In recent years social media has become the foundation of communication between information outlets and citizens across the country. Because of this, many researchers have theorized that social media has caused a division between the United States’ two political parties and the citizens that affiliate with them when taking a stance on policies and political actions. Ultimately the more a user utilizes social media, the more polarized their stance on political topics will become, resulting in factors that confuse the user politically and tilt their political stance towards an extreme. One main theory that explains this is the political affirmation that is received by the way social media is structured. As social media users utilize the platform, the algorithm continues to feed the user with topics that appeal to him or her, therefore creating a one-sided stance on a vast majority of political stances.
There are three types of information that are provided on these social media platforms. The first type that researchers Born, Gorodnichenko, King, and Prior all highlight in their research is politically inaccurate information that can dilute the legitimacy of information that citizens receive daily. This can cause up to a 62 percent effect on the decision-making procedures that political officials go through to decide on a stance to suit the public opinion. Politically inaccurate information was a huge topic discussed in the 2016 Presidential election when Donald Trump accused the New York Times of discrediting his campaign by producing this ‘false and misleading information’. The second type of information that is spread throughout social media is misinformation. Kelly Born differentiates politically inaccurate information and misinformation by the identification of a credible source behind it. Typically, politically inaccurate information is used as a tool to discredit other politicians while misinformation is mainly used to dilute the media of its credibility and cause distrust in the audience it targets. The third and final type of information that has been founded by Kelly Born is political propaganda that is spread throughout social media to push opinion towards a specific stance. Political propaganda is the type of information that has a planned-out agenda behind it. The issue with this form of information is that it damages the target audiences by using emotion as a tool to divide the populace leading to an overall dissonance in political information that then causes a paradox between sources that contradict each other.
The paradox that has been found by Lars Haahr (2014) begins when a credible source is either found in a private or public domain (private domain including information coming from companies and organizations whereas public domains include official statements coming from politicians in power). Information received from a private domain could contradict information received on a public domain leading to distrust in one domain or vice versa (typically information contradicted between social media and government officials). This information paradox is also found in the information received between the state government and federal government. Because of the information paradox, users tend to move their political views towards polarized stances the more a topic aligns with a federal issue. There have been multiple sources that show how this falsifiable information affects society. Gary King, Jennifer Pan, and Margaret Roberts discuss how foreign nations like the Chinese Federation have pushed their political agenda by using over two million bots to produce approximately 480 million political comments a day to push the two political parties further away from each other by posting constant affirmation towards one side or the other.
Public opinion on the credibility of social media or any other information source can exacerbate the polarized political society we live in. However, social media can also minimize polarity by giving light to issues that are relevant and reliable. This scenario can only occur if the people hold the government accountable to the Checks and Balances that were agreed upon during the founding of the United States. Polarization can be solved if the people fact checks their information with other reliable sources. Although this could seem like a tedious task, it is crucial during this time of political insecurity. Ultimately because of the types of information that is received through social media can cause many different routes towards a polarized stance on the political decisions the government goes through. Polarization needs to be addressed in social media before our government takes more steps towards disinformation in public engagement.
Born, K. & Edgington, N. (2017). Analysis of philanthropic opportunities to mitigate the disinformation/propaganda problem. Hewlett Foundation.
Gorodnichenko, Yuriy, Tho Pham, and Oleksandr Talavera. (2017). Social Network, Sentiment, and Political Outcomes: Evidence from #Brexit. Editorial Express.
Haahr, L. (2014). Wrestling with Contradictions in Government Social Media Practices. International Journal of Electronic Government Research, 10(1), 35–45. DOI: 10.4018/ijegr.2014010103
Huckle, S. & White, M. (2017). Fake News: A Technological Approach to Proving the Origins of Content, Using Blockchains. Big Data, 5(4),356–371. https://doi.org /10.1089/big.2017.0071
King, G., Pan, J., & Roberts, M.E. (2017). How the Chinese Government Fabricates Social Media Posts for Strategic Distraction, Not Engaged Argument. American Political Science Review, 111(3), 484–501. https://doi.org/10.1017/S0003055417000144.
Mainka, A., Hartmann, S., Stock, W. G., & Peters, I. (2014). Government and Social Media: A Case Study of 31 Informational World Cities. 2014 47th Hawaii International Conference on System Sciences. DOI: 10.1109/hicss.2014.219
Prior, M. (2013). Media and political polarization. Annual Review of Political Science, 16, 101- 127.
Zhang, H., & Xiao, J. (2017). Assimilation of social media in local government: an examination of key drivers. The Electronic Library, 35(3), 427–444. DOI: 10.1108/el-09-2016-01