The effects of real-world events on music listening behavior: an intervention time series analysis

We approach the research question whether real-world events, such as sport events or product launches, influence music consumption behavior. To this end, we consider events of different categories from Google Trends and model listening events as time series using Last.fm data. Performing an auto-regressive integrated moving average analysis to decompose the signal and subsequently an intervention time series analysis, we find significant signal discontinuities, in particular for the Google news category. We found that news and events are likely to increase the number of songs listened to per person per day by about 2%, while tech events commonly cause 1% less music being consumed.