An Investigation of Tweets Submited by Using Music Player Applications release_7lbje75bjjhmva5vdf53w3n6na

Abstract

What users are doing at a certain point in time is important for designing various services and applications in social media, such as targeted advertisement, news recommendation, and real-world analysis. As a result, in this study, we investigated tweets which users submitted when they were listening to music by using music player applications. We collected 2,000 tweets including hashtags generated by music player applications and found about 65% of them were tweets where impressions were described, 15 % of them were tweets where reasons why users were listening to music were described, and 10 % of them were tweets where actions while listening to music were described. We applied machine learning techniques to detect tweets where two kinds of actions while listening to music, moving to somewhere or going to bed, were described. The experimental result shows that our method is useful for providing behavior based services and applications in social media.
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