Semiotic Analysis of Bias Language in Online News: The Case of the Indonesian Teacher Marketplace Policy

Abstract

News discourse is often regarded as an objective and formal form of discourse. However, the definition of news is rather elusive. News must be something extraordinary and captivating enough to attract public attention. This research discusses a semiotic analysis of online news, specifically on Beritasatu.com, concerning the new policy for teachers in Indonesia, known as the Teacher Marketplace, proposed by the Minister of Education, Nadiem Makarim. This policy, an online database of teachers that allows schools to immediately choose available teachers, was perceived as an insult to the teaching profession. Consequently, social media platforms such as online news pages, TikTok, Instagram, and others saw online demonstrations suggesting that the government is demeaning teachers by ‘selling’ them in an online database. This research focuses on the headlines, lexical choices, language use, typography, and photography in 15 news articles related to the Teacher Marketplace on Beritasatu.com. Using the interactive model analysis to analyze the data, the results revealed 15 harsh headlines, 27 harsh lexical choices, 18 instances of unpleasant language, 10 satirical photographs, and standard typography across the online news. These findings highlight the need for news readers to be more aware of harsh language in certain online news platforms.