Research On The Correlation Of Online Music Information Behavior

Music is an art form with a long history.The number of online music users in China is huge.This article uses NetEase cloud music as a research platform to study online music information behavior,because NetEase cloud music has many loyal users and distinctive features.The domestic research on music information behavior started late and the number is small.The existing research on music information behavior mainly focuses journalism and communication.This research focuses on online music information behavior and its correlation,which can enrich the research perspective and content of music information behavior.Chapter One Inroduction.This chapter has explained the background and significance of this topic and sorted out the domestic and foreign research on user music information behavior and correlation of online music information behavior as a theoretical reference for this article.This article has also combed the research content and ideas and introduced the research methods and tools.At last,the innovation of this article has been explained.Chapter Two Research design.This chapter has introduced the data source,including the introduction of Netease cloud music and the reason for the selection.Then it has described the data capture time,data acquisition method and data items captured.Chapter Three NetEase cloud music users and their information behavior.This chapter has studied the users of NetEase cloud music as a whole and devided them into three categories: singer,radio anchor and ordinary user.Then,their personal homepage data has been grabbed to analyze their characteristics.At last,the definition and characteristics of online music information have been explained.Chapter Four The relevance of online music information behavior in two functions.This chapter has choosed song list and radio station as the typical situations.The song lists and radio stations try to cover all music types equally as far as possible to ensure the comprehensiveness of the research.Then it has analyzed the correlation between the user muic information management behavior,sharing behavior,comment behavior and playback behavior by grabbing the data in the song list and radio station.It has also compared the similarities and differences of the correlation between the online music information behaviors in the song list and radio station.It is found that there are both a certain correlation between the users' music information behaviors in the song list and radio station,and there are differences between the two.Chapter Five Correlation of online music information behavior in different styles.Taking song list as the research unit,the music style selects folk music,rap,light music,ancient style.This chapter has explored the influence of style on the online music information behavior.Then this chapter has collected the data in each type of song list separately,compared and analyzed the user management behavior,sharing behavior,comment behavior and playback behavior.It is found that the differences in style have a certain impact on the user's collection,sharing,and playback behaviors,but there is no obvious impact on whether users comment on the song list.Then a comparative analysis of the relevance of online music information behavior in different styles has been made.It is found that in the four styles of playlists,except for a few cases where the correlation is not significant,there is a strong positive influence between collection behavior,sharing behavior,comment behavior,and playback behavior.In addition,the correlation between online music information behaviors in rap song lists is relatively strong.The correlation between online music information behaviors in folk song lists is relatively weak.Chapter Six Correlation of online music information behavior in different scenes.Taking song list as the research unit,the music scene selects morning,night,sports,afternoon tea,driving.This chapter has explored the influence of scene on the online music information behavior.Then this chapter has collected the data in each type of song list separately,compared and analyzed the user management behavior,sharing behavior,comment behavior and playback behavior.It is found that the differences in scene have a certain impact on the user's collection,sharing,and playback behaviors,but there is no obvious impact on whether users comment on the song list.Then a comparative analysis of the relevance of online music information behavior in different scenes has been made.It is found that in the five scenes of playlists,except for a few cases where the correlation is not significant,there is a moderate and above positive influence between collection behavior,sharing behavior,comment behavior,and playback behavior.In addition,the correlation between online music information behaviors in night song lists is relatively strong,and the correlation between collection behavior and other behaviors is particularly obvious.The correlation between online music information behaviors in afternoon tea song lists is relatively weak.Chapter Seven Conclusion.This chapter has summarized the entire study,research results and shortcomings of the article,and also has put forward related suggestions and research prospects.