A Social Network Analysis of Indonesian Online Conversations on the Palestine Conflict
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DOI:
https://doi.org/10.33102/jcicom.vol5no2.132Keywords:
Social Network Analysis, Sentiment Analysis, Thematic Analysis, Palestine, Indonesia, Social Media ConversationAbstract
The Palestine conflict remains one of the most intensively discussed humanitarian issues in the digital sphere, particularly across Indonesian social media where public solidarity with Palestine is consistently strong. This study examines how Indonesian netizens engaged with the issue during June–July 2025 by combining Social Network Analysis (SNA), sentiment analysis, and thematic analysis. The research focuses on mapping network structures, identifying dominant narratives, and understanding emotional patterns that shaped public discourse. Data were collected through Brand24, which provides estimates of online mentions and digital reach across major social media platforms; however, these figures should be interpreted as approximations rather than exact measurements. A major spike in online attention occurred following the Israeli airstrike on July 3, 2025, which killed the head of the Indonesian Hospital in Gaza. This event intensified online discussions and elevated humanitarian frames, although the overall sentiment remained mixed, with neutral posts forming the majority. Network analysis indicates that conversations were centralized around mainstream media accounts, while thematic analysis reveals recurring topics such as Gaza attacks, broader Middle East tensions, Indonesian media responses, humanitarian concerns, and diplomatic relations. Emotional reactions were dominated by sadness, reflecting a surge of empathic engagement, while peace-oriented narratives appeared marginal. This study demonstrates how Indonesian social media users rapidly mobilize around humanitarian crises and how online media amplify emotional and agenda-setting dynamics. Limitations include reliance on platform-generated analytics and the short observation period, which constrain the generalizability of the findings.
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