The Inequalities of Status on Social Media

The Inequalities of Status on Social Media

The essay explores the inequalities of status on social media, using Twitter as an example. Based on Nancy Fraser’s observation, the paper provides examples and case studies of the inequalities by showing that:

1. The systems used in the operation of social media platforms contribute to the development of the inequalities.
2. People who are literate in the use of social media platforms influence the kind of information circulated.
3. The elite exerts more influence over new digital social platforms.
4. Socio-economic backgrounds factor in since the elite dominates new digital social platforms.
5. The use of social media platforms resulted in the creation of new social divides.
6. The new divides then resulted in increased psychological distress.
7. Only a small percentage of social media users produce the information shared on the platforms while the remaining percentage are observers.

Questioning Social Media

There are many questions that one can ask and that need to be asked about social media. Such critical thought aims to create a structure that can benefit everyone in the society and media. Twitter, as a form of social media, makes use of the analysis of big data (Fuchs, 2017, 55). Algorithms are involved in the investigation, such that the algorithms make choices and give a definition of needs for humans. The algorithms make assumptions similar to human thought and mannerisms according to how the developers of the algorithms program the system. Unfortunately, the algorithms do not take beliefs and principles into consideration but act according to linear logic. Human beings are complex as compared to the algorithmic systems and so the reasoning of the algorithms is subject to error, especially in the creation of false truths. The consequences can be dire for human beings as innocent people may end up framed as criminals and face discrimination by financial institutions and public services. The creation of such suspicion dictates that one is either innocent until the presentation of evidence to prove one guilty or one is guilty until proven innocent in the same manner. The control of big data in the manner described provides a scapegoat and a distraction from the roots of societal problems. In essence, the use of algorithms for inference introduces new methods of judgment that comprise of rational discrimination, exploitation, and aggregate disadvantage. Whoever owns and controls the big data becomes powerful and puts the less powerful at a disadvantage.

Inequalities in Access and Influence

Inequalities such as moral authority, affluence, social class, political power, status, and cultural capital contribute to social divide. There is need to involve people of high status for the advancement of digital literacy yet the people of less influence are the ones savvy with social media technology (Muschert & Ragnedda, 2013, 168). Several studies discovered that the people with better access to technological resources take advantage to increase the existing inequalities. The people can do so because socio-economic factors and abilities stratify the access to create and spread information across social media platforms. Access to the internet has little effect on the matter in question (Muschert & Ragnedda, 2013, 173). The major challenge is the lack of awareness and insufficient promotion of the existence of digital resources, digital illiteracy, and a general gap in knowledge. The motivation for the group with technological education is to seek validation through the interactions on social media platforms. On the other hand, the group without the know-how has little concern with the developments on the platforms. As a result, there are issues of collaboration and communication breakdowns between the two groups. Ultimately, the people of high status lack the motivation to be involved in social media technology while those of less influence control the system and contribute little to the advancement of digital literacy.

The Impact of Socio-Economic Status

Commentators have also argued that the internet reinforces social inequalities. Recently, studies have established that people who have a higher socio-economic status make productive use of the internet as compared to those of a lower status (Aillerie & Mcnicol, 2017, 3-4). The result is that the elite dominates the new digital social platforms, even though some social networking services (SSN) are linked to specific social classes more than other services. Socio-economic status remains a significant influence as people on the lower end of the spectrum are disadvantaged. Research shows that people from high socio-economic backgrounds use social media platforms for enlightening and self-improvement while those from low backgrounds use the platforms for entertainment. The group from high socio-economic backgrounds tends to seek out information related to academic and educational matters to obtain potentially valuable information. In the comparison of the two groups, a constant factor for both parties is easy to access to social media technology. The issue of societal expectations comes into play (Aillerie & Mcnicol, 2017, 9). The society expects the parties in question to behave in a particular manner and the groups conform to the expectations. There is a clear demonstration of the inequality that arises from socio-economic backgrounds.

The Negative Impact of Social Networking Platforms

Another observation that researchers have made is the increase and use of social networking platforms. Researchers noted that the intensified use impacted social trust and civic involvement among the users (Cho, 2014, 2816-2818). The impact has been negative on family interactions as more people substitute face-to-face conversation with a conversation over social media platforms. As a result, people use social media platforms to communicate with other people of significance to the given time while the less important people are forgotten. Before the introduction of social platforms, conversations held one-on-one did not exclude the less significant people. The people who are left out experience what researchers call social deprivation due to the comparison the group makes with the group of people who receive more attention. The increased use of social media platforms by the three parties involved ultimately results in increased psychological distress (Cho, 2014, 2816-2825). Scholars observed the impact of the distress through increased loneliness and lowered self-esteem among the respondents in a study. The group that received less attention could relate to the inequality experienced due to the use of social media platforms.

The Influence of Top Users

The final argument states that only a small percentage of social media users produce the information shared on the platforms while the remaining percentage are observers. Researchers conducted a study to evaluate the behavioral tendencies of users on Twitter (Momeni & Rabat, 2016, 15). The basis of the research was in the analysis of the spread of information in the form of user posts. The researchers examined the flow of the information shared in the posts and highlighted the trends involved. The result obtained was that many users tend to converge around other users who have high attributes on the platform. The researchers established that many users would follow other users with similar or higher attributes. The finding implies that Twitter users in the top percentile of users with high attributes exert more influence over the platform as compared to other users. Such users have more say over the information that flows in the discussion they are involved in as compared to other users with low attributes.

Conclusion

In conclusion, the essay gives a demonstration of how the inequalities of status play out on social media platforms. The first case scenario is of the systems that social media platforms use. Algorithms are involved in the analysis of big data collected from social media users. The algorithms make choices and define needs for humans. Human beings are complex as compared to the algorithmic systems and so the reasoning of the algorithms is subject to error, especially in the creation of false truths. In the second instance, there is a need to involve people of high status for the advancement of digital literacy yet the people of less influence are the ones savvy with social media technology. The group with less influence controls the system and contributes little to the advancement of digital literacy. A third demonstration is of the elite’s domination of new digital social platforms. Socio-economic status remains a major influence as people on the lower end of the socio-economic spectrum are disadvantaged. Another case of inequality is in the prioritization of people that creates a previously non-existent divide in a community. The newly created divide results in increased psychological distress. Lastly, only a small percentage of social media users produce the information shared on the platforms while the remaining percentage are observers. The producers of the information are individuals who have accrued top attributes amongst the users of a social platform. As a result, such individuals attract other users with similar or lower attributes. The individuals with high attributes then tend to control the flow of the discussions the users are involved in. One can rightfully conclude that even though the development of social media platforms has proven advantageous to the enlarging of social interactions, the platforms have also served to increase the gaps of inequality of status on social media.

References

Cho, J., 2014, Will Social Media Use Reduce Relative Deprivation? Systematic Analysis          of         Social Capital’s Mediating Effects of Connecting Social Media Use with Relative       Deprivation, International Journal of Communication, Vol. 8, Pp. 2811–2833.

 Fuchs, C., 2017, “Social Media: A Critical Introduction,” Sage: Los Angeles, Print.

Mcnicol, S. & Aillerie. K., 2017, Digital inequalities and social media: experiences of young people in Chile. Information and Learning Science, Emerald Insight, Vol. 118, No. 7/8,           Pp. 372-384.

Ragnedda, M. & Muschert, G., 2013, “The Digital Divide: The Internet and Social Inequality in    International Perspective,” Routledge: New York, Print.

Momeni, N. & Michael Rabbat, M., 2016, Qualities and Inequalities in Online Social Networks        through the Lens of the Generalized Friendship Paradox, PLoS ONE, Vol. 11, No. 2, Pp.      1-17: e0143633. doi:10.1371/journal.pone.0143633