Multimodal Analysis of State-Funded News Coverage of the Israel-Hamas War on YouTube Shorts
2026-04-02T08:52:18Z•92e9fda9d90c1a4b2ae4dd2922d99d28c750f3c863b5a9b24ebd0d87f2bcf349
ABSAInstagramIsrael-Hamas warLLM comparisonTikTokYouTube Shortsalgorithmic environmentsaspect-based sentiment analysisautomatic transcriptiondomain-adapted modelsmedia studiesmultimodal analysisresource-efficient modelssemantic scene classificationsentiment analysisshort-form videospeech-to-textstate-funded mediavisual forensics
What happened
Two arXiv papers: (1) Presents a multimodal pipeline for analyzing state-funded news coverage of the Israel–Hamas war on YouTube Shorts combining automatic transcription, aspect-based sentiment analysis (ABSA), and semantic scene classification. Applied to >2,300 conflict-related Shorts and ~94,000 visual frames, the study finds transcript sentiment about specific aspects varies by outlet and over time, scene classifications reflect real-world visual cues, and smaller domain-adapted models outperform large transformers/LLMs for sentiment tasks—highlighting efficient models for humanities/media
Why it matters
A reviewed impact interpretation has not been published for this record.
Evidence and limitations
- Source ID
- arxiv_cs_si
- Record identifier
- 92e9fda9d90c1a4b2ae4dd2922d99d28c750f3c863b5a9b24ebd0d87f2bcf349
- Enrichment time
- 2026-04-02T08:52:18Z
- AI-assisted enrichment
- Yes
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