Kosetsu TSUKUDA ta) , Masahiro HAMASAKI tb) , Nonmembers, and Masataka GOTO tc) , Fellow SUMMARY Why and how do people view lyrics?  Although various lyrics-based music systems have been proposed, this fundamental ques- tion 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 this paper, 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," we analyze over 23 million lyrics request logs sent from the smartphone application of a music streaming service. Our analysis results suggest several reusable in- sights, 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 structure such 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 ap- plication examples that could enable people to more actively view lyrics and listen to new songs, which would not only diversify and enrich peo- ple's music listening experiences but also be beneficial especially for music streaming platforms. key words:  lyrics, smartphone, user behavior analysis, log analysis
Introduction
1.
When people seek help in identifying a particular song that they have listened to, they often provide words in the song's lyrics as a clue for identification [2] , [3] .  In other situations when people listen to music, it has been reported that they choose songs according to not only the musical audio con- tent, such as the music genre, mood, melody, vocal timbre, and rhythm, but also the topics of lyrics [4] , [5] .  To meet these 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 [6] – [9] and systems for exploring songs according to the topics esti- mated from lyrics [10] – [13] .  As illustrated here, lyrics are an essential element of music for both listeners and MIR researchers. Despite the importance of lyrics, more fundamental in- vestigation of lyrics remains an under-addressed topic: why and how do people view lyrics? In this paper, we aim to an- swer these questions.  Investigating people's lyrics-viewing behavior and revealing reusable insights would be benefi- cial for researchers and music streaming platforms to imple- ment 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 [14] – [21] , and the ob- tained insights have contributed 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 [22] . Recently, 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 lis- tening 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 be- come a mainstream format for listening to music [16] .  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 more reusable for future work. Our main contributions can be summarized as follows: ● To our knowledge, this is the first study on the interac- tions between users and lyrics in terms of why and how users view lyrics when they listen to music. ● To investigate why users view lyrics, we conducted a large-scale questionnaire-based online survey involv- ing 206 participants. In the survey, more than 75% of the participants answered that they often view lyrics to confirm what an artist sings or more deeply 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 ben- eficial 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 users according to their reasons for viewing lyrics, such as a function that displays tips to sing each part of the song’s lyrics for users who want to sing. ● We investigated how users view lyrics by analyzing over 23 million lyrics request logs for over 600 thou- sand smartphone users for a year on a music stream- Copyright © 2023 The Institute of Electronics, Information and Communication Engineers ing service. The data shows that people tend to view more lyrics after coming back home at night and be- fore 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 with viewing the same lyrics. Considering these findings, we make several proposals for music streaming platforms to at- tract users ( e.g. , when a user gets bored with 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 inter- views. Typical questions about music listening ask why peo- ple listen to music [17] – [19] , [21] and how they use music websites, services, and applications [2] , [16] , [23] . Regard- ing the former question, the main reasons include emotional reasons such as relaxation [18] (even at work [19] ) and re- lief [17] .  People also listen to music to concentrate and to pass time [21] . Regarding the latter question, Lee and Wa- terman [16] revealed that people use music websites and ap- plications for various reasons such as discovering new mu- sic and learning about the artists. They also compared their results with those in 2004 [2] and showed increases in the popularity of music streaming and mobile music consump- tion.  A more recent work conducted a survey on the use of cloud music services and considered the future design of such services [23] . Moreover, Lee and Price [15] conducted interviews with music listeners and revealed 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 on- line music services [24] , [25] or (2) Twitter, where tweets related to music listening are gathered via specific tags such as "#nowplaying" and "#itunes" [26] – [28] .  Logs have been analyzed in terms of various aspects, including the long tail distribution of listening events per user, track, and artist [24] , [25] , [28] , the popularity of genres, moods, and tags [26] – [28] , and the temporal distribution (hour of day and day of week) [24] , [27] , etc.. One characteristic of mu- sic listening behavior is repeat consumption [29] .  Reports have indicated that, in a user's music play logs, about 70% of played songs have already been played before, and this percentage is much higher than for other domains such as viewing videos and visiting restaurants [29] , [30] . In repeat consumption, the number of times a song is played is heavy- tailed ( i.e. , a user repeatedly listens to a small proportion of songs again and again). Benson et al. [30] reported 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 with the song. Although listening to sung lyrics is one factor in lis- tening to music, our study differs from the above studies in that we particularly focus on lyrics viewing behavior. Fo- cusing on a particular element of music is beneficial to sug- gest new possibilities for future research as was indicated by Demetriou et al. [5] who focused on vocals.  Research on why people listen to music has tended to involve user studies, because they have the advantage of enabling re- searchers 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 pro- cessing.  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 stud- ies, including lyrics-to-audio alignment [31] – [37] , analy- sis of lyrics characteristics [38] – [43] , accurate lyrics re- trieval [44] – [46] , and genre and mood classification [47] – [52].  Below, we review more related studies that aim to support user activity by using lyrics. One major approach is enabling users to search for songs by words in lyrics, in which a query can be text [6] , [7] or user's sung lyrics [8] , [9] .  Systems have also been pro- posed for exploring songs according to topics estimated from lyrics [10] – [13] . Fujihara et al. [53] proposed the con- cept 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. Song- Words [54] displays a music collection on a two-dimensional canvas based on self-organizing maps for lyrics and tags. Lyricon [55] is a system for displaying icons that match the word sequences of lyrics so that users can intuitively un- derstand the lyrics.  Moreover, Funasawa et al. [56] imple- mented a system that automatically generates slideshows for music by generating queries from lyrics and searching for images.  O'Hara et al. [57] demonstrated how to learn the meanings of chord sequences from lyrics annotated with chords.  Ibraham et al. [58] proposed a method for estimat- ing the intelligibility of lyrics in a given song to help users learn a second language. In this paper, we investigate more fundamental ques- tions about lyrics:  why and how people view them.  For researchers, the insights of our analysis can be used in im- plementing lyrics-based systems.  For example, when re- searchers propose systems to support understanding lyrics, they can claim these systems' importance based on the high demand for deeply understanding lyrics, as we will report in Sect. 3.2.1. In Sects. 3 and 4, we also suggest application ex- amples such as recommending songs according to lyrics and supporting lyrics viewing. We believe that our suggestions are also beneficial for music streaming platforms to make their smartphone applications more attractive to users. 558 3.
Why People View Lyrics
In this section, we report why people view lyrics by con- ducting an online survey involving 206 participants.
Participants
3.1
We recruited participants for our survey via an online re- search company. We limited the participants to those who listen to music on average at least one day per week on a smartphone application via any online music service and have viewed lyrics on the application while listening to mu- sic at least 10 times in their lifetime.  In addition, to align with the user nationality in the lyrics viewing log data, as described in Sect.4.1.1, all participants were Japanese. The participants answered our questionnaire through a web browser.  We paid about 15.73 USD (2,275 JPY) to each participant. Although 297 participants joined the survey, to make the analysis results more reliable, we removed the an- swers from 91 participants: 14 of them gave the same an- swers to all questions ( e.g. , choosing "1" for all questions), and 77 of them finished answering the questions in a very short time t . The remaining 206 participants were well bal- anced 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 can- didate reasons. (1) Confirmation : The user wants to confirm what the artist sings. (2) Understanding: The user wants to more deeply under- stand the lyrics. (3) Singing: The user wants to sing to herself (not in pub- lic). (4) Structure : The user wants to figure out the structure of the lyrics, such as verse and chorus. (5) Karaoke : The user wants to practice for singing in pub- lic, 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 frequency of 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 i . For each reason, Fig. 1 shows the frequency distribu- tion 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 Understand- ing are high: in fact, the paired Wilcoxon signed-rank tests with Bonferroni correction reveal that the medians of Con- firmation and Understanding are statistically higher than the remaining six reasons at p < 0.01. It would be beneficial to provide additional functions according to users' reasons for viewing lyrics. For example, for a user whose reason is Understanding , displaying diverse interpretations of lyrics could help her understand them more deeply. An interesting future work would be to automatically mine web pages that describe interpretations of given song's lyrics and display the collected interpretations along with the lyrics. Among the remaining six reasons, more than half of the participants 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 their singing per- formance, we suggest more advanced functions that display tips for singing each part of the lyrics and automatically judge their singing skill [59] .  As for the Structure reason, one possible application is coloring blocks of lyrics accord- ing to the estimated structure [60] , [61] ; this would enable the user to quickly figure out the structure. Although Boredom , Language , and Writing are rela- tively 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 and eventually provide benefits for music streaming platforms.  For a user who views lyrics because of Boredom , displaying informa- tion related to the played song, such as similar songs by dif-
ferent artists, may help her discover unfamiliar songs. When a user views lyrics for learning ( Language and Writing ), she may want to use functions that improve the efficiency of the learning process. Examples for Language include enabling the user to see the meaning of a word in lyrics just by tapping the word and recommending a song by the same artist with more intelligible lyrics [58] .  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 than 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 and recommend lyrics-related functions (like those described above) 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 par- ticipants who gave a rating of 4 or 5 so that we could inter- pret the characteristics of the reasons more accurately (see Fig. 1 for the number of such participants for each reason). Aspect 1:  timing.  First, to each participant, we showed a reason for which she gave a rating of 4 or 5 and asked, "When you view lyrics for this reason, do you de- cide to do so (a) before playing a song or (b) after play- ing 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 "Tim- ing" column of Table 2 lists the frequency of responses in each group for each reason.  For Structure that has a sta- tistically high frequency in the "After" group, it would be effective to enable users to more quickly execute the corre- sponding function proposed in Sect. 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 applica- tion provided an option to play a song in the setting of the Karaoke function explained in Sect.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 to singing a song, it is interesting that Singing has almost the same frequencies in the "Before" and "After" groups. Aspect 2:  repetition.  Our next question was "When you view lyrics for this reason, how many times do you con- tinuously 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 more than three times.  Because no significant difference was observed be- tween answers (2) and (3), we report the results with an- swers (2) and (3) merged as a "Many" group, while answer (1) is labeled as "Once."  The "Repetition" column of Ta- ble 2 lists the results. It can be observed that, for all reasons, the "Many" group has higher frequency. It is thus common behavior to continuously view lyrics while repeating a song. Therefore, it would be helpful for users to change the dis- played 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). 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 "Par- tial" ("Most") group.  The "Percentage" column of 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 signif- icant difference between "Partial" and "Most" was not ob- served for Boredom only.  This result indicates that when a user stops viewing lyrics within a short time, she is likely bored. Therefore, music streaming platforms have a big op- portunity to give such users valuable information, as illus- trated in Sect. 3.2.1.
How People View Lyrics
4.
In this section, we report how people view lyrics based on over 23 million lyrics request logs sent from smartphone ap- plications for playing music.
4.1
Dataset
Lyrics Viewing Log
4.1.1
For lyrics viewing, we used log data given by a lyrics dis- tribution company (SyncPower Corporation) in Japan.  Al- though this company provides lyrics text to various music- listening smartphone applications, we focused on the iOS application of a Japanese online music service and used logs collected from it.  In the application, a user can view the lyrics of a played song while listening to the song. The ap- plication 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. Note that the application does not automatically get lyrics when a song is played; rather, it only gets them when a user ex- plicitly requests them.  Therefore, the logs are suitable for analyzing how users view lyrics. We first collected logs whose timestamp was between 1/1/2018 and 12/31/2018. We then removed logs whose du- ration was less than 30 seconds, because such short-term logs may have resulted from users' wrong operations.  Fi- nally, our dataset (hereafter, LyLog ) consisted of 611,895 users, 214,434 unique songs, and 23,034,417 logs.
4.1.2  Music Listening Log
To investigate the difference between lyrics viewing be- havior and music listening behavior, we used the Last.fm dataset released by Schedl [24] .  This dataset consists of users' 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 † , 718,466 unique songs, and 2,932,430 logs ↑↑ . We do acknowledge some limitations of using Lastfm for comparison.  For example, the years in Lastfm are dif- ferent from those in LyLog , and Lastfin includes play his- tories from not only smartphones but also PCs.  There- fore, it should be noted that the purpose of the comparison in this paper is not to provide generalizable insights about the differences between lyrics viewing and music listening. Nonetheless, we think it is still worth comparing the differ- ences 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.
4.2
Basic Statistics
We first investigated several basic characteristics of lyrics viewing.  Figure 2 shows the distribution of the number of consumption logs per song ††† .  Although the curves of both LyLog and Lastfm show the heavy tail of their consumption patterns, lyrics viewing behavior is more biased to popular songs: 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. In Fig. 3, we show the distribution of logs over the hours of the day.  According to a survey on time use by the Statistics Bureau of Japan [62] , the average Japanese person gets up at 6:32 am, commutes to school or work between 7:30 am and 8:30 am, commutes from school or work be- tween 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 number of 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 in tapping the screen to request and look at lyrics; in contrast, users can listen to songs even with a smartphone in a pocket. Therefore, we can guess that users often view lyrics in a relaxed state after coming back home. When a smartphone application recommends some of the functions described in Sect. 3.2.1 to a user, night would be a more suitable time, because the user would engage more ac- tively in viewing lyrics than during the daytime: it would be an interesting future work to verify the usefulness of chang- ing the recommendation frequency of each function accord- ing to time. Regarding the distribution of logs over the days of week, people view lyrics and listen to music 6.64% and 6.53% more often on weekends than on weekdays, respec- tively; and no significant difference was observed between the datasets.
4.3
Repeat Consumption
We next investigated repeat consumption behavior in which a user consumes the same song repeatedly over time.  We first computed the fraction of repeat consumption for each 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 than that for Lastfm ; in fact, the average fractions for LyLog and Lastfm are 0.378 and 0.604, respectively. However, we can say that the frac- tion 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) [30] . The above analysis did not consider how many times each song was repeatedly consumed. Thus, we also computed the ratio of user-song pairs, in which each song was repeatedly consumed x or more times by each user, to all user-song pairs, as shown in Fig. 5.  We can see that the numbers of repetitions for both 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 re- peatedly view the same lyrics as many times as they listen to the same song. Benson et al. [30] reported that, in repeat consumption, each item has its own lifetime for a user, as described in Sect.2.1.  Following their processes, we investigated the lifetime characteristics of lyrics viewing as follows. Given a user, we first sorted all songs for which she requested lyrics in ascending order of the timestamp.  We then ex- tracted songs whose first and last consumption events were in 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 = { i 1 , . . , i N } .  When a particular song s is consumed k times at indices { i } . . , i } ∈ L , the index gap between the jth and j+ 1th consumption events is defined by g j = i j+ 1 − i j+ 1 − i j+ Figure 6 shows the transition of the mean gap, with all gaps normalized by the first gap g 1 (the average values of g 1 for LyLog and Lastfm were 19.0 and 248, respectively).  As in the report by Benson et al. [30] , in lyrics viewing behavior, too, the gap tends to grow over time. This means that when a user repeatedly views the lyrics of a song, she views it again within a short span at the beginning; the span gradually in- creases as she gets bored with it, and eventually she stops viewing the lyrics. As can be seen in Fig. 6, the gap increase rate for LyLog was smaller than that for Lastfm. Because the gap grows over time, there is a possibil- ity that we can detect a user who begins to get bored with particular lyrics by using the method proposed by Benson et al. [30] . When such a user is detected, suggesting functions (from those described in Sect. 3.2.1) that she has not used for the lyrics is one possible way to hold her attention on the lyrics for a longer time.  In contrast, recommending novel lyrics related in terms of, say, the topic [10] – [13] would be a good trigger for the user to listen to new songs and expand her interest to other artists; this would 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.  Regard- 562 ing the "why" part, we conducted an online user survey involving 206 participants; regarding the "how" part, we analyzed over 23 million lyrics request logs.  From the re- sults, we discussed reusable insights that are beneficial for researchers and music streaming platforms, such as the ex- tent 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 to users' reasons for viewing lyrics.  We believe that realizing the functions would diversify and enrich users' music listening experi- ences. 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 research questions on an un- explored topic, it is valuable to report not only unexpected results but also such obvious results based on the data; ob- vious but verified results can then be used as evidence for claiming the appropriateness of proposed methods or sys- tems in later studies. We acknowledge a limitation of this paper in that we investigated lyrics viewing behavior by only Japanese peo- ple in both the "why" and "how" parts.  Nonetheless, we believe that our study is a worthwhile contribution 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 investigat- ing the differences in lyrics viewing behavior among coun- tries. It would also be an important future work to investi- gate lyrics viewing behavior on other devices ( e.g. , PCs and tablets) and at various locations ( e.g. , homes, restaurants, and public transportation).
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 JST CREST Grant Number JPMJCR20D4 and JSPS KAKENHI Grant Number JP20K19934.