Toward an Understanding of Lyrics-viewing Behavior
While Listening to Music on a Smartphone

Kosetsu Tsukuda, Masahiro Hamasaki, Masataka Goto

Speaking words ofwisdom, let it be
Let it be, let it be,
let it be, let it be

Whisper words ofwisdom, let it be
Let It Be / The Beatles

 Some smartphone applications for online
music services (e.g., Spotify and Apple Music)
have provided a function that enables a user
to view song’s lyrics while listening to the song Such a function will become one of the mainmeans for viewing lyrics, given the currentsituation where music streaming services onsmartphones have become a mainstream format
for listening to music

National Institute of Advanced Industrial Science and Technology(AIST)

Various lyrics-related studies in MIR community
 Enable users to search for songs by words in lyrics
 Estimate the topics of lyrics for exploring songs
 Estimate the lyrics intelligibility for language learning
More fundamental question remains unexplored Why and how do people view lyrics?
 Investigate the behavior of viewing lyrics
on a smartphone while listening to music

WHY do people view lyrics?

HOW do people view lyrics?

Reasons

Lyrics Viewing Logs

Conduct an online survey involving 206 participants

ReasonConfirmation
Understanding
SingingStructure
Karaoke
BoredomLanguage
Writing

The user wants to:
Confirm what the artist singsMore deeply understand the lyrics
Sing to herself (not in public)
Figure out the structure of the lyrics,
such as verse and chorus
Practice for singing in public, as in karaoke
Get rid of her boredom by viewing lyrics
Learn a language with the lyrics
Study for writing lyrics

Analyze 23 million lyrics request logs for a year Collect logs from an iOS application of aJapanese online music service
 Logs include the timestamp, user ID, and song ID
 The application gets a song’s lyrics
Song ID
only when a user explicitly requests themLyrics
 The dataset (LyLog) consists of611,895 users, 214,434 songs, and 23,034,417 logs
 For comparison, music listening dataset of Last.fm (Lastfm)
consisting of 660 users, 718,466 songs, and 2,932,430 logs

Basic Statistics

Distribution of the number of logs per song

Frequency of Reasons There are 𝑦𝑦 songs thathave 𝑥𝑥 logs
 LyLog is more biased topopular songs: top 6.64%
of the popular songs
dominates 80% of wholelogs (34.8% for Lastfm)

Investigate how often users view lyrics for each reason

Provide functions according to users’ reasons for viewing lyrics:- For Understanding, displaying diverse interpretations of lyrics- For Singing and Karaoke, automatically judging singing skill- For Language, enabling users to see the meaning of a wordin lyrics just by tapping the word

Behavior Details

Investigate users’ detailed behavior in terms of three aspects(e.g., in terms of timing, for each reason, do users tend to decideto view lyrics before playing a song or after playing a song?)

Distribution of logs over the hours of the day People tend to view more
lyrics after coming backhome at night and before
going to bed
 Because viewing lyrics
requires users to interact
with the app more actively

Repeat Consumption

It has been reported that a user often listens to the same song
repeatedly over time; what about lyrics viewing behavior?

Fraction of repeat consumption for each user An average of 37.8% ofuser’s viewed lyrics havealready been viewed by
the user (60.4% for Lastfm)
 Still high compared toother domains (e.g., 26%for watching videos)Transition of temporal gap between consumptions

 Timing: for reasons with high frequency in the “After” group,
enable users to quickly execute the corresponding functions
 Percentage: for a user who stops viewing lyrics within a shorttime, provide information related to the played song
because she is likely bored

Small temporal gap

Large temporal gap

 When a user repeatedly views the lyrics of a song,
the gap grows over time as she gets bored with it For such a user, recommend novel lyrics related in termsof the topic to expand her interest to other songs

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