The Role of Facebook Access and Partisan Bias on Belief in Misinformation: The Case of 2019 Indonesia Presidential Election

Rizka Halida

doi:10.7454/hubs.asia.1290320

Abstract

This study aims to understand the role of Facebook access and partisan bias on the belief in misinformation in the political context of the 2019 Presidential Election. Frequent use of Facebook and partisan bias for presidential candidates were predicted to influence belief in misinformation about illegal migrant workers from China in Indonesia. Using a structured questionnaire, a total of 1,818 participants who were representative of the Indonesian voter population were interviewed using a structured questionnaire asking about their frequency of Facebook use, political support, awareness, and belief in misinformation about thousands of illegal migrant workers from China, as well as other demographic variables as part of national survey questions. Of these, there were 804 participants who were aware of misinformation about illegal migrant workers from China to be analyzed. The results of binomial logistic regression analysis showed that partisan bias significantly affected belief in misinformation —Subianto's (vs Widodo's) supporters significantly have (vs. have not) a belief in the misinformation, whereas the frequency of Facebook usage and the effect of their interactions were not significant. This finding shows the strength of the influence of political support on belief in misinformation and the need to further study the influence of social media in Indonesia's political context.

Highlights

After the 2016 US Presidential Election, misinformation in the context of electoral politics has attracted research interest (Allcott & Gentzkow, 2017; Lippman, Samuelsohn, & Arnsdorf, 2016)
Studies have found that Facebook was one of the primary sources of fake news; for example, Fourney, Racz, Ranade, Mobius, and Horvitz (2017) found that 68% of page visit to fake news domain was from social media, and of these, 99% referrals were from Facebook (Fourney, et al, 2017)
Statistical analysis of binary logistic regression with the dependent variable is the belief in misinformation carried out in four models: Model (1), analysis to control the demographic variables of age, sex, rural-urban, and education; Model (2), analysis of the frequency of access to political news through Facebook; Model (3), analysis of support for Widodo, and support for Subianto; Model (4), an analysis of the interactions of each between the frequency of political news access through Facebook with support for Widodo and support for Subianto

Summary

Introduction

After the 2016 US Presidential Election, misinformation in the context of electoral politics has attracted research interest (Allcott & Gentzkow, 2017; Lippman, Samuelsohn, & Arnsdorf, 2016). Studies have found that Facebook was one of the primary sources of fake news; for example, Fourney, Racz, Ranade, Mobius, and Horvitz (2017) found that 68% of page visit to fake news domain was from social media, and of these, 99% referrals were from Facebook (Fourney, et al, 2017). This raises new concerns about the influence of social media (e.g., Facebook) on the political process and democracy. Because of the personalization and homogeneity of the environment it provided to the users, the social media was considered as the source of information bias, increasing polarization and enhancing people's belief in misinformation that were previously received

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