Subheading: It is evident that the advancement in technology and the advent of social media has brought a difference in social relationships. People form relationships in various ways and such relationships are directly linked to how an individual thinks or behaves. What type of media people prefer, individual’s susceptibility to a media source or relationships are analyzed.
In a recent survey by 'Making Done a Common,' approximately 950 American teenagers reported feeling lonely after the COVID-19 pandemic. This value increases 36% of the teenagers who felt lonely before the pandemic. Like this, people are isolated much more than we think; thus, the current 21st century and the pandemic of COVID-19 emphasize the importance of community even more. One of the essential parts of our lives is forming social relationships and being part of a community.
Nevertheless, young people use various social media to supplement their social relationships, and social media is and has been a rising trend among people: a recent platform that became popular among these generations, the Clubhouse. As an example, in South Korea, a young age known as the MZ-Generation has emerged as a new social group with their bases on online communities within an individualistic lifestyle based on technology. Sometimes this disparity challenges some of the old trends of the old generation, which forms a unique layer(different) between these generations. Thus, it can be implied that social media plays a vital role in communication between each individual as well as affecting each other's conception.
As of the 2019-2020 survey conducted by Statista, each internet user uses an average of 145 minutes on social media each day. This implies that social media is a significantly massive portion of our lives and is an alternative way of conducting our daily lives, including the social interaction between relationships. This may have some influence on how people interact and form social relationships. For example, how much a person feels secure about the relationship may differ, and the personal belief regarding the social relationship may alter. Thus, this research aimed to correctly identify the trend and influence of social relationships and social media uses and create an algorithm: the predictable measure of social relationships and social media's impact on people.
Four different questions were asked to correctly classify the respondents into specific categories and identify the respondents' characteristics. These questions consisted of:
An individual's sociability
An individual's likeability of using technology
An individual's representative adjective of personality
An individual's interest
Not only about these factors the central division of respondents, but also the primary characteristics such as age, gender, and nationality were used to analyze the respondents, and these variables collected were taken into consideration while analysis and while creating the linear regression algorithm for measuring the predictable measures of social media susceptibility. Key variables that were taken into account were the sociability of a respondent, the enjoyability of technology which was statistically significant to analyze with the amount of data collected. Adjectives(personality characteristics) and trending topics would apply to a more substantial sample of data and, through this research, was identified as a secondary variable due to its statistical significance. Last but not least, age was a significant portion of the comparison that was taken into account. As social media and social relationships appear differently between each generation, it was vital that the comparison between the age groups was to be made.
Upon the difference between generations, it was shown that the elder generation(30 or above) had more use of Youtube and Facebook rather than other social media sources. The teenage (13-29) generation had their primary benefits of social media on Instagram and some other social media channels that included a mobile chatting service. Furthermore, the youth generation (13 or younger) showed their primary use of social media on gaming sources such as the rising metaverse platforms - Minecraft and Roblox- that give entertainment and a source of communication. Also, a supporting claim about this was that 50% of the U.S students have been using Roblox over the last vacation, showing that each generation has different preferences on social media. Not only about such online media uses, but it was also represented throughout the research that offline interaction still plays a vital role in social relationships and how people feel secure about those relationships made within.
The specific characteristics analyzed also correlated with specific social media and the predictable measures given. One example can be that the more social the person is, the more likely the respondent was able to share their lives through social media and that their belief on the effect of social media was on a medium range. But for the respondent with less sociability, it was more likely shown that the respondent was more likely to share their personal beliefs through social media and believed that their thoughts were more likely to be affected by social media. The use of technology also showed a similar correlation between these measures. This indicates that certain characteristics have specific strengths and benefits on the different types of social media. These dependent characteristics can be one of the reasons why the generations have other preferences in making social relationships and using social media.
With such concepts of how offline interaction still is the preference of making social relationships and a platform that is trusted, this can be the supporting reason why platforms such as 'metaverse' and 'virtual reality are becoming more popular with a combination of both social interaction and online source of matching, incorporating real-world interface as a metaphor.
As the survey contained a large variety of populations, it may not have a sign of trend applied to a specific population around the world. As a survey conducted in South Korea, more than ⅔ of the respondents had a nationality in South Korea; thus, the people can not be generalized into the global population. Also, it is to be seen that the collection of data was minimal, which in order to have a statistically significant value or prediction of an algorithm, better design and data sample will be required. Also mentioned above, a few of the variables did not show statistical significance over the analysis in which more data will be needed to state a thesis of a predictable measure of personality accurately. The sample was too small to apply all personality traits into a single correlation generally.
This research not only shows the primary meanings of the data but also shares a secondary meaning and a general implication for future research. Some of those implications may include that if more data can be collected, it can create a more accurate algorithm of predicting social media measures and social media usage on social relationships. For example, a person who is less social but is an enjoying technology user may be more likely to express their beliefs and lives through social media, and their preference of social media would be different from the other person who might be social who may be less on expressing their thoughts on social media preferring a different platform. These measures are essential in the future as new applications are to be created incorporating other functions and preferences of each person. This data collection will be a necessary part of creating specific applications targeted for a particular age group or population, and with it, any profits or benefits could be made.
Further studies may validate the results of this research, but it is also susceptible that this research can be conducted in different fields of predicting measures. For example, another question can be the social media usage pattern on certain media having more attraction over another. This way, the public interface of social media can be revised, and the type of media portrayed may be changed accordingly. This concept also leads to the big idea of 'affective computing,' which, when A.I(Artificial Intelligence) can prove statistical evidence or trends, will be applied to many more fields of the world to provide the best suitable actions and suggestions for life-dependent decisions.
References
Daily Social Media Usage Worldwide. Statista. (2022, March 21). Retrieved April 8, 2022, from http://www.statista.com/statistics/433871/daily-social-media-usage-worldwide/
Most used social media 2021. Statista. (2022, March 8). Retrieved April 8, 2022, from http://www.statista.com/statistics/272014/global-social-networks-ranked-by-number-of-users/.
Walsh, C. (2021, February 17). Young adults hardest hit by loneliness during pandemic, study finds. Harvard Gazette. Retrieved April 8, 2022, from http://news.harvard.edu/gazette/story/2021/02/young-adults-teens-loneliness-mental-health-coronavirus-covid-pandemic/.
social media and marketing. pixabay. (n.d.). Retrieved April 10, 2022, from https://pixabay.com/ko/photos/
[Survey Reference]
Comments