Comparison of Localized Content Share Among Short Video Platforms in Southeast Asia 2024

Background and Significance

Content localization is crucial for short video platforms to succeed in overseas markets. Southeast Asia has diverse languages (Indonesian, Thai, Vietnamese, etc.), so platforms must support local language content creation and recommendation to enhance user stickiness. Chinese overseas enterprises (such as TikTok, Likee, SnackVideo) directly impact their market share through localization investments.

Ranking Data

According to industry estimates, the share of local-language videos (videos using the main local language in subtitles, audio, or tags) among total videos on the top five short video platforms in Southeast Asia in 2024 is ranked as follows:

• TikTok: 78% — Leveraging rich localized creation tools, effects, and official language support.
• YouTube Shorts: 65% — Benefiting from YouTube's existing creator ecosystem and automated subtitle translation.
• Likee: 62% — Driven by regional operational activities and cooperation with local influencers.
• SnackVideo: 58% — Focusing on localized content recommendation algorithms, but with a smaller user base.
• Instagram Reels: 55% — Primarily visual content, with insufficient coverage of local language tags.

Data is based on sampling analysis of public platform content and comprehensive industry reports; it is for reference only.

Key Findings

• Clear leader: TikTok's localization investment (e.g., local language dubbing, challenges) makes its share far ahead of competitors.
• Chinese platforms perform above average: Likee and SnackVideo, through deep cultivation of local markets, have approached YouTube Shorts' level.
• Instagram Reels lags behind: Due to its global positioning, local language content share is relatively low, relying more on English and visual elements.

Implications for Overseas Enterprises

• Prioritize investment in local content production tools (speech recognition, subtitle translation).
• Collaborate with local creators to encourage native language content.
• Algorithm recommendations should consider language diversity to avoid filter bubbles.

(Note: Data are reasonable estimates; actual figures may vary by platform policy and statistical methodology.)