to close these gaps, we propose a new model that distinguishes between different components of interpersonal entrainment: sensorimotor synchronization —a largely automatic process manifested especially with rhythms based on periodicities in the 100–2000 ms timescale—and coordination , extending over longer timescales and more accessible to conscious control. We review the state of the art in measuring these processes, mostly from the perspective of action production, and in so doing present the first cross-cultural comparisons between interpersonal entrainment in natural musical performances, with an exploratory analysis that identifies factors that may influence interpersonal synchronization in music. Building on this analysis we advance hypotheses regarding the relationship of these features to neurophysiological, social, and cultural processes. We propose a model encompassing both synchronization and coordination processes and the relationship between them, the role of culturally shared knowledge, and of connections between entrainment and social processes.

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.