The Influence of the Social Media Recommendation System Against Polarization in the Views of User Politics: Experiments on Social Media Newsfeed on Social Media Users

Abstract

The background of this research is based on 2019 election issues that spread massively in social media. From these issues comes the polarization and divides into two opposing sides. However, users are basically not presented with balanced information as a result of the recommendation system on social media. This research is expected to enrich the study of social media impact, especially on study of recommendation system with context in Indonesia. This thesis examines the recommendation system designed by social media operators tends to be uniform or aligned with the user's political views only. The phenomenon is The Filter Bubbles where the information circulating on our social media is filtered only in accordance with the user's own views. Polarization becomes an effect that can be influenced by the recommendation system because the acceptance of information from the user after going through a personalized curation system raises uniform news. This research uses quantitative approach with experiment method. The results of this study indicate that the recommendation system makes the polarization position of users more extreme. This is marked by increasingly persistent opinions and political views held by previous users.

Political Polarization and Selective Exposure of Social Media Users in Indonesia

“…Each faction tends to reject a different perspective, even if it makes sense or is objective. News which agrees with the way of thinking can influence the polarization of the social media users' political views (Rosyadi & Eriyanto, 2019). According to Spohr (2017), the consequence of filter bubbles and selective exposure is the loss of diversity.…”

“…However, the outcomes of studies by Dylko (et. al, 2017) and Rosyadi and Eriyanto (2019) show the influence of the social media algorithm system. According to such two studies, the filter bubble has influence because the system works in a subtle way.…”

A Spectral-Based Model for Describing Social Polarization in Online Communities

“…By using them, we can efficiently access information matching our interests and easily communicate with other users who share common interests [2]. Although the convenience of social media has been enhanced by providing individually optimized information to each user and promoting connections among people through recommendation functions, these features have the potential for causing polarization among users [3]. In many studies, social polarization refers to the division of a social group into two groups.…”