Computer Science > Social and Information Networks
[Submitted on 7 Oct 2025 (v1), last revised 25 Feb 2026 (this version, v3)]
Title:Media Coverage of War Victims: Journalistic Biases in Reporting on Israel and Gaza
View PDF HTML (experimental)Abstract:October 7, 2023 marked the start of a war against Gaza, one of the most devastating conflicts in modern history, which quickly produced a stark global attitudinal divide. To examine the role of media bias in shaping public understanding of this asymmetrical war, we analyzed more than 14,000 news articles published during its first year across three major Western outlets (The New York Times, BBC, CNN) and one non-Western English-language outlet (Al Jazeera English). Focusing on media narratives surrounding Israeli and Palestinian victims, we identify three systematic biases in Western coverage: (1) Identifiable Victim Reporting: Israeli victims were substantially more likely to be depicted as identifiable individuals, whereas Palestinian victims were predominantly represented as undifferentiated collectives. (2) Equalization Bias: Despite the profound asymmetry in casualties, displacement, and other forms of suffering, Western reporting repeatedly invoked the October 7 attacks to equalize Israeli and Palestinian victimhood, even in the absence of new Israeli-casualty events. (3) One-sided Doubt Casting: Journalists disproportionately used language that casts doubt on the credibility of casualty figures and the reliability of sources when reporting Palestinian (vs. Israeli) victim counts, selectively undermining trust in information about Palestinian suffering. Across all three phenomena, these patterns were either absent or greatly attenuated in Al Jazeera English. Taken together, our analysis uncovers a coherent set of systematic biases in high-profile Western media coverage of the Gaza war, with implications for how global audiences come to understand and morally evaluate the conflict.
Submission history
From: Bedoor AlShebli [view email][v1] Tue, 7 Oct 2025 20:43:53 UTC (6,256 KB)
[v2] Thu, 30 Oct 2025 16:23:19 UTC (6,260 KB)
[v3] Wed, 25 Feb 2026 21:40:37 UTC (5,551 KB)
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