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Abstract

The purpose of this study was to explore the addictive nature to being on social media, are young people on social media more than older people are on social media. The study hypothesized that younger people get more addicted to social media than older people. The study found out that there was a correlation between social media addictiveness and age was not significant. This means that age does not determine whether an individual can get additive to social media or not.

Addiction to social media can be interpreted as a type of addiction to the Internet, where people are forced to use social media in abundance (Abbasi, 2019). People with an addiction to social media are often unnecessarily anxious regarding social media and are motivated by an uncontrolled desire to log in and use social media. Symptoms of exposure to social media may be expressed in personality, memory, physical and emotional responses, and interpersonal and psychiatric disorders.

Addictive being young and older on Social Media

The rise in social media use has coincided with a significant increase in the number of time people spent online over the past decade. Adults in the United States, for example, devote more than 6 hours a day to streaming channels. They will use applications and websites on their phones, laptops, computers, and other smart gadgets, including gaming consoles, to use social media. There is proof that people in other wealthy countries devote several hours a day online as well. Individuals, groups, and organizations can upload, co-create, address, partake in, and change user-generated or self-curated information shared online as users connect with any of these technological tools. Additionally, through the development of websites, podcasts, videos, and gaming pages, social media is used to record experiences, learn and discover topics, express oneself, build relationships, and grow ideas. The new field of techno personality focuses on the changing interactions between individuals and technology. This research aims to look at how people of different ages use social media.

Personal characteristics may certainly illustrate who uses social media and who does not the relationship between extraversion and openness is positive with social media, whereas the relation between emotional stability and social media is negative. Social media appear to be used more by people with greater social comparison alignment than people with less social comparison orientation. This literature review examines various factors affecting social media use by different groups and makes informed decisions on which age groups are most used in social media.

Social Media Usage Is Based on Appearance-Related Online Activity and Self-Esteem

Zimmer-Gembeck et al. (2021) looked at how attractiveness-related online behavior correlated with personality and appearance contrast. Self-presentation and presentation comparison, on the other hand, were more closely related. They discovered that younger generations used social media mostly to improve their looks. Midgley et al. (2020), on the other hand, correlated social media use by groups dependent on self-esteem. People who had poor self-esteem had severe upward similarities, which expected lower self-evaluations in between each correlation. Furthermore, people with poor self-esteem made more upward distinctions, implying that they would have lower self-esteem and satisfaction level during the session.

Social Media Use Based on Mood

Yuen et al. (2019) looked at how different people used social media depending on their mood. Their results indicated that since young people experience the most mood swings, they are more likely to use social media than adults. The findings showed that when participants selectively browsed Facebook, their mood had dramatically deteriorated relative to when they browsed the Internet. Furthermore, feelings of jealousy influenced the connection between online interaction and mood, but not feelings of meaning. Furthermore, according to Aalbers et al. (2019), PSMU attendance did not anticipate depressive symptoms, distress, or anxiety. On the other hand, previous exhaustion, and loneliness projected PSMU, implying that these symptoms would cause participants to browse through social media sites.

Hu et al. (2018), on the other side, say that the key effects of media personality and transgression seriousness on PSR elimination, attribution of triggers, and forgiving are important. The association between PSR and forgiving was partly influenced by cause attribution. As a result, Naslund et al. (2016) suggest that people with major mental illnesses who participated in lifestyle programs by the neighborhood and mental health centers have smartphone and internet access rates equal to the general population. Mobile phone use and the use of the Internet, instant messages, and social media are equal to the general public.

Social Media Usage Based on Age

According to Uchino et al. (2016), the number of ambivalent network relations expected higher diastolic blood pressure reactivity. There was also an Age Ambivalent Relations relationship, in which the number of ambivalent ties was linked to higher systolic blood pressure reactivity, particularly in older adults. These findings are explored in terms of how social networks affect people's health across their lives. Von Hippel et al. (2008), on the other hand, conclude that social fulfilment in late adulthood derives from several outlets. Older people are more optimistic and less pessimistic than younger people. Nonetheless, cognitive interaction and working memory diminish with age, potentially limiting older adults' social functioning. These socially facilitative vs. socially debilitative shifts were found to underpin social happiness stasis in the latest research of younger and older adults. The lack of an aggregate impact for age will obscure vying for social functioning improvements in late adulthood when social pleasure channels can shift even though the result does not.

Furthermore, Baltes & Lang (1997) say that the resource-rich group's young-old older adults spent less time engaged in academic or cultural practices than the old-old older adults. This may be the outcome of compensating or refining techniques. Also, according to Yeung et al. (2008), the well-documented negative link between age and the number of peripheral social partners was observed only among Chinese adults with lower interdependence, not among those with higher interdependence. These results stress the importance of looking at the underlying cause rather than a specific SNC trend in various cultures.

Hypothesis

I predict younger people use social media more than older people. My study set out to explore the relationship between the time spent on social media use and age. The study further hypothesized that younger people tend to use social media more than older people. The literature review results support the hypothesis, meaning that younger people use social media more than older people. Some authors have associated this from the literature review with the younger people having less depression, being mentally stable, and more time to socialize than the older people.

Methods

Design

This study uses a correlational design to test the hypotheses was that who is more addictive on social media a younger or an older person are related to social dominance and social media orientation. Then mediational regression will be used to test whether these relationships are maintained when social desirability is included in the model. Finally, a between samples test will be conducted to determine if there are any gender differences for the personality variables or social dominance.

Participants

The study included a convenience survey of 49 volunteers who were recruited through my personal Facebook social media page. _35____ participants were women, __14_ were men, and there were _2_ gender responses. The mean age was _45__. The racial and ethnic breakdown was _100_.__% African American/Black.

Procedure

Friends, family, and classmates were recruited from on my face book Social Media page and posted on my survey window. After participants received a personal message with survey, they were taken directly to the online survey created on Qualtrics. They were first presented with an informed consent paragraph detailing the purpose of the study and any potential risks. By clicking “I agree” to the question “Are you at least 18 years old and do you agree to participate?”, they were giving their consent to continue with the study and were presented with the measures detailed below. At the end of the study, they were given a confirmation message to thank them for helping me fulfill my research requirements.

Results

Measures

Time Spent on Social Media

A fourteen-item measure was developed for this study; survey items include “I’m on social media daily”. These items were measured on a 5-point scale from strongly agree to strongly disagree. So that high scores indicate more time spent on social media.

Demographics

General demographics included questions about amount of time on social media, age, and gender. Participants were asked their social media usage on a 5-point scale from strongly agree to strongly disagree. These questions were used to evaluate the demographics of the sample.

Main Analyses

References

Aalbers, G., McNally, R. J., Heeren, A., De Wit, S., & Fried, E. I. (2019). Social media and depression symptoms: A network perspective. Journal of Experimental Psychology: General, 148(8), 1454-1462. https://doi.org/10.1037/xge0000528

Abbasi, I. S. (2019). Social media addiction in romantic relationships: Does user's age influence vulnerability to social media infidelity?Personality and Individual Differences, 139, 277-280.

Baltes, M. M., & Lang, F. R. (1997). Everyday functioning and successful aging: The impact of resources. Psychology and Aging, 12(3), 433-443. https://doi.org/10.1037/0882-7974.12.3.433

Hu, M., Young, J., Liang, J., & Guo, Y. (2018). An investigation into audiences’ reactions to transgressions by liked and disliked media figures. Psychology of Popular Media Culture, 7(4), 484-498. https://doi.org/10.1037/ppm0000146

Midgley, C., Thai, S., Lockwood, P., Kovacheff, C., & Page-Gould, E. (2020). When every day is a high school reunion: Social media comparisons and self-esteem. https://doi.org/10.31234/osf.io/zmy29

Mirzaei-Paiaman, A., Asadolahpour, S. R., Saboorian-Jooybari, H., Chen, Z., &Ostadhassan, M. (2020). A new framework for selection of representative samples for special core analysis. Petroleum Research, 5(3), 210-226.

Naslund, J. A., Aschbrenner, K. A., & Bartels, S. J. (2016). How people with serious mental illness use smartphones, mobile apps, and social media. Psychiatric Rehabilitation Journal, 39(4), 364-367. https://doi.org/10.1037/prj0000207

Uchino, B. N., Kent de Grey, R. G., & Cronan, S. (2016). The quality of social networks predicts age-related changes in cardiovascular reactivity to stress. Psychology and Aging, 31(4), 321-326. https://doi.org/10.1037/pag0000092

Von Hippel, W., Henry, J. D., & Matovic, D. (2008). Aging and social satisfaction: Offsetting positive and negative effects. Psychology and Aging, 23(2), 435-439. https://doi.org/10.1037/0882-7974.23.2.435

Yeung, D. Y., Fung, H. H., & Lang, F. R. (2008). Self-construal moderates age differences in social network characteristics. Psychology and Aging, 23(1), 222-226. https://doi.org/10.1037/0882-7974.23.1.222

Yuen, E. K., Koterba, E. A., Stasio, M. J., Patrick, R. B., Gangi, C., Ash, P., Barakat, K., Greene, V., Hamilton, W., & Mansour, B. (2019). undefined. Psychology of Popular Media Culture, 8(3), 198-206. https://doi.org/10.1037/ppm0000178

Zimmer-Gembeck, M. J., Hawes, T., & Pariz, J. (2021). A closer look at appearance and social media: Measuring activity, self-presentation, and social comparison and their associations with emotional adjustment. Psychology of Popular Media, 10(1), 74-86.