The impact of the filter bubble in social media: A case of facebook

Content filter algorithms used in social media shape the user experience by assuming the gatekeeper role in the traditional media. The filtering algorithms constitute a personalized information ecosystem by providing personalized content to the social media users. This ecosystem is criticized since it is turned into a structure harboring negative impacts called filter bubble. This study aims at determining the impact of the issue of the "filter bubble" on the participants, which occurs due to Facebook's personalized news feed. In the study designed with the quantitative method and general scanning model, the data were obtained from 400 participants through online questionnaires. The frequency and percent values of the data were calculated, and chi-square was analyzed. The present study empirically reveals that the perception of the filter bubble is determined by a number of variables (such as age, training level, etc.) within the context of Facebook news feed. This study has concluded that the participants perceive the personalized news feed of Facebook in terms of the filter bubble. In conclusion, the personalized news feed of Facebook has not been considered useful because the participants have a doubt about the data collection methods for personalisation (for instance being alert because of the insecurity). It has been further concluded that demographic characteristics (such as age, training, etc.) might affect the perception of the filter bubble. While there has been no direct impact of different occupational groups on the perception of the filter bubble, it has been observed that training level has a significant effect on the perception of the filter bubble. Participants with lower training level have been statistically observed to be less aware of the filtering impacts. In order to generalize the findings gathered in this study, further research needs to be conducted to scrutinize the impacts on the perception of the filter bubble in different settings (such as search engines).