Does Emo Rap Makes You Less Emo? the Influence of Emo-Rap on Young Adults’ Emotions

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Does emo rap makes you less emo? The influence of emo-rap on young adults’ emotions.

Author: Christiaan van der Weijden

Student number: 10625224

Master’s thesis

Graduate School of Communication

Master’s programme Communication Science

Supervisor: Susanne Baumgartner

Date: 26-6-2019

Abstract

Emo-rap is currently gaining more and more popularity, especially among emerging adults. Taking into account the lack of research about this genre, the relationship between emo-rap and emerging adults’ emotions will be studied. Contrary to popular belief, sad music can also induce happy emotions among listeners. This study will research if this also holds for emo-rap. An online experiment (N=170) was conducted, with a 2 emotional valence (happy vs. sad) x 2 music genre (rap vs. pop) factorial design. After inducing sad mood, emerging adults had to listen to one of four songs. Results show that music in general lead to more happiness among participants, when in a sad mood. In contrast to the predictions, happy music lead to more happiness than sad music. Moreover, a high preference for the heard genre made participants happier than a low genre preference, when in a sad mood.

Introduction

Nowadays, hip-hop and rap music are gaining more and more popularity, especially among adolescents and young adults (Statista, n.d.) in the Netherlands (NPO, 2018) and the

United States (Nielsen, 2018). One could argue that these genres have become pop-music, regarding the presence of numerous rap and hip-hop songs in major Spotify charts. One of the popular sub-genres is called emo-rap, with Lil Uzi Vert and the late XXXTENTACION as one of the most popular right now. They are known for their face tattoos, colored hair and lyrics about drugs and depression. To illustrate their popularity, XXXTENTACION’s most popular song ‘SAD’ has been streamed almost a billion times, whereas Lil Uzi Vert’s ‘XO

TOUR Llif3’ already surpassed one billion streams. This genre is “characterized by soporific beats, dreamy atmospheres and lyrics displaying an emotional vulnerability that is the antithesis of the hard-bragging, hard-living gangsta rap that has come to define mainstream hip-hop” (Teffer, 2018). Since a lot of people are exposed to this music, and thereby come in contact with depictions of feelings of depression and accompanying behavior (e.g. drug use), its possible influence needs to be investigated.

In particular, music can have several influences on listeners, one of them is emotion induction (Thompson, & Robitaille, 1992). In this case, one would think that emo-rap will probably induce negative emotions on listeners. But is this really the case? First of all, a definition of the broad concept ‘emotion’ is needed. Emotion is “a quite brief but intense affective reaction that usually involves a number of subcomponents—subjective feeling, physiological arousal, expression, action tendency, and regulation—that are more or less

“synchronized.” Emotions focus on specific “objects” and last minutes to a few hours (e.g., happiness, sadness)” (Juslin, & Sloboda, 2013, p. 585).

For rap is considered as ‘problem’ music (North, & Hargreaves, 2006), prior research regarding other negative deemed music genres might be relevant. Contrary to popular belief,

3 hard rock, for example, is known to regulate sad emotions (Schave, & Schave, 1989).

Moreover, previous research shows that music study results in general could benefit physical health (Juslin, & Sloboda, 2013).

However, there aren’t any studies that research the influence of emo-rap on emotions.

And taking into account the age-groups among which emo-rap is the most popular and the fact that music can portray society and therefore can help young adults meet their social- emotional needs (Miranda, & Claes, 2004), the focus of the current research will be on emo- rap and young adults. And to see if there will be any differences between different music genres on emotions, the effects of emo-rap on emotions will be compared to those of rap as a broader genre as well as pop-music.

In short, the goal of this research is to provide a basis for future research regarding this relatively new genre, while presenting new insights about the effects of emo-rap on young adults’ emotions. Hence, the research question for the current study will be as follows: What is the influence of emo-rap on young adults’ emotions when already being sad?

Theoretical background

Enjoyment of sad media

Before researching the relationship between emo-rap and emotions, we need to understand why people might enjoy this kind of sad media content. Oliver’s (1993) research about the paradox of enjoying sad films discusses the difference between direct experiences of emotion and the evaluation of these experienced emotions. The latter are called metaemotions. Oliver (1993) argues that people can experience positive metaemotions

(positively appraising negative feelings) through sad films and this could meet certain needs.

People could perceive this as gratifying. This relates to Oliver and Raney’s (2011) reasoning that consumers of entertainment media not only choose to consume certain kinds of content

4 because of hedonic reasons, but also because of eudaimonic reasons. Whereas hedonic reasons are about amusement and pleasure, eudaimonic reasons are deeper and more meaningful. For instance, eudaimonic reasons could be searching for the meaning of life, truths, purposes or certain feelings.

In addition, Bartsch (2012) argues that emerging adults still have their whole life in front of them. Hence, they feel like they have endless possibilities in all aspects of life. This can result in several jobs, relationships and, concerning media, it can result in the consumption of more frightful and thrill-seeking content. They are more attracted to fear and sadness than older adults, since the latter feel like they have less time left. They prefer to spend their ‘short’ time left being alive in a positive way; they avoid negative affect. That’s why older adults are more inclined to consume entertainment content for hedonic reasons.

This also explains why young adults will listen to sad music during a heartbreak or watch a scary movie (Bartsch, 2012; Oliver, 1993). Through these negative types of media, young adults can learn valuable life lessons. Older adults, on the other hand, have already learned these lessons and are not inclined to experience them again. These findings are supported by

Mares, Oliver, and Cantor (2008), which argue that young adults are willing to undergo unpleasant, but arousing, experiences, since it will benefit them in the long run. Especially through entertainment media, for it is a great way to pass through these (emotional) experiences vicariously (Bartsch, 2012). So one could say that older adults prefer emotional stability, whereas young adults tend to choose for intense emotions. This could explain why young people might choose emo-rap.

Besides the general media theories, the theory of music, mood and movement, poses that “music produces the psychological response of altered mood leading to improved health outcomes” (Murrock & Higgins, 2009, p. 2252). The brain processes rhythm, melody, pitch and harmony. The system in which this processing occurs is also in charge of one’s emotions,

5 sensations and feelings. So music can change one’s mood through affective responses to the processed music (Murrock & Higgins, 2009). Important to say is that this theory doesn’t include lyrics, it is specifically about the music itself. However, it is possible that the lyrics might enhance the feeling the music already produces. The theory provides a basis regarding how music can regulate one’s emotions.

In short, the discussed theories give an overview of why young adults would listen to emo-rap. Moreover, Murrock and Higgins (2009) provide reason to believe that emo-rap might alter negative emotions. This could be done either through hedonic reasons (seeking pleasure and amusement) or eudaimonic reasons (searching for the meaning of their lives, for instance).

Music and emotion regulation among young adults

Next, a better understanding about the relationship between music and emotions is needed, specifically regarding young adults. Zimmer-Gembeck, and Skinner (2011) argue that adolescents need to be able to control their emotions and levels of stress to withstand the developmental difficulties they go through. One way of coping with these difficulties is through music, it helps adolescent regulate their emotions (Saarikallio, & Erkkilä, 2007). This is in line with previous research (Schave, & Schave, 1989), which poses that, in this case, heavy metal music can regulate listeners’ mood. This can happen through validating people’s emotions and feelings and showing they are not the only one experiencing what they are experiencing.

Moreover, music can be the tool for adolescents to release emotions vicariously or just serve as a distraction from unpleasant emotions (Schwartz, & Fouts, 2003). This corresponds with Bartsch’ (2012) research, who states that emerging adults use entertainment media

(among which music) to pass through emotional experiences vicariously. These mentioned

6 similarities between literature on adolescents and emerging adults provides reasons to believe that the findings on adolescents could be similar for emerging adults. As becomes clear, music can be used to regulate emotions. However, the emotional valence of the music might influence this regulation of emotions.

Difference between emotional valence

For the variety in emotional valence could be important regarding emotion regulation, this relationship will be discussed as well. As mentioned already, Oliver and Raney (2011) and Bartsch (2012) argue that young adults can consume media for eudaimonic reasons. In addition, Brattico and colleagues (2011) state that sad music can also be considered satisfying.

Also, Ter Bogt, Vieno, Doornwaard, Pastore, and Van den Eijnden, (2017) state that adolescents can gravitate towards certain music to feel melancholic. Moreover, Ladinig and

Schellenberg’s (2012) state that people are more attracted to sad music when experiencing sad emotions or being in a negative mood. Further research underlines this and shows that sad music is more appealing when people are in a sad mood, in comparison to being in a neutral or happy mood (Hunter, Schellenberg, & Griffith, 2011). This is because the sad music won’t change their affective states, since they are already in a sad mood. Hunter and colleagues

(2011) argue that people prefer music that is congruent with their emotions. Besides, other research (Garrido, & Schubert, 2015; Saarikallio & Erkkilä, 2007) states that one of the main reasons for people to listen to sad music is to improve their mood. In addition, listening to sad music can have a purging effect regarding negative and sad feelings (Van den Tol, &

Edwards, 2013).

Recent research (DeMarco, & Friedman, 2018) provides a possible explanation for the above mentioned choice of listening to sad music. The research confirms that participants choose music that is consistent with their emotional state, after sadness induction. So when in

7 a sad mood, they prefer to listen to sad music. DeMarco and Friedman (2018) further explain that people might turn to sad music for a better and deeper understanding of their emotional state. Secondly, people might feel that listening to happy music in a sad mood is inappropriate. Hence, they turn to sad music out to avoid these concerns (Friedman, Gordis, &

Förster, 2012).

Another thing that has to be taken into account, is that sad music is usually perceived as it is; sad. However, listening to this music can result in positive emotions (Kawakami,

Furukawa, Katahira, & Okanoya, 2013). This corresponds with Oliver’s (1993) study, which poses that people can positively appraise negative feelings.

Considering the fact that people will listen to music that is congruent with their mood

(e.g. Hunter et al., 2011) and the fact that listening to sad music can be used in a cathartic way

(Van den Tol, & Edwards, 2013), the researcher predicts the following:

H1a: When in a sad mood, a sad song will make young adults happier than a happy song.

Differences between music genres

Next to the valence of a song, the genre might also be important in explaining effects on emotion regulation. In this study, the focus lies on two genres, namely rap and pop music.

As rap music is sometimes considered ‘problematic music’, previous research on problem music might provide useful insights. North and Hargreaves (1999) conducted a research on hard rock and they found that youngsters listen to hard rock for comforting reasons. The music portrays certain emotions and feelings which are similar to theirs and their peers and serves as a safe place. Furthermore, Arnett (1991) found that people who preferred heavy metal didn’t feel depressed or sad because of the music, despite the depressing and concerning themes. In fact, people with a preference for heavy metal use the music in a cathartic way.

The participants said the music gets rid of negative feelings instead of increasing them. It’s

8 likely that Arnett’s (1991) results will also apply to emo-rap, for the negative themes are similar to those depicted in emo-rap.

Additionally, Bodner and Bensimon (2015) argue that problem music works better to regulate mood than non-problematic music, such as pop-music. Through listening to problem music, listeners will restore positive moods and get rid of negative emotions. However, this only applies to people who actually like the music, since they value and believe the effects this music can have more than people who are not into problem music (Bodner, & Bensimon,

2015).

In short, this shows that problem music (in this case hard rock and heavy metal) can have positive influences on listeners, despite the usually negative and intense messages and music. And since rap is considered problem music as well (North, & Hargreaves, 2006) due to the usually negative topics (e.g. drugs and depression), the researcher has reasons to believe that these influences will also apply to emo-rap.

In addition, the already discussed research by Friedman et al. (2012) argues when people are in sad mood, they turn to sad music to avoid happy music, since this might feel inappropriate. Taylor and Friedman (2015) replicated this finding and add that due the improperness of choosing a happy song while being sad, it might not work to alter mood.

Thus, considering the fact that emo-rap lyrics are predominantly sad, whereas pop music is usually happier and therefore might be deemed inappropriate, and taking into account the lack of research on this genre, the researcher predicts the following:

H1b: When in a sad mood, a rap song will make young adults happier than a pop song.

Furthermore, taking into account the already mentioned possibility of mood regulation through ‘problem’ music (e.g. Arnett, 1991; Bodner, & Bensimon, 2015) and sad music (Ter

Bogt et al. 2017), and the discussed fact that sad music can convey happy emotions (e.g.

Kawakami et al., 2013), the researcher predicts the following:

H2: There will be an interaction effect between music genre and emotional valence of the music. When in a sad mood, an emo-rap song will make young adults happier than a happy rap song, a happy pop song and a sad pop song.

Genre preference

Furthermore, one must consider the fact that not everyone is into rap or pop music and therefore the music might have a different effect on listeners. Musical preference can even be considered one of the important influences on the positive effects of music (Lee, Chung,

Chan, & Chan, 2005). The research by Murrock and Higgins (2009) emphasizes the importance of musical preference on changing mood. Moreover, Liljeström, Juslin, and

Västfjäll (2013) found that people who listen to their preferred music are happier and enjoy the music more. Although the music used in the study varies (pop music was the most common), the researcher expects that the results will also be the case for rap.

In addition, previously discussed literature (Arnett, 1991; Bodner, & Bensimon, 2015) also highlight the importance of music preference. Therefore, the researcher predicts the following:

H3a: When in a sad mood, a high genre preference will make young adults happier than participants with low genre preference.

Furthermore, the prediction is that genre preference will have a moderating effect on music genre:

H3b: When in a sad mood, a rap song will make young adults happier than a pop song. This effect will be stronger for participants with a high preference for rap than participants with a low preference for rap.

Methods

To answer the research question, an experiment has been conducted. As mentioned, the majority of rap listeners are adolescents and young adults (Statista, n.d.). For ethical reasons, only young adults (18-30 year olds) were part of the target group for the current research. This research consisted of four experimental conditions (emo-rap, happy rap, sad pop music and happy pop music) and one control condition. From now on, the emo-rap condition will be called sad rap because of convenience and for better understanding.

The author used a convenience sample, people within the target age-group were contacted within the author’s own network through Whatsapp. People were sent a link of the survey and were asked to fill it in. This way did not provide enough participants. The researcher used Amazon’s Mechanical Turk to complement the amount of participants.

Through Mechanical Turk, people within the target age-group could fill out the survey as well. Participants received a small fee around fifty cents.

Pre-test

Before conducting the experiment, a pre-test was conducted (N=20) to see if the two sad songs were actually perceived equally sad and if the two happy songs were perceived equally happy. The sad rap song is ‘Life is beautiful’ by late emo rapper Lil Peep. The happy rap song chosen is ‘Broccoli’ by D.R.A.M. and Lil Yachty. The sad pop song is Sam Smith’s

‘Stay with me’ and the happy pop song is called ‘Can’t stop the feeling’ by Justin Timberlake.

To improve comprehension, a lyric video with the song was shown. Moreover, Brattico and collegues (2011; 2016) pose that lyrics are important for recognizing sadness in music. The video clips of the songs were not showed, for the visuals might cause too much distraction.

The focus in this research is really on the music and lyrics.

The participants, all young adults, were asked to rate each of the four songs. They could answer through a seven-point Likert scale, which ranged from ‘very sad’ to ‘very happy’. To see if both sad and happy songs were perceived sad and happy, two paired sampled t-test were conducted. The means and standard deviations of the four songs can be seen in Table 1. Regarding the sad songs, participants perceived both songs equally sad, t(19)=0.34, p=.741. However, the happy songs were not perceived equally happy, t(19)=3.94, p=.001. Participants perceived ‘Can’t stop the feeling’ significantly happier than ‘Broccoli’.

Although the two happy songs differed from each other significantly, the researcher wanted to see if both happy songs are significantly happier than the two sad songs. Both sad songs were individually paired with the two happy songs, which resulted in four paired samples t-tests.

Table 1. Means and standard deviations of the happiness of the songs

Can’t stop the Stay with me Broccoli Life is beautiful feeling

M (SD) M (SD) M (SD) M (SD)

6.45 (.61) 2.40 (.75) 5.40 (1.19) 2.30 (1.38)

‘Can’t stop the feeling’ and ‘Stay with me’ differed significantly in perceived happiness, t(19)= -15.21, p<.001. Participants perceived ‘Can’t stop the feeling’ significantly happier than ‘Stay with me’. Furthermore, ‘Can’t stop the feeling’ and ‘Life is beautiful’ differed significantly in perceived happiness, t(19)=-11.86, p<.001. Participants perceived

‘Can’t stop the feeling’ significantly happier than ‘Life is beautiful’. Moreover, ‘Broccoli’ and ‘Life is beautiful’ differed significantly in perceived happiness, t(19)=-6.94, p<.001.

Participants perceived ‘Broccoli’ significantly happier than ‘Life is beautiful’. Also,

‘Broccoli’ and ‘Stay with me’ differed significantly in perceived happiness, t(19)=-10.03, p<.001. Participants perceived ‘Broccoli’ significantly happier than ‘Stay with me’.

Since both happy songs were significantly happier than both of the sad songs, the researcher decided to include the happy songs in the experiment, even though both happy songs significantly differed from each other in happiness.

Sample

A total of 203 people participated in this study. Some participants did not agree to further participate in the survey and some didn’t finish the survey completely. These participants were excluded from the analyses, resulting in a total of 170 participants. The majority was male (56.5%), whereas 42.3% was female and 1.2% identified as “other”. The average age of the participants was 23.64 (SD=2.30) and participants’ age ranged from eighteen to thirty years. The number of participants per condition can be seen in Table 2.

Table 2. Participants per condition (N)

Happy rap Happy pop Sad rap Sad pop Control

36 34 36 35 29

Design

This research had a 2 emotional valence (happy vs. sad) x 2 music genre (rap vs. pop) factorial design. Emotion was measured within-subjects, all the other variables were measured between-subjects. Participants in the control condition were exposed to a short article, which took the participants approximately the same time to read as the length of the songs. The participants were randomly assigned to one of the four experimental conditions or to the control condition.

At the start of the survey, participants were asked how they were feeling. For it was difficult to find participants that were already in a sad mood, participants were exposed to a short video first. The video is a short clip from Disney’s Lion King. The scene was about

Simba finding out his father, Mufasa, had died. After exposing participants to the video, they were asked how they were feeling, again. Then, participants were exposed to one of the four experimental conditions or the control condition. Participants were either exposed to the sad rap song, the happy rap song, the sad pop song, the happy pop song or the article. The four songs were the same as in as in the pre-test. After listening to the music, participants were asked how they were feeling one more time. The article which participants in the control group read was suspected to not trigger emotions heavily. The article was about a robot dog prohibited in an US state because of privacy legislation (see appendix).

Genre preference

Besides the already discussed independent variables music genre and emotional valence, the third independent variable was genre preference. This means the participant’s extent of preference towards the exposed music. This variable was measured through a seven- point scale, asking participants if they liked the heard music genre. Participants could choose from 1 being ‘not at all’ to 7 being ‘very much’. A median split was used to divide the two groups evenly. Everyone scoring higher than the median was considered having a high preference for the music genre, whereas the participants scoring below the median were considered having a low preference for the music genre (M=1.57, SD=.50).

Emotion

The main dependent variable was a state variable, namely emotion. As discussed, emotion was measured three times through the same question: How are you feeling right

14 now? Participants could answer on a seven point scale, ranging from ‘very sad (1)’ to ‘very happy (7)’. Participants had to answer this question in the beginning of the survey (M=4.82,

SD=1.27), then after seeing the sad video (M=2.98, SD=1.56) and lastly after listening to one of the songs or after reading the article (M=4.30, SD=1.54).

Results

Randomization check

First, an univariate ANOVA with a Bonferroni Post Hoc test was conducted to check if age was randomized over the five conditions. Condition (sad rap vs. happy rap vs. happy pop vs. sad pop song vs. control condition) was the independent variable and age the dependent variable. The test showed that participants’ mean age was not significantly different across the conditions, F(165)=0.954, p=.434. This means that randomization of participants across conditions was successful regarding age.

Furthermore, a Chi-square test was conducted to check if gender was randomized over the five conditions. Condition (sad rap vs. happy rap vs. happy pop vs. sad pop song vs. control) was the independent variable and gender (male, female and other) the dependent variable. Since only two participants answered ‘other’, and at least five are required, their answers were not used for the analysis. The test showed that participants’ gender was not significantly different across conditions, X ²(8)=4.43, p=.817. This means that randomization of participants across conditions was successful regarding gender (male and female).

Sadness induction

To see whether the Lion King scene really induced sad emotions, a two paired sampled t-test was conducted. Participants’ emotion measured before and after the video are being compared. The video made participants significantly sadder, t(169)=15.10, p<.001.

Participants felt significantly happier before the video (M=4.82, SD=1.27) than after the video (M=2.98, SD=1.56). This shows that the video has successfully induced sad emotions among participants.

Main analyses

To answer the hypotheses, a repeated measures ANOVA was conducted, with exposure (emotion before music exposure vs. emotion after music exposure) as within- subjects factor, and emotional valence (sad vs. happy), music genre (pop vs. rap) and genre preference (high vs. low) as between-subjects factors. The Wilks’ Lambda in the multivariate tests showed that there was a significant effect of exposure on participants’ emotions,

F(1,133)=87.39, p<.001. Participants were significantly happier after being exposed to music

(M=4.43, SD=1.60) than before being exposed to music (M=3.05, SD=1.53).

Also, the Wilks’ Lambda showed that there was a significant interaction effect between exposure and emotional valence on emotions, F(1,133)=18.23, p<.001. Participants were significantly happier after being exposed to a happy song (M=4.80, SD=1.53) than after being exposed to a sad song (M=4.06, SD=1.59). Because the effect is opposite from what was expected, H1a is rejected. When in a sad mood, a sad song did not make young adults happier than a happy song. In contrast, a happy song made participants feel happier than a sad song (Figure 1).

Furthermore, the Wilks’ Lambda showed that there was a non-significant interaction effect between exposure and music genre on emotions, F(1,133)=4.05, p=.526. Participants were not happier after being exposed to rap music than after being exposed to pop music

(Table 3). H1b is therefore rejected. When in a sad mood, a rap song did not make young adults happier than a pop song.

Figure 1. Exposure x emotional valence

Table 3. Exposure x music genre

Before exposure After exposure

M (SD) M (SD)

Rap song 3.25 (1.55) 4.57 (1.56)

Pop song 2.84 (1.49) 4.28 (1.63)

Moreover, the Wilks’ Lambda showed that there was a significant interaction effect between exposure and preference towards the heard music genre, F(1,133)=9.19, p=.003.

Participants were significantly happier when they had a high preference towards the heard music genre (M=4.73, SD=1.71) than with a low preference towards the heard music

(M=4.03, SD=1.37). H3a is therefore supported (Figure 2).

Figure 2. Exposure x genre preference

Besides, the Wilks’ Lambda showed that there was a non-significant three way interaction effect between exposure, emotional valence and music genre on emotions,

F(1,133)=0.04, p=.850. A sad rap song did not make listeners happier than a happy rap song, a sad pop song or a happy pop song (Table 4). This means that H2 is rejected as well.

Table 4. Exposure x emotional valence x music genre

Before exposure Rap song Pop song

M (SD) M (SD)

Sad song 3.31 (1.55) 3.23 (1.50)

Happy song 3.19 (1.58) 2.44 (1.40)

After exposure

Sad song 4.06 (1.64) 4.06 (1.57)

Happy song 5.08 (1.32) 4.50 (1.69)

In addition, the Wilks’ Lambda showed that there was a non-significant three-way interaction effect between exposure, music genre and genre preference on emotions,

F(1,133)=0.532, p=.467. A rap song did not make young adults happier than a pop song. And this effect was not stronger for participants with high genre preference than participants with low genre preference (Table 5). Therefore, H3b is rejected.

Table 5. Exposure x music genre x genre preference

Before exposure Low genre preference High genre preference

M (SD) M (SD)

Rap song 3.38 (1.19) 3.15 (1.81)

Pop song 2.72 (1.44) 2.93 (1.54)

After exposure

Rap song 4.13 (1.26) 4.93 (1.70)

Pop song 3.93 (1.49) 4.53 (1.71)

Also, the control condition was not included in the abovementioned repeated measures

ANOVA, for emotional valence and music genre didn’t apply for this condition. A univariate

ANOVA with a Bonferroni Post Hoc test was conducted, with condition as independent variable and emotion measured after exposure to one of the conditions as dependent variable.

A significant effect of condition on emotion has been found, F(4,165)=4.37, p=.002. The Post

Hoc test shows that there was a significant difference between the control and happy rap condition, p=.002. Participants in the happy rap condition (M=5.08, SD=1.32) were significantly happier than those in the control condition (M=3.69, SD=1.04).

Discussion

The goal of the current study was to research the influence of emo-rap on young adults’ emotions, when already being sad. The findings showed that when in a sad mood, exposure to any song will make listeners happier. Furthermore, the data showed that when in a sad mood, a happy song will make listeners happier than a sad song. This is in contrast with the discussed literature (Garrido, & Schubert, 2015; Kawakami et al., 2013), which pose that a sad song will in fact make listeners happier. However, recent research (Zavoyskiy, Taylor, &

Friedman, 2016) did found that happy music gets rid of sad feelings. Furthermore, the research even reveals that listening to sad music makes people feel even sadder. It seems that the relationship between the emotional valence of music and emotions needs to be studied further.

Another important finding was that, when in a sad mood, a high genre preference of the heard song made listeners happier than a low genre preference towards the heard song.

This result is in line with the discussed literature about how preference for a genre can increase happiness (e.g. Liljeström, Juslin, & Västfjäll, 2013). Moreover, the findings of the current study showed that a happy rap song made listeners significantly happier than people who weren’t exposed to any song.

Overall, the findings thus show that when in a sad mood, an emo-rap (sad) song can make young adults happier. However, it seems that exposure to any type of music increased happy emotions in this study.

Limitations and implications for future research

There are different possible explanations regarding the rejection of the majority of the hypotheses. For starters, the participants in the current study were randomly exposed to one of the four songs, they had no influence in the choice of music whatsoever. Although the results

20 show that some songs worked better to alter mood positively than others, giving participants a choice of music might work even better. Liljeström, Juslin, and Västfjäll (2013) confirm that when participants get to listen to music of their choice, happiness increases. Moreover, the music choice in different studies varied from a few songs (Chan et al., 2009; Hsu & Lai,

2004) to some CD’s (Chang, Chen, & Huang, 2008) to an entire collection (Lee et al., 2005).

Despite this variety in music choice and sample, the studies show that the possibility to choose music reduces anxiety and tension. This might influence listeners happiness as well.

Another thing that these articles have in common, is the duration of music exposure. Whereas in the current study participants just listened to one song once, participants in the abovementioned studies listened to music for longer periods of time and over several days.

The participants where part of what is called (active) music therapy. Using (emo-)rap in music therapy in future studies could result in more positive emotions.

Secondly, as already mentioned, lyrics are important to recognize sadness in the heard music (Brattico et al., 2011; 2016). However, they also found that participants preferred sad instrumental music over sad music with lyrics. Related to this, Chan, Wong, Onishi, and

Thayala (2012) argue that certain aspects of the songs, like melody and harmony, can induce emotions on listeners. In future studies, it might be useful to compare the used songs in the current study with instrumental rap music. This relates to the discussed theory of music mood and movement (Murrock & Higgins, 2009), which poses that affective responses to a song’s instrumentals can alter mood as well.

Also important to highlight, Juslin (1995) provides us with a model about musical communication, which can be used in future studies to get a better understanding about which components of the used songs in the current research trigger certain emotions, like rhythm and articulation.

Moreover, where Arnett (1996) found that people listening to heavy metal together results in negative behavior, like increased intention in aggressive behavior, Liljeström,

Juslin, and Västfjäll (2013) argue that people listening to different music genres (different

‘problem’ music genres among them) together with other friends results in positive and more intense emotions. It seems there isn’t really consensus about the influence of listening to music with peers. And since the participants in the current study listened to the music by themselves, it would be interesting to clarify the differences between listening to music together and alone, especially regarding (emo-)rap.

Another argument regarding ‘problem music’ comes from Lozon and Bensimon

(2014). They argue that the music is a mere reflection of culture and environment from which the music was created, especially for problem genres like gangsta rap. Therefore music can’t be deemed positive nor negative. However, this presumption does not directly mean that music cannot have positive or negative effects on listeners. Although this is more of a cultural and philosophical explanation of the effects of music, it might be interesting to combine this with emotions derived from listening to (emo-)rap and see whether there are differences between different cultural groups.

Through the discussion of the theories, which was the basis for this research, it has become clear that people can gravitate towards sad subjects and sad emotions in music.

However, the reasons behind this attraction remains unanswered. The enjoyment of sad content is a complex paradox. Garrido and Schubert (2011) provide a possible explanation.

They argue that personality and other individual traits influence the enjoyment of sad music.

This reasoning is supported by Ladinig, and Schellenberg, (2012), which pose that individual differences, like history of music lessons and personality, influence the way people experience music. Although this seems a bit obvious, these factors should be included in future research.

Another limitation could lie in the cognitive part of the listeners. Kreutz, Schubert and

Mitchell (2008) found that there are two styles of music listening, namely music systemizers and music empathizers. Whereas the former pay more attention to the music itself, the latter are more focused on the expressed emotions. Therefore, mood altering might work better for music empathizers than for music systemizers. It is possible that the majority of the sample in the current study were music systemizers. The distinction between the two styles could be important for future research as the effects may only occur among music empathizers.

Lastly, DeMarco and Friedman (2018) argue about the difference in sadness induction.

They state that the choice for happy or sad music is influenced by fiction-based (like in the current research) or reality-based induction (e.g. documentary). They found that participants that were in the reality-based condition preferred sadder songs. So not only the emotional valence of the music is important, but also the way of inducing mood has to be taken into consideration to alter people’s mood and emotions.

In conclusion, this research contributes to decreasing the research gap regarding the relationship between emo-rap and emotions among young adults. Music is a great way to alter mood positively among young adults, when already in a sad mood. However, the influence of emo-rap on emotions among young adults can be explored more thoroughly and knowledge about this relationship can be expanded, provided that the abovementioned limitations and implications will be taken into account in future research. Only then a clearer answer can be given on which kind of music is the best to alter mood positively among young adults.

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Appendix

Sony’s $2,900 robot dog Aibo is a companion robot, one the company claims “learns its environment and develops relationships with people.” Aibo even enlists a camera in its nose to scan faces and determine who’s who so it can react to them differently.

Because of its face-detecting capabilities, Sony doesn’t sell Aibo in Illinois. The state’s

Biometric Information Privacy Act (BIPA) regulates the collection of biometric data, including face scans.

The story doesn’t stop with Sony’s quirky robot. Illinois also limits access to facial recognition in home security cameras, a feature that’s becoming increasingly prevalent in the consumer security market. Let’s take a closer look at BIPA, the growth of biometric tech in consumer products — and how other states in the US treat your biometric info.

The Biometric Information Privacy Act was established in 2008 to regulate “the collection, use, safeguarding, handling, storage, retention, and destruction of biometric identifiers and information.” BIPA defines “biometric identifiers” as retina scans, iris scans, fingerprints, hand scans, face scans and voiceprints.

Basically, an individual or a company needs “informed written consent” to use another individual’s biometric info. …

A Sony support page titled “Why Is Aibo Not for Sale in Illinois?” simply says:

Due to state regulations and policies, the Aibo™ robotic companion is not for sale or use in

Illinois.

In order to mimic the behavior of an actual pet, an Aibo device will learn to behave differently around familiar people. To enable this recognition, Aibo conducts a facial analysis of those it observes through its cameras. This facial-recognition data may constitute

“biometric information” under the law of Illinois, which places specific obligations on parties collecting biometric information. Thus, we decided to prohibit purchase and use of Aibo by residents of Illinois.

While Sony simply opted out of selling the face-detecting Aibo in Illinois, other companies, like Nest, sell their facial recognition-enabled cams in Illinois, with the facial recognition feature disabled.

At the same time that states are implementing biometric privacy laws, [more companies are offering] consumer devices with facial recognition. Two examples are the Honeywell Smart

Home Security system and Nest.

Not only is facial recognition more prevalent, there are more products that enlist fingerprints or hand scans for identification. The iPhone and other smartphones have fingerprint-scanning capabilities so you can quickly unlock your phone. At CES 2019, a smart lock called the

Elecpro US:E that relies on a face scan and a hand scan to unlock was being demonstrated.

Airports are increasingly adding tech that scans faces or fingerprints to determine who you are, too. Adam Schwartz of the Electronic Frontier Foundation (EFF) refers to the growing popularity of biometric tech as a “normalization of biometrics,” something the EFF finds concerning, he says.

“If you start using biometrics to board your airplane because it’s convenient, other forms of biometrics seem more normal. We’re very concerned about that,” explains Schwartz.

Whether or not you’re personally concerned about your biometric data, expect to see more regulations around it in the coming years. Alaska, Michigan, Montana and New Hampshire are already working on their own biometric laws. And, given the influx of devices that use biometric information both for consumer and commercial purposes, more are probably on the way.