HttpRequest Automate
Automated workflow for HttpRequest that extracts engaging moments from YouTube videos based on intensity scores. It processes video data to identify timestamps with high viewer engagement, filtering out close moments for clarity. Users receive structured, human-readable links to these moments, enhancing content discovery and viewer experience.
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Workflow Overview
Automated workflow for HttpRequest that extracts engaging moments from YouTube videos based on intensity scores. It processes video data to identify timestamps with high viewer engagement, filtering out close moments for clarity. Users receive structured, human-readable links to these moments, enhancing content discovery and viewer experience.
This workflow is ideal for:
- Content Creators: YouTubers looking to identify and highlight engaging moments in their videos to boost viewer retention.
- Marketers: Professionals aiming to analyze video performance and extract key moments for promotional content.
- Educators: Teachers and trainers who want to create engaging learning materials by pinpointing impactful moments in educational videos.
- Developers: Those interested in integrating video analysis features into applications or platforms using webhooks.
This workflow addresses the challenge of extracting engaging moments from YouTube videos based on viewer interaction intensity. It automates the process of identifying timestamps in videos that are likely to hold viewers' attention, which can be particularly useful for content optimization and audience engagement strategies.
- Webhook Trigger: The workflow starts with a webhook that listens for incoming requests with a YouTube video ID.
- Input Variables: The video ID is captured and assigned for further processing.
- HTTP Request: A request is sent to the YouTube API to retrieve data about the specified video, specifically looking for the most replayed segments.
- Conditional Check: The workflow checks if there is intensity data available. If not, it responds with a message indicating no results.
- Split Out: If intensity data exists, it splits the data to analyze individual markers of interest.
- Filtering: The workflow filters out moments with intensity scores greater than 0.6, ensuring only the most engaging segments are considered.
- Time Conversion: Milliseconds are converted to seconds for easier interpretation.
- Further Filtering: Close moments are filtered out to avoid redundancy, ensuring a clean list of engaging moments.
- Human Readable Format: Each moment is formatted into a user-friendly message with a direct link to the timestamp in the YouTube video.
- Aggregation: All engaging moments are aggregated into a single response object.
- Response Preparation: Finally, the workflow prepares to respond with either the engaging moments or a message indicating no results were found.