Research Summary: A Multimodal Framework for Measuring Creator Communication on Short-Form Video

Researchers have introduced a five-stage multimodal framework designed to systematically measure creator communication strategies and thematic focus across short-form video platforms at scale. By combining platform metadata, audio transcripts, and vision-language descriptions of sampled frames with large language models, the methodology enables scalable content analysis while controlling for creator posting volume disparities.

Why it matters

This framework matters because it provides a standardized, scalable approach to decoding complex multimodal signals across contemporary short-form video environments. It allows analytical teams to extract structured intelligence from unstructured video assets without permitting high-volume accounts to distort aggregate findings. Consequently, organizations can achieve more accurate oversight of digital creator ecosystems.

Key insights

• Short-form video presents severe analytical challenges due to the simultaneous presence of speech, visual imagery, and on-screen text.
• The five-stage multimodal pipeline synthesizes platform metadata, speech transcripts, and vision-language frame descriptions.
• Large language models are utilized to generate both predefined indicators and open-ended thematic classifications.
• An audited keyword scheme is implemented to consolidate and standardize open-ended thematic labels.
• A creator-weighted aggregation technique assigns equal weight to individual accounts regardless of posting volume to eliminate skew.
• The methodology demonstrated efficacy on a dataset of 9,852 TikTok videos from 16 creators, retaining 7,311 videos across seven themes and seven communication indicators.

Source

arXiv — Computers and Society — https://arxiv.org/abs/2610.33043