Language Bias on Tweets among Different Groups over Israel-Iran Conflict

Abstract

This study knows perception of conflict by analyzing statement of the tweets and its comments over Israel-Iran conflict based on Maass et al. (1989) framework, the Language Intergroup Bias (LIB) theory which focuses on how language use in intergroup interactions can unintentionally reinforce or build perceptions about a group, and the theory Language Category Model (LCM) by Semin and Fiedler (1988, 1991, 1992) that provides an implicit attribute that is perceived by the language that is used to describe and analyze case studies in the context of Iran-Israel conflict. Using a qualitative method, data was collected from the Twitter platform by searching the hashtag #IranIsraelConflict. In total of 10 data were analyzed, consisting of 1 tweet with 9 comments. This research aims the understanding of how language choices can affect intergroup perceptions and potentially influence conflict dynamics through social media. The analysis was conducted by identifying the use of language bias, as well as classifying the statements as positive or negative behavior of the ingroup or outgroup, also categorized into four-level language category. The results of the analysis show that abstract language tends to be used to describe negative actions of the outgroup, reinforcing existing biases and shaping public perceptions of the conflict.