The Negative Effects of Social Media on Adolescents
With more and more adolescents spending large periods of time online – often on platforms such as Instagram, Facebook, Snapchat, YouTube – social media use has become almost inevitable in today’s society. 85% of today’s adolescents use YouTube, 72% use Instagram, 69% use Snapchat, 51% use Facebook, with smaller percentages using Twitter, Tumblr, or Reddit (Anderson and Jiang, 2018). This high level of social media use is strongly correlated with the number of teenagers who own a smartphone, which is now at an all-time high of 95%. Of the teenagers who use a computer or a cell phone, 45% say that they are online almost constantly, while 44% say that they are online several times a day (Anderson & Jiang, 2018). This constant barrage of social media towards teenagers is likely to have some effects on their lives, particularly with the unique qualities of social media. As the lives of teenagers are increasingly invaded by social media use, adolescents are experiencing legitimate psychological effects brought on by the unique qualities of social media platforms.
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The primary function of many social media websites, especially the four that teenagers use at the highest rates (YouTube, Instagram, Snapchat, Facebook), is the dissemination of images and videos. Often, individuals use these platforms to show off a “perfect life”, sharing photoshopped images, pictures of beautiful vacations or delicious food, and videos of massive homes, material goods, and leisurely lives. Teenagers feel pressure to post content that makes them look good or that will get a large number of likes and comments from their friends. When teenagers view photos with a lot of likes, they are more likely to like it themselves. In fact, in a 2016 study, when teenagers viewed photos with many likes, they had an increased activity in neural regions related to reward processing, social cognition, imitation and attention (Sherman, Payton, Hernandez, Greenfield, & Dapretto, 2016). Today’s viewing, liking, and commenting culture has been shown to have a direct impact on the reward processing of teenagers. As a result, social media use may have significant effects on the lives of adolescents. For example, getting very few likes on a post could negatively impact an adolescent’s emotional state. Therefore, in today’s society, it is increasingly important to investigate the ways in which social media use negatively affects adolescents.
This review will analyze the negative effects of social media reported by adolescents in a variety of recent studies. The negative effects observed range from increased symptoms of depression and anxiety to an increase in risk taking and unhealthy sleep patterns. This review will also examine how the frequency and duration of social media use impacts these negative effects. Finally, this review will discuss the implications of previous research and will address directions for future research.
One widely-researched negative effect of social media use is depression. Symptoms of depression can include a steep decline in interest in activities or feeling withdrawn from others (Muzaffar et al., 2018). Many studies report different findings concerning whether or not social media use affects adolescent depressive symptoms. For example, in one study, higher depression levels were associated with higher social media use (Woods & Scott, 2016). Depression was also correlated with higher emotional investment in social media. However, the study did note that with regards to the depression model used that although 21% of the participants were classified as depressed, the majority of those participants fell within the borderline range. Thus, the study’s findings may not be generalizable to non-borderline depressed individuals.
Since depression often peaks during young adulthood, many studies also examine the relationship between social media use and depression in young adults aged 19-32 (Shensa et al., 2017). Shensa et al. (2017) specifically examined the relationship between problematic social media use and depression. Problematic social media use was defined as being excessively concerned about social media, having a strong motivation to use social media, and spending so much time on social media that it negatively affected other areas of life such as career or relationships. This study found that problematic social media use was significantly correlated with depressive symptoms even after controlling for both overall social media use (as opposed to problematic) and demographics. These results, along with the Woods and Scott (2016) results suggest a clear relationship between social media use and depressive symptoms.
However, not all studies found a correlation between social media use and depression. For example, Muzaffar et al. (2018) examined different types of Facebook behaviors and how repetitive these behaviors were. Examples of Facebook behaviors included posting a photo, searching for friends, sending messages, or commenting on another person’s status update. The study found that neither type of behavior nor repetitiveness of behavior was associated with depressive systems and suggested that the disparity between this study and other previous studies may be due to the ethnic breakdown of the study. This study was composed of primarily Latinx and African American participants. On the other hand, Shensa et al. (2017) was composed of majority white participants, and Woods and Scott (2016) did not give a racial analysis. Furthermore, Muzaffar et al. (2018) examined only Facebook use, while Woods and Scott (2016) and Shensa et al. (2017) both used the more ubiquitous term “social media”. Although some research links depressive symptoms with social media use, not all studies have found the same link, but the aforementioned differences between the studies could have resulted in this discrepancy.
Another potential negative effect of social media use is increased levels of anxiety. Woods and Scott (2016) also explored the relationship between social media use and anxiety. Some symptoms of anxiety from the scale used included feeling restless, sudden feelings of panic, and having worrying thoughts. The study found that, as with depression, increased social media use correlated with higher levels of anxiety. Furthermore, higher anxiety was also correlated with nighttime-specific social media use and having a higher level of emotional investment in social media.
Muzaffar et al. (2017) also examined anxiety as it relates to social media but differed from Woods and Scott (2016) by distinguishing social anxiety from general anxiety. Social anxiety was defined as the fear of social situations in which a person is exposed to new people or potential scrutiny from others, while general anxiety was defined as the excessive worry about activities or events and the difficulty controlling this worry. The study found no correlation between social anxiety symptoms and types of Facebook behaviors or repetitiveness of Facebook behaviors. On the other hand, the study did find a correlation between general anxiety symptoms and both types and repetitiveness of Facebook behaviors. Specifically, the study suggests that those with general anxiety may not be able to control their anxiety, and as a result, they are in the habit of returning to previously posted items.
Other potential negative effects of social media, particularly with the rise of photo editing technology, are negative body image and symptoms of eating disorders such as counting calories. Salomon and Brown (2018) examined early adolescents’ duration and frequency of social media self-objectification behaviors as they related to body surveillance and shame about one’s body. Self-objectification behaviors were defined as posting selfies of one’s face, asking for others to rate oneself, posting selfies of body with no face, posting pictures of oneself, posting pictures of oneself with others, and posting selfies of one’s face and body. Shame was defined as negative feelings towards one’s self, such as feeling weak for not obtaining the ideal body type seen in media, specifically thin bodies for females and muscular bodies for males. Although the researchers acknowledged that girls typically experience higher levels of body surveillance and shame, they suggested that the mechanism for both boys and girls experiencing shame could be the same. The study looked specifically at Twitter, Facebook and Instagram use specifically. Salomon and Brown (2018) observed that a higher use of social media for self-objectification behaviors was correlated with higher levels of body shame, and they found that this association was mostly explained by increased body surveillance. Overall, the study found that time spend social media, especially with higher levels of self-objectivation behaviors, was associated with increased body surveillance, which was then associated with increased body shame, or worse body image.
Another study examined a similar relationship between social media use and eating disorder symptoms in young adults aged 19-32 rather than in early adolescents (Sidani, Shensa, Hoffman, Hanmer, & Primack, 2016). This study examined social media use across a greater number of platforms, including Google+, YouTube, LinkedIn, Pinterest, Reddit, Tumblr, Vine, Snapchat and Reddit along with Facebook, Twitter, and Instagram. However, this study measured eating concerns rather than body shame and surveillance. Eating concerns were defined using an eating disorder screening exam, rating items such as “food dominates my life”, “someone has expressed concerns about my eating patterns”, “my weight negatively affects the way I feel about myself”, and “losing control over how much I eat concerns me”. The researchers found that when comparing participants who scored in the highest quartile of the study in terms of social media frequency or volume to participants who scored in the lowest quartile of the study, that there was a significantly higher risk of eating concerns among those with greater social media use.
Another consequence of high social media use is unhealthy sleep patterns. So often, adolescents stay up late on their phones or get alerts throughout the night, waking them up. Woods and Scott (2016) found that poorer sleep quality correlated with higher social media use, high emotional investment in social media, and, unsurprisingly, nighttime-specific social media use. Poorer sleep quality also correlated with lower self-esteem and higher depression and anxiety levels. However, this correlation was found without any understanding of the mechanism behind it. Scott and Woods (2018) again studied sleep and social media use, this time with the lens of “fear of missing out”, or FOMO. FOMO is described as a state of anxiety brought on by the fear of missing out on social experiences, which is often facilitated by social media. Adolescents with higher levels of FOMO were found to use social media at higher levels during the night. Furthermore, adolescents who had higher levels of social media use at night tended to go to bed later, feel more alert in bed, take longer to fall asleep, and sleep less. Two mechanisms were suggested to explain these findings. First, FOMO entices adolescents to engage more in social media during the night out of feat that they are missing some social event. Second, having anxiety over FOMO keeps adolescents awake longer during the night. Both Sidani et al. (2016) and Woods and Scott (2016) show a clear correlation between unhealthy sleep patterns and use of social media, with the possible mechanism of FOMO.
Another potential negative effect of social media is an increase in risk-taking behaviors, specifically with regards to sexual activity. Romo et al. (2016) examined the correlation between adolescent sexual activity and social media use. They found that frequent social media users had higher rates of sexual behavior such as kissing, oral sex, and vaginal sex as compared to less frequent social media users. Higher social media use was also associated with use of contraception, use of emergency contraception, and being unsure of partner’s use of contraception. Sexting was also found to be more likely among adolescents with higher levels of social media use (Vente, Daley, Killmeyer & Grubb, 2017). Both studies found higher levels of sexual risk taking among teens with higher levels of social media use.
Vente et al. (2017) also studied the relationship between social media use and self-harm, which they defined as nonsuicidal self-injury (NSSI). They found that adolescents with higher social media use had higher rates of NSSI than those with lower social media use. Sampasa-Kanyinga and Lewis (2015) examined the relationship between mental health and daily use of social media and found that individuals who reported more than two hours of daily social media use had higher rates of unmet needs for mental health support, higher rates of psychological distress, and significantly higher rates of suicide ideation. Suicide ideation was assessed by asking the question, “During the last 12 months, did you ever seriously consider attempting suicide?”. Based on the findings of these studies, self-harm, suicide ideation, and other mental health issues are potentially negative effects of increased social media use that must be addressed.
Two other negative effects found in the literature included increased rates of underage drinking and lower academic performance. Brunborg, Andreas, and Kvaavik (2017) examined the frequency of episodic heavy drinking, which they defined as drinking four (for girls) and six (for boys) drinks in a day. They surveyed Norwegian middle school and high school students about their social media use and the frequency of their episodic drinking. The study found that spending more amount of time on social media was associated with a greater likelihood of episodic heavy drinking, even after adjusting for the factors of age, impulsivity, sensation seeking, depressive symptoms, and peer relationship issues. Rosen, Carrier and Cheever (2013) explored how distractive technology affected academic performance. The researchers observed middle school, high school, and university students studying in their homes. Even though the students knew they were being observed, students still exhibited distracted behavior, switching away from their studying on average every 5-6 minutes. Students who checked Facebook one or more times during the fifteen minutes also had, on average, lower GPAs.
However, despite the numerous negative effects observed by researchers, spending time on social media also has positive effects. For example, van Schalkwyk, Ortiz-Lopez, Volkmar, and Silverman (2016) found that for students with Autism Spectrum Disorder (ASD), a group that typically struggles with social interaction, use of social media improved the quality of their friendships. The researchers found that social media use could improve the friendships of students with ASD. However, if the students suffered from anxiety, that anxiety was experienced online as well. This is one example of the variety of potential positive effects of social media. Others, such as easy access to information and news, streamlined communication, and greater ease among adolescents in social situations facilitated by online interactions, are notable. However, the negative effects are undeniable.
This review and the studied cited have several limitations to note. With regards to the review itself, the studies referenced were selected by the reviewer so as to highlight the negative effects of social media on adolescents. Studies showing positive effects were generally ignored, as were studies that did not examine adolescents or young adults. For the purposes of examining the narrow topic of the negative effects of social media use on adolescents, this selection bias is not an issue. However, it does limit the generalizability of the discussion in the review to the impacts of social media use on other age groups. Furthermore, as a result of this selection bias, this review should not be viewed as an authoritative discussion on the positives and negatives of increased social media use among adolescents. Additionally, since the field of social media research is relatively new, all of the research found postdates 2013. This, however, is not a limitation imposed by selection bias, but rather is a limitation created by the newness of the field itself.
Limitations of These Studies
All of the studies referenced were self-reported, so neither the demographics of the participants nor the research itself is verified. This could be a problem in terms of replicability, which is seen in the difference between the results of Shensa et al. (2017), Muzaffar et al. (2018), and Woods and Scott (2016). As these studies examined different demographic, socioeconomic, and age groups, there is a limit to how generalizable the findings are.
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Directionality. Another problem relating to the recency of the field is that all of the studies cited were unable to determine directionality, since all of the studies were cross-sectional rather than longitudinal. Woods and Scott (2016) noted that it was unknown whether social media use heightens depression or if depressed individuals are more likely to use social media. They also noted their uncertainty whether increased social media use caused higher levels of anxiety or whether individuals with higher levels of anxiety were more likely to use social media. Sidani et al. (2016) also noted that the directionality of the study could not be determined, so it was unknown whether young adults with eating concerns are more likely to use social media, or young adults with higher use of social media were more likely to have eating concerns. Rosen et al. (2013) stated that the researchers did not know if students who spent more time on Facebook ended up with lower GPAs or if students who had lower GPAs spent more time on Facebook. Sampasa-Kanyinga and Lewis (2015) were not certain whether suicidal adolescents spent more time on social media, or whether increasing time spent on social media was more likely to encourage suicide ideation. None of the studies addressed in this review were able to establish directionality due to the recency of the field.
Race. Furthermore, the studies were conducted across a variety of different countries and were not limited to the United States. Sampasa-Kanyinga and Lewis (2015) conducted their research in Canada, Woods and Scott (2016) in Scotland, Scott and Woods (2018) in the United Kingdom, and Brunborg et al. (2017) in Norway. Furthermore, these four studies also did not control for or provide data on race, nor did Vente et al. (2017). Without racial data, it is impossible to know who these studies observed and which populations this research can be generalized to.
The remaining studies provided racial data. However, there was a large variation of racial makeup across studies. Shensa et al. (2017)and Sidani et al. (2016) had a white majority of 57.3% and 57.2%, respectively, and Salomon and Brown (2019), had a plurality at 45%. These studies are therefore more likely to be generalizable to a majority white population. It is interesting to note that both of the studies concerning eating disorders and negative body image are two of the three studies in which a white majority was observed. On the other hand, Muzaffar et al. (2018) and Romo et al. (2016) observed a Latinx majority of 60.8% and 80.2% respectively, and Rosen et al. (2013) had a Latinx plurality of 43.7%. These studies are more likely to be generalizable to a majority Latinx population. These three studies all examined different factors: sexual risk, distractibility and academic performance, and anxiety and depression.
Age. In addition to race, other demographic factors given differed as well. The age ranges of the studies cited varied as each focused on early adolescents, adolescents, or young adults. Salomon and Brown (2019) was the only study to focus on early adolescence, thus their finding that increased social media use correlated with body shame may be ungeneralizable to other age groups. Sidani et al. (2016) and Shensa et al. (2017) focused on young adults rather than adolescents, so conversely, their research may not be generalizable to younger adolescents.
Gender. Another factor that could affect the generalizability of these studies is gender. Six of the studies had a strong majority female gender breakdown. Romo et al. (2016) was 66.2% female, Rosen et al. (2013) was 55.5% female, Muzaffar et al. (2018) was 56.9% female, Scott and Woods (2018) was 66% female, Salomon et al. (2019) was 69.7% female, and Sampasa-Kayinga and Lewis (2015) was 55% female. These studies, due to their strong female majorities, may not be generalizable to male populations. Furthermore, neither Vente et al. (2017) nor Woods and Scott (2016) gave gender breakdowns for their studies. Therefore, the generalizability of their findings across gender is unknown.
SES. Another limitation of these studies is their lack of response to differences in socioeconomic status. Very few studies mentioned gathering data on socioeconomic status. Brunborg et al. (2017), Muzaffar et al. (2018), Romo et al. (2016), Rosen et al. (2013), Scott and Woods (2018), Woods and Scott (2016), and Vente et al. (2017) all failed to address socioeconomic status. Therefore, whether or not socioeconomic status was a factor in their results cannot be verified. Salomon and Brown (2019) examined students from 4 middle schools, with a range of 49%-85% qualifying for a free lunch at the middle schools. This short mention of socioeconomic status is not enough to verify the range of socioeconomic status in their participants, so socioeconomic status as a factor in their results cannot be addressed. Similarly, Sampasa-Kanyinga and Lewis (2015) categorized their participants into low or high socioeconomic status but did not state the percentages or the ranges for each category, again making this factor impossible to address. Both Sidani et al. (2016) and Shensa et al. (2017) were the only studies to give percentages and ranges for low income (under $30,000 annually), medium income ($30,000-$74.999 annually) and high income ($75,000 or above annually). Their percentages were both skewed towards middle and high income but with a significant minority of low income participants. Therefore, only these two studies could argue that socioeconomic status did not have any bearing on their results.
Duration versus frequency. Finally, one more limitation of these studies is the variation of how each study measured high levels of social media use. Some studies focused more on duration of time spent on social media, while other studies focused more on frequency of times going on social media. Romo et al. (2016) and Scott and Woods (2018) both focused on frequency, as did Sidani et al. (2016) who looked at times on social media a week, and Shensa et al. (2017) who looked at times on social media a day. Many other studies looked at duration of time on social media, such as Salomon and Brown (2019), who examined hours spent on SM a week. Muzaffar et al. (2018), Rosen et al. (2013), and Sampasa-Kanyinga and Lewis (2015) all examined hours of social media use per day. Several studies examined more specific types of social media use. For example, Vente et al. (2017) looked at hours on social media a day and also how many types of social media were used, Woods and Scott (2016) combined how often and how many hours a day participants were on social media, and Brunborg et al. (2017) also combined times a week and hours per time. This variation in frequency and duration is another limitation to the generalizability of each study’s findings.
Conclusions and Future Study
Longitudinal research is necessary to determine the directionality of all of the effects seen. Studies should also take care to replicate previous research with the lens of intersectionality, examining the effects of race, gender, socioeconomic status, sexuality and other factors on the correlation between increased social media use and the observed negative effects on adolescents. Further research is needed to determine if duration or frequency of social media use results in a measurable effect on the negative effects observed among adolescents. Such research should address whether longitudinally, duration or frequency matters more.
Researchers should also conduct novel research to determine if there are any key factors that predispose adolescents to the negative effects of increased social media use. Additionally, such research would be useful for determining if there are certain factors that predispose adolescents to the positive effects. Future research should look to determine why these negative effects observed may be exacerbated by increased social media use. Specifically, research is needed to determine the precise mechanisms at play, whether they be neurological, environmental, or other pathways. Finally, researchers should also examine how social media can be regulated in order to make its use more positive and limit the negative effects of increased use. All this research and more should be conducted to illuminate the topic of the negative effects of social media on adolescents.
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