TOWARD AN UNDERSTANDING OF LYRICS-VIEWING BEHAVIOR
WHILE LISTENING TO MUSIC ON A SMARTPHONE
Kosetsu Tsukuda
Masahiro Hamasaki
Masataka Goto
National Institute of Advanced Industrial Science and Technology (AIST), Japan{k.tsukuda, masahiro.hamasaki, m.goto}@aist.go.jp
ABSTRACTWhy and how do people view lyrics? Although various
lyrics-based systems have been proposed in MIR community, this fundamental question remains unexplored. Better
understanding of lyrics viewing behavior would be beneficial for both researchers and music streaming platforms
to improve their lyrics-based systems. Therefore, in thispaper, we investigate why and how people view lyrics, especially when they listen to music on a smartphone. To answer “why,” we conduct a questionnaire-based online user
survey involving 206 participants. To answer “how,” weanalyze over 23 million lyrics request logs sent from thesmartphone application of a music streaming service. Our
analysis results suggest several reusable insights, including
the following: (1) People have high demand for viewing
lyrics to confirm what the artist sings, more deeply understand the lyrics, sing the song, and figure out the structuresuch as verse and chorus. (2) People like to view lyrics after returning home at night and before going to sleep rather
than during the daytime. (3) People usually view the same
lyrics repeatedly over time. Applying these insights, we
also discuss application examples that could enable people
to more actively view lyrics and listen to new songs, which
would not only diversify and enrich people’s music listening experiences but also be beneficial especially for musicstreaming platforms.
1. INTRODUCTION
When people seek help in identifying a particular songthat they have listened to, they often provide words in thesong’s lyrics as a clue for identification [1,2]. In other situations when people listen to music, it has been reported thatthey choose songs according to not only the musical audio
content, such as the music genre, mood, melody, vocal timbre, and rhythm, but also the topics of lyrics [3,4]. To meetthese demands, in the field of Music Information Retrieval(MIR), researchers have proposed systems for identifying
a song by using the words in lyrics as a query [5–8] andsystems for exploring songs according to the topics estimated from lyrics [9–12]. As illustrated here, lyrics arean essential element of music for both listeners and MIR
researchers.
Despite the importance of lyrics in the MIR community,
more fundamental investigation of lyrics remains an underaddressed topic: why and how do people view lyrics? In
this paper, we aim to answer these questions. Investigating
people’s lyrics-viewing behavior and revealing reusable
insights would be beneficial for researchers and musicstreaming platforms to implement lyrics-related systems
and functions, such as viewing support for lyrics and song
recommendation based on lyrics. With regard to music listening, researchers have investigated why and how people listen to music [13–20], and the obtained insights havecontributed to later studies in the MIR community. Although listening to music includes listening to sung lyrics,
our study differs from these studies in that we focus on
lyrics-viewing behavior.
Users can view lyrics in various ways, such as a lyrics
sheet included with a compact disc (CD), a web service
for lyrics search, and a YouTube video with lyrics overlaid [21]. Recently, some smartphone applications for online music services (e.g., Spotify and Apple Music) haveprovided a function that enables a user to view song’s lyrics
while listening to the song. Such a function will become
one of the main means for viewing lyrics, given the current situation in which music streaming services on smartphones have become a mainstream format for listening tomusic [15]. In light of the above, we investigate the behavior of viewing lyrics on a smartphone while listening
to music, because we can make the obtained insights morereusable for future work in MIR community.
Our main contributions can be summarized as follows:• To our knowledge, this is the first study on the interactions between users and lyrics in terms of why andhow users view lyrics when they listen to music.
• To investigate why users view lyrics, we conducteda large-scale questionnaire-based online survey involving 206 participants. In the survey, more than
75% of the participants answered that they often
view lyrics to confirm what an artist sings or moredeeply understand lyrics. Moreover, over 50% of
the participants often view lyrics to sing a song or
figure out the structure of the lyrics (verse, chorus,etc.). These results are beneficial for both MIR researchers and music streaming platforms to implement their systems or functions. In fact, in this paper, we suggest examples of functions to support© Kosetsu Tsukuda, Masataka Goto. Licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0). Attribution: Kosetsu Tsukuda, Masataka Goto, “Toward an Understandingof Lyrics-viewing Behavior While Listening to Music on a Smartphone”,
in Proc. of the 22nd Int. Society for Music Information Retrieval Conf.,
Online, 2021.

705

Proceedings of the 22nd ISMIR Conference, Online, November 7-12, 2021

users according to their reasons for viewing lyrics,
such as a function that displays tips to sing each partof the song’s lyrics for users who want to sing.
• We investigated how users view lyrics by analyzingover 23 million lyrics request logs for over 600 thousand smartphone users for a year on a music streaming service. The data shows that people tend to view
more lyrics after coming back home at night and before going to bed. In addition, an average of 37.8%
of user’s viewed lyrics have already been viewed
by the user, and eventually the user gets bored withviewing the same lyrics. Considering these findings,
we make several proposals for music streaming platforms to attract users (e.g., when a user gets boredwith the lyrics of a song, the platform could suggest
related lyrics in terms of the topic).
2. RELATED WORK
2.1 User Behavior in Music Listening
One approach to analyze user behavior in music listening
is conducting user studies based on questionnaires and interviews. Typical questions about music listening ask why
people listen to music [16–18, 20] and how they use music
websites, services, and applications [1, 15, 22]. Regarding
the former question, the main reasons include emotional
reasons such as relaxation [17] (even at work [18]) and relief [16]. People also listen to music to concentrate andto pass time [20]. Regarding the latter question, Lee andWaterman [15] revealed that people use music websites
and applications for various reasons such as discovering
new music and learning about the artists. They also compared their results with those in 2004 [1] and showed increases in the popularity of music streaming and mobile
music consumption. A more recent work conducted a survey on the use of cloud music services and considered thefuture design of such services [22]. Moreover, Lee andPrice [14] conducted interviews with music listeners andrevealed seven typical personas, such as a user who enjoys
curating music that is already familiar and a user who enjoys serendipitous music discovery.
Another approach is analyzing users’ play logs. These
logs are typically collected from (1) APIs provided by online music services [23, 24] or (2) Twitter, where tweets
related to music listening are gathered via specific tags
such as “#nowplaying” and “#itunes” [25–27]. Logs havebeen analyzed in terms of various aspects, including thelong tail distribution of listening events per user, track,and artist [23, 24, 27], the popularity of genres, moods,
and tags [25–27], and the temporal distribution (hour of
day and day of week) [23, 26], etc. One characteristic ofmusic listening behavior is repeat consumption [28]. Reports have indicated that, in a user’s music play logs, about
70% of played songs have already been played before, andthis percentage is much higher than for other domains suchas viewing videos and visiting restaurants [28, 29]. In repeat consumption, the number of times a song is played isheavy-tailed (i.e., a user repeatedly listens to a small proportion of songs again and again). Benson et al. [29] re706

ported that each song has its own lifetime for a user: at the
beginning of the lifetime, the temporal gap between listening events is small; but at the end of the lifetime, the gap
becomes large, and eventually the user becomes bored withthe song.
Although listening to sung lyrics is one factor in listening to music, our study differs from the above studies inthat we particularly focus on lyrics viewing behavior. Focusing on a particular element of music is beneficial to suggest new possibilities for future research as was indicatedby Demetriou et al. [4] who focused on vocals. Researchon why people listen to music has tended to involve user
studies, because they have the advantage of enabling researchers to ask questions to analyze people’s intent. In
contrast, research on how people listen to music has often
analyzed large log data to take advantage of statistical processing. Applying both of these advantages, in this paper,
we investigate why and how people view lyrics by using
questionnaires and logs, respectively.
2.2 Lyrics in MIR
Researchers have considered lyrics in various studies,including lyrics-to-audio alignment [30–36], analysis of
lyrics characteristics [37–42], accurate lyrics retrieval [43–45], and genre and mood classification [46–51]. Below, we
review more related studies that aim to support user activity by using lyrics.
One major approach is enabling users to search forsongs by words in lyrics, in which a query can be text [5,6]or user’s sung lyrics [7, 8]. Systems have also been proposed for exploring songs according to topics estimatedfrom lyrics [9–12]. Fujihara et al. [52] proposed theconcept of a “Music Web” in which songs are hyperlinked to each other based on phrases of lyrics. Visualization is also a useful approach to browse a music collection. SongWords [53] displays a music collection on a
two-dimensional canvas based on self-organizing maps for
lyrics and tags. Lyricon [54] is a system for displaying
icons that match the word sequences of lyrics so that users
can intuitively understand the lyrics. Moreover, Funasawa
et al. [55] implemented a system that automatically generates slideshows for music by generating queries from lyrics
and searching for images. O’Hara et al. [56] demonstrated
how to learn the meanings of chord sequences from lyrics
annotated with chords. Ibraham et al. [57] proposed amethod for estimating the intelligibility of lyrics in a given
song to help users learn a second language.
In this paper, we investigate more fundamental questions about lyrics: why and how people view them. For
researchers, the insights of our analysis can be used in implementing lyrics-based systems. For example, when researchers propose systems to support understanding lyrics,
they can claim these systems’ importance based on thehigh demand for deeply understanding lyrics, as we willreport in Section 3.2.1. In Sections 3 and 4, we also suggest application examples such as recommending songs according to lyrics and supporting lyrics viewing. We believethat our suggestions are also beneficial for music streaming platforms to make their smartphone applications moreProceedings of the 22nd ISMIR Conference, Online, November 7-12, 2021

attractive to users.
3. WHY PEOPLE VIEW LYRICS
In this section, we report why people view lyrics by conducting an online survey involving 206 participants.
3.1 Participants
We recruited participants for our survey via an online research company. We limited the participants to those who
listen to music on average at least one day per week on asmartphone application via any online music service andhave viewed lyrics on the application while listening tomusic at least 10 times in their lifetime. In addition, toalign with the user nationality in the lyrics viewing log
data, as described in Section 4.1.1, all participants wereJapanese. The participants answered our questionnairethrough a web browser. We paid about 21.1 USD (2,275
JPY) to each participant. Although 297 participants joined
the survey, to make the analysis results more reliable, weremoved the answers from 91 participants: 14 of themgave the same answers to all questions (e.g., choosing “1”for all questions), and 77 of them finished answering thequestions in a very short time 1 . The remaining 206 participants were well balanced in gender and age range: 95
males (10s: 2; 20s: 20; 30s: 22; 40s: 26; 50s: 25) and 111
females (10s: 4; 20s: 21; 30s: 27; 40s: 28; 50s: 31).
3.2 Results and Discussion
3.2.1 Reasons
To understand why people view lyrics on a smartphone
while listening to music, we listed the following eight candidate reasons 2 . (1) Confirmation: The user wants to confirm what the artist sings. (2) Understanding: The user
wants to more deeply understand the lyrics. (3) Singing:
The user wants to sing to herself (not in public). (4) Structure: The user wants to figure out the structure of the lyrics,
such as verse and chorus. (5) Karaoke: The user wants topractice for singing in public, as in karaoke. (6) Boredom:
The user wants to get rid of her boredom by viewing lyrics.
(7) Language: The user wants to learn a language with the
lyrics. (8) Writing: The user wants to study for writing
lyrics. The participants were asked to rate the frequencyof viewing lyrics for each reason on a scale of 1 to 5 (1:never; 5: very often). The reasons were displayed in a random order to each participant 3 .
For each reason, Figure 1 shows the frequency distribution and the number of users whose score was 4 or 5
(i.e., the number who often viewed lyrics for that reason).
We can see that the ratings for Confirmation and Understanding are high: in fact, the paired Wilcoxon signed1 We applied a tight rule for this filtering to reduce the risk of noisy
answers as much as possible. Nonetheless, the remaining 206 participants
are sufficient to discuss the general tendency of people’s behavior [15].
2 The eight candidate reasons were decided through discussions among
the authors.
3 We also provided an open-ended answer format for asking the participants to freely describe other reasons. However, only thee participants
used it; their answers are omitted here due to space limitations. We therefore think that the eight candidate reasons covered the possible reasons
well. Using a fully open-ended answer format to compare results could
be an interesting future work.

Confirmation (181)
Understanding (159)
Singing (128)
Structure (103)
Karaoke (105)
Boredom (69)Language (57)
Writing (28)
0
25
Never
Rarely50

75 100Sometimes

125 150
Often

175 200
Very often

Figure 1. Frequency of reasons why people view lyrics ona smartphone while listening to music (1: never; 5: very
often). The number in parentheses represents the numberof participants rating 4 or 5.
rank tests with Bonferroni correction reveal that the medians of Confirmation and Understanding are statisticallyhigher than the remaining six reasons at p < 0.01. It would
be beneficial to provide additional functions according tousers’ reasons for viewing lyrics. For example, for a user
whose reason is Understanding, displaying diverse interpretations of lyrics could help her understand them moredeeply. An interesting future work would be to automatically mine web pages that describe interpretations of given
song’s lyrics and display the collected interpretations alongwith the lyrics.
Among the remaining six reasons, more than half of theparticipants gave a rating of 4 or 5 for Singing, Structure,
and Karaoke. For users who view lyrics to sing (Singing
and Karaoke), some smartphone applications already provide a function that automatically scrolls lyrics by synchronizing them with the playback time. To improve theirsinging performance, we suggest more advanced functions
that display tips for singing each part of the lyrics and automatically judge their singing skill [58]. As for the Structure reason, one possible application is coloring blocks oflyrics according to the estimated structure [59, 60]; thiswould enable the user to quickly figure out the structure.
Although Boredom, Language, and Writing are relatively minor reasons, it is still worth considering functions
for them, not only because it is important to build systems to support niche uses but also because more users
may begin to view lyrics to use such functions. This may
give users chances to listen to music more frequently andeventually provide benefits for music streaming platforms.
For a user who views lyrics because of Boredom, displaying information related to the played song, such as similarsongs by different artists, may help her discover unfamiliar songs. When a user views lyrics for learning (Language
and Writing), she may want to use functions that improvethe efficiency of the learning process. Examples for Language include enabling the user to see the meaning of aword in lyrics just by tapping the word and recommending
a song by the same artist with more intelligible lyrics [57].
Examples for Writing include explaining poetic and rhetorical techniques used in writing lyrics and recommending
songs with the same techniques.
Finally, Table 1 lists the number of participants who
gave a rating of 4 or 5 to at least k reasons. Because 89.8%
of the participants gave high scores for more than one reason and over 60% of them often view lyrics for more than707

Proceedings of the 22nd ISMIR Conference, Online, November 7-12, 2021

Table 1. Number of participants who gave a rating of 4 or
5 to at least k reasons.
k#participants
Percentage

1
196
95.1

2
185
89.8

3
168
81.6

4
125
60.7

5
87
42.2

6
39
18.9

7
22
10.7

8
2
3.88

three reasons, we can say that the reason for viewing lyrics
is not exclusive; rather, it is common to have multiple reasons. An interesting future work would be to predict andrecommend lyrics-related functions (like those describedabove) to use next according to those already used.
3.2.2 Behavior
We now investigate users’ detailed behavior in viewing
lyrics for different reasons in terms of three aspects. Note
that, for each reason, we asked follow-up questions to participants who gave a rating of 4 or 5 so that we could interpret the characteristics of the reasons more accurately(see Figure 1 for the number of such participants for eachreason).
Aspect 1: timing. First, to each participant, we showeda reason for which she gave a rating of 4 or 5 and asked,“When you view lyrics for this reason, do you decide to doso (a) before playing a song or (b) after playing a song?”
The possible answers were (1) mostly (a), (2) moderately(a), (3) about the same, (4) moderately (b), and (5) mostly(b). Answers (1) and (2) ((4) and (5)) were then merged
into a “Before” (“After”) group. The “Timing” column ofTable 2 lists the frequency of responses in each group foreach reason. For Structure that has a statistically high frequency in the “After” group, it would be effective to enable
users to more quickly execute the corresponding function
proposed in Section 3.2.1 while listening to a song, as compared to the functions for other reasons. On the other hand,
Karaoke has a statistically high frequency in the “Before”
group. Therefore, if a smartphone application provided an
option to play a song in the setting of the Karaoke function
explained in Section 3.2.1, users would be expected to use
the application more frequently to practice for karaoke. In
Table 2, although both Singing and Karaoke are related tosinging a song, it is interesting that Singing has almost thesame frequencies in the “Before” and “After” groups.
Aspect 2: repetition. Our next question was “Whenyou view lyrics for this reason, how many times do you
continuously view them while repeatedly playing a song?”
The answers consisted of (1) mostly once (i.e., no repetition), (2) mostly two or three times, and (3) mostly morethan three times. Because no significant difference was observed between answers (2) and (3), we report the resultswith answers (2) and (3) merged as a “Many” group, whileanswer (1) is labeled as “Once.” The “Repetition” columnof Table 2 lists the results. It can be observed that, forall reasons, the “Many” group has higher frequency. It isthus common behavior to continuously view lyrics while
repeating a song. Therefore, it would be helpful for users
to change the displayed information according to the number of repetitions (e.g., when a user listens to a song for
the Understanding reason, different interpretations of the
lyrics can be shown every time she plays it.).
708

Table 2. Behavior frequency in terms of three aspects: timing, repetition, and percentage.
ReasonConfirmation
Understanding
Singing
Structure
Karaoke
Boredom
Language
Writing

TimingBefore After
49
95∗∗
60
70
50
51
29
46∗
55∗
33
12
39∗∗
27
18
11
10

Repetition
Once Many
70
111∗∗
38
121∗∗
36
92∗∗
33
70∗∗
14
91∗∗
29
40
12
45∗∗
3
25∗∗

Percentage
Partial Most
53
84∗∗
20
116∗∗
16
85∗∗
18
57∗∗
13
78∗∗
19
31
2
39∗∗
2
17∗∗

∗ (∗∗) denotes the statistical difference at p < 0.05 (p < 0.01) based on a two-tailed
z-test.

Aspect 3: percentage. In our last question, we asked,“When you view the lyrics for this reason, what percentage of the lyrics do you view?” The answers were (1)≤20%, (2) 21%–40%, (3) 41%–60%, (4) 61%–80%, and(5) ≥81%. We merged answers (1) and (2) ((4) and (5))
into a “Partial” (“Most”) group. The “Percentage” columnof Table 2 lists the frequency of responses in each group.
Because “Most” was more popular for all reasons, people
tend to view most of the lyrics in any situation. However,a significant difference between “Partial” and “Most” wasnot observed for Boredom only. This result indicates thatwhen a user stops viewing lyrics within a short time, she islikely bored. Therefore, music streaming platforms have a
big opportunity to give such users valuable information, as
illustrated in Section 3.2.1.
4. HOW PEOPLE VIEW LYRICS
In this section, we report how people view lyrics based onover 23 million lyrics request logs sent from smartphone
applications for playing music.
4.1 Dataset
4.1.1 Lyrics Viewing Log
For lyrics viewing, we used log data given by a lyrics distribution company (SyncPower Corporation) in Japan. Although this company provides lyrics text to various musiclistening smartphone applications, we focused on the iOS
application of a Japanese online music service and usedlogs collected from it. In the application, a user can viewthe lyrics of a played song while listening to the song. The
application gets the lyrics by using an API provided by the
lyrics distribution company. The company stores request
logs that include the timestamp, user ID, and song ID. Notethat the application does not automatically get lyrics whena song is played; rather, it only gets them when a user explicitly requests them. Therefore, the logs are suitable foranalyzing how users view lyrics.
We first collected logs whose timestamp was between
1/1/2018 and 12/31/2018. We then removed logs whose
duration was less than 30 seconds, because such short-term
logs may have resulted from users’ wrong operations. Finally, our dataset (hereafter, LyLog) consisted of 611,895
users, 214,434 unique songs, and 23,034,417 logs.

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Number of songs

104
103
102

7

Log percentage

LyLog
Lastfm

105

5
4
3
2
1

101
100 0
10

6

LyLog
Lastfm

0

101

102
103
Number of logs

Figure 2. Distribution of the number of logs per song.
There are y songs that have x logs.
4.1.2 Music Listening Log
To investigate the difference between lyrics viewing behavior and music listening behavior, we used the Last.fm
dataset released by Schedl [23]. This dataset consists ofusers’ play logs, each of which includes the timestamp,user ID, song ID, and artist ID. To align the users’ nationality with the LyLog dataset, we first extracted logs
of Japanese users (the dataset also includes each user’s
nationality). We then collected logs whose timestamp
was between 1/1/2013 and 12/31/2013 and removed logs
whose duration was less than 30 seconds. This gave us
a music listening dataset (hereafter, Lastfm) consisting of
660 users 4 , 718,466 unique songs, and 2,932,430 logs 5 .
We do acknowledge some limitations of using Lastfm
for comparison. For example, the years in Lastfm are different from those in LyLog, and Lastfm includes play histories from not only smartphones but also PCs. Therefore,
it should be noted that the purpose of the comparison inthis paper is not to provide generalizable insights about
the differences between lyrics viewing and music listening. Nonetheless, we think it is still worth comparing thedifferences as a first step toward understanding the characteristics of lyrics viewing behavior. We leave it as a future
work to compare lyrics viewing logs and music listening
logs from the same platform 6 .
4.2 Basic Statistics
We first investigated several basic characteristics of lyrics
viewing. Figure 2 shows the distribution of the number ofconsumption logs per song 7 . Although the curves of both
LyLog and Lastfm show the heavy tail of their consumption
patterns, lyrics viewing behavior is more biased to popularsongs: in Lastfm, 80% of the whole logs are dominated by
the top 34.8% of the songs in terms of the number of logs,
while in LyLog, those are dominated by only the top 6.64%
of the songs.
4 There is no correspondence between the users in LyLog and those in
Lastfm.
5 A similar Last.fm dataset was released more recently [24], but theincluded logs are older than those in Schedl’s dataset [23]. Therefore, we
decided to use the latter dataset.
6 We cannot do so in this paper because the lyrics distribution company mentioned in Section 4.1.1 cannot store music play logs that do notinclude lyrics requests.
7 Throughout our investigation, the word “consumption” refers toviewing lyrics in LyLog or listening to music in Lastfm.

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23

Hour

104

Figure 3. Distribution of logs over the hours of the day.
In Figure 3, we show the distribution of logs over thehours of the day 8 . According to a survey on time use by
the Statistics Bureau of Japan [61], the average Japaneseperson gets up at 6:32 am, commutes to school or work
between 7:30 am and 8:30 am, commutes from school orwork between 6:00 pm and 7:00 pm, and goes to sleep
at 11:15 pm. Referring to this time schedule, we can see
some common characteristics in both datasets: the numberof logs increases during the morning commute and after
returning home in the evening; then, the number gradually decreases as people go to sleep. Between 5:00 pm
and 11:59 pm, however, LyLog has a higher percentage
than Lastfm does. Viewing lyrics on a smartphone requires
users to interact with the application more actively, as intapping the screen to request and look at lyrics; in contrast, users can listen to songs even with a smartphone ina pocket. Therefore, we can guess that users often view
lyrics in a relaxed state after coming back home. When asmartphone application recommends some of the functions
described in Section 3.2.1 to a user, night would be a moresuitable time, because the user would engage more actively
in viewing lyrics than during the daytime: it would be aninteresting future work to verify the usefulness of changing
the recommendation frequency of each function according
to time. Regarding the distribution of logs over the daysof week, although we do not show a chart due to the space
limitation, people view lyrics and listening to music 6.64%
and 6.53% more often on weekends than on weekdays, respectively; and no significant difference was observed between the datasets.
4.3 Repeat Consumption
We next investigated repeat consumption behavior inwhich a user consumes the same song repeatedly over time.
We first computed the fraction of repeat consumption foreach user. For example, if a user’s fraction is 0.4, then
40% of viewed lyrics have been already viewed by her.
Figure 4 shows this fraction’s distribution. It can be observed that the fraction for LyLog tends to be lower thanthat for Lastfm; in fact, the average fractions for LyLog andLastfm are 0.378 and 0.604, respectively. However, we cansay that the fraction of repeat consumption for lyrics viewing is still high compared to that of other domains such as
watching videos (fraction: 0.26) and clicking on English
Wikipedia pages (fraction: 0.15) [29]. The above analysis
8 Note that Japan does not observe daylight saving time.

709

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User percentage

2.5

LyLog
Lastfm

2.0
1.5
1.0
0.5
0.0

0.0

0.2

0.4
0.6
0.8
Fraction of repeat consumption

1.0

Figure 4. Fraction of repeat consumption for each user.

Ratio of user-song pairs

100

Figure 6. Normalized mean index gaps.

LyLog
Lastfm

10 2
10 4
10 6
100

101

102
Number of repetitions

103

Figure 5. Distribution of the ratio of user-song pairs thatare repeatedly consumed x or more times.
did not consider how many times each song was repeatedly
consumed. Thus, we also computed the ratio of user-songpairs, in which each song was repeatedly consumed x ormore times by each user, to all user-song pairs, as shownin Figure 5. We can see that the numbers of repetitions forboth LyLog and Lastfm have a heavy tail. However, because the LyLog curve is located below and to the left of
the Lastfm curve, people do not repeatedly view the same
lyrics as many times as they listen to the same song.
Benson et al. [29] reported that, in repeat consumption,
each item has its own lifetime for a user, as described in
Section 2.1. Following their processes, we investigated thelifetime characteristics of lyrics viewing as follows. Givena user, we first sorted all songs for which she requested
lyrics in ascending order of the timestamp. We then extracted songs whose first and last consumption events werein the middle 60% of the list, so that we could consider
songs that certainly began and ended their lifetimes during the period of data collection. Suppose that a user’s
extracted consumption list consists of N songs and is represented by L = {i1 , . . . , iN }. When a particular song s
is consumed k times at indices {is1 , . . . , isk } ∈ L, the index gap between the jth and j + 1th consumption eventsis defined by gj = isj+1 − isj . Figure 6 shows the transitionof the mean gap, with all gaps normalized by the first gap
g1 (the average values of g1 for LyLog and Lastfm were19.0 and 248, respectively). As in the report by Benson etal. [29], in lyrics viewing behavior, too, the gap tends togrow over time. This means that when a user repeatedly
views the lyrics of a song, she views it again within a shortspan at the beginning; the span gradually increases as shegets bored with it, and eventually she stops viewing the
lyrics. As can be seen in Figure 6, the gap increase rate for
LyLog was smaller than that for Lastfm.
710

Because the gap grows over time, there is a possibility that we can detect a user who begins to get bored withparticular lyrics by using the method proposed by Benson et al. [29]. When such a user is detected, suggesting functions (from those described in Section 3.2.1) that
she has not used for the lyrics is one possible way to holdher attention on the lyrics for a longer time. In contrast,
recommending novel lyrics related in terms of, say, thetopic [9–12] would be a good trigger for the user to listen
to new songs and expand her interest to other artists; thiswould also be beneficial for music streaming platforms.
5. CONCLUSION
In this paper, we investigated why and how people view
lyrics while listening to music on a smartphone. Regarding the “why” part, we conducted an online user survey
involving 206 participants; regarding the “how” part, weanalyzed over 23 million lyrics request logs. From theresults, we discussed reusable insights that are beneficial
for researchers and music streaming platforms, such as theextent of the demand for the eight major reasons to view
lyrics and the generality of repeatedly viewing the same
lyrics. We also suggested several functions according tousers’ reasons for viewing lyrics. We believe that realizing the functions would diversify and enrich users’ music
listening experiences. Some of the reported findings might
be obvious (e.g., people view lyrics more often at night).
However, in this kind of study that investigates researchquestions on an unexplored topic, it is valuable to report
not only unexpected results but also such obvious results
based on the data; obvious but verified results can then be
used as evidence for claiming the appropriateness of proposed methods or systems in later studies.
We acknowledge a limitation of this paper in that weinvestigated lyrics viewing behavior by only Japanese people in both the “why” and “how” parts. Nonetheless, we
believe that our study is a worthwhile contribution to MIR
community, because this is the first attempt to reveal lyrics
viewing behavior and verifies the fundamental characteristics of the behavior. At the same time, this limitation
indicates the possibilities of this research topic and guides
future work such as investigating the differences in lyrics
viewing behavior among countries. It would also be an important future work to investigate lyrics viewing behavior
on other devices (e.g., PCs and tablets) and at various locations (e.g., homes, restaurants, and public transportation).

Proceedings of the 22nd ISMIR Conference, Online, November 7-12, 2021

6. ACKNOWLEDGMENTS
The authors would like to extend their appreciation to
SyncPower Corporation for providing the lyrics request
logs. This work was supported in part by JSPS KAKENHI
Grant Number 20K19934 and JST CREST Grant Number
JPMJCR20D4, Japan.
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