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Architecting 3D content: what video structuring can teach us about the metaverse SEO [an experiment

Architecting 3D content: what video structuring can teach us about the metaverse SEO [an experiment

The metaverse is coming, and it is about time to get ready. Applying computer vision logic to video structure helps with content distribution on YouTube Shorts. This is true if the content is carefully developed by using computer vision principles. More than 90% of the likes and views can come as a result of YouTube search features because their algorithm comprehends video content better with the help of advanced 3D optimization strategy. In this session, Emilija will show you what video structure is teaching us about 3d content & metaverse SEO. So you will be well prepared for what is to come in the near future.

Emilija Gjorgjevska

March 21, 2023
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  1. Architecting 3D content:
    what video structuring can teach us
    about the metaverse SEO [an experiment]
    1

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  2. The experiment:
    swiss raclette servings
    from different angles, in
    different setups w/
    different ingredients,
    incorporated in one video.
    2

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  3. Let’s quickly list the
    top known video ranking factors
    together
    3

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  4. Is your video search strategy resilient?
    4

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  5. Research-backed & business-focused computer science engineer,
    specialising in content engineering & end-to-end SEO
    studied, worked, published in visited
    what shaped me as an SEO
    5

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  6. Worked in entertainment and learned creative approaches from
    award-winning journalists, video editors & producers, famous faces,
    marketing directors since 2010, thanks to Macedonian Idol, Dragi
    Nedelchevski, Igor Tomeski, Samir Ljuma.
    what shaped me as an SEO
    6

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  7. We need more
    open, scientific, actionable research in our industry.
    I practice what I preach and…
    7

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  8. my imperative is that marketing is the
    GENEROUS
    act of helping other people achieve their goals
    and an opportunity to
    SERVE
    8

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  9. Let’s see what Google patents hint
    9

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  10. Excerpt(s)
    Summary: more engagement means better ranking.
    Evidence: “...means for determining a user engagement value for
    the media item based on at least one user shares of the media
    item, user indications of interest in the media item, user comments
    on the media item…”
    11

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  11. Excerpt(s)
    Summary: more engagement means better ranking.
    Evidence: “...A score for a media item is computed by determining
    a plurality of positive user actions associated with the media,
    combining a plurality of score contributions from the plurality of
    positive user actions to determine a value for the
    score, and applying an exponential decay to the value for the
    score. The media items are ranked based on the scores…”
    12

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  12. Excerpt(s)
    Summary: embedding videos can lead to better ranking.
    Evidence: “...A user may thereby be presented with relevant search
    results, ranked by or including content sharing data, for example how
    often the item of content has been shared, and possibly in conjunction
    with other ranking data (internal and/or external reference).”
    14

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  13. What if I
    ignore classic SEO tips + these G-patents
    for video ranking optimization?
    15

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  14. What could be so special about
    a video that has nearly
    700+ views and 20+ likes
    on YouTube until today?
    16

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  15. New idea!
    Cross-functionality:
    apply known computer science techniques to a new type of problem
    17

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  16. Excerpt(s)
    Summary: demonstrated ability to detect and predict 3D shapes.
    Evidence: “...The core novelty of our method is a fast, single-pass
    architecture that both detects objects in 3D and estimates their
    shapes…Thus our model is able to extract shapes without access to
    groundtruth shape information in the target dataset.”
    19

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  17. Search & CTR:
    efforts for displaying products
    in 3D & augmented reality
    in SERPs
    20

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  18. 3D
    synergy
    Search
    Generative
    AI
    Metaverse
    3D as a key
    for immersive
    experiences
    21

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  19. Reinvent your SEO with 3D:
    dissecting everything step by step
    22

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  20. My real video talk - verified by Yandex leak too!
    Lacking: no synonyms for swiss raclette, no tags, no comments, no
    shares, no description, no ad or email campaign, no user signals, no
    social signals, no language settings, no captions, no start and end
    screens, undefined location/geography, no established niche youtube
    channel fan-base (core audience), never had a history of channel
    advertising in any way, no embeds, no schema markup, no backlinks, no
    link text on YouTube, no link depth, URL length and slug were defined by
    Google, my channel & personal region did not overlap with Switzerland,
    no tagged products, not even in a playlist!
    23

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  21. My real video talk
    Implemented: short video (filtered multiple public Instagram Swiss
    Raclette videos combined together), verified host (Google), simple
    filename (raclette.mp4), good objects, quality title (longer one combining
    topical entities = lemmas), no prohibited content, had YT channel with
    some history already in place, was nearby in Germany during video’s
    lifetime.
    Approach: apply computer science knowledge to video.
    24

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  22. Deep dive insights
    25

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  23. Over 87% of the traffic came from content intelligence features!
    26

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  24. Google
    Vision
    API
    +
    Vision
    Intelligence
    API
    27

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  25. Can we deconstruct videos by using computer vision?
    Yes, computer vision technology can be used to deconstruct videos.
    Computer vision algorithms can analyze video frames to extract and
    process visual information, such as object detection, image segmentation,
    optical flow, etc.
    28

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  26. Popular computer vision algorithms for video deconstruction
    1. Object Detection: YOLO, Faster R-CNN, RetinaNet.
    2. Image Segmentation: Mask R-CNN, U-Net, DeepLabv3+.
    3. Optical Flow: Farneback, Lucas-Kanade.
    4. Action Recognition: Two-Stream Convolutional Networks, Temporal
    Segment Networks (TSN), 3D Convolutional Neural Networks
    (3D-CNN).
    5. General Video Analysis: Keyframe Extraction, Video
    Summarization.
    6. Object Tracking: KCF, Deep SORT, GOTURN.
    29

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  27. 1. Object character recognition or extracting written text from video
    (could be books, notes, shops’ names).
    2. Object categorization or organizing objects by their look, shape,
    texture (items, people, animals, stuff).
    3. Automatic speech recognition or what is said during the video.
    4. Audio or other relevant sounds that can help grasp the topic of the
    video to match better (example: water, forest...), also in scene
    understanding.
    5. Sentiment understanding or emotions during the video.
    6. Safe search classification and which color scheme is used.
    7. Even certain movements that people make like the “what’s the time
    gesture”.
    30

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  28. ..a paradox! Assume that
    everything can be detected and
    identified.
    Assume that everything can be
    misinterpreted or inappropriately
    tagged or classified.
    Google has a lot of
    data and engineering
    resources that we
    cannot get or
    implement but we
    have a…
    V/S
    31

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  29. Are your data and story good enough?
    32

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  30. “The experiments we performed are clearly indicating
    that even when we use advanced algorithms like YOLO,
    there’s still a space for the objects to be incorrectly
    labeled in visual environments like videos and metaverse
    spaces (virtual reality and augmented reality platforms).
    Having this in mind, we need to find a more structured
    way of providing 3D information to search engines.”
    - Emilija Gjorgjevska, WordLift’s blog
    https://wordlift.io/blog/en/metaverse-seo/
    3D schema markup
    33

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  31. Apple.
    Seriously?
    34

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  32. 3D schema markup
    35

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  33. Everything matters
    Beware: Sometimes the story behind the video won’t allow optimizing for
    objects etc. in video.
    User signals like comments, likes, subscribers, and so on matter a lot.
    However, the reach to other audiences without a solid basis that focuses
    on how the video is created in the first place is limited.
    36

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  34. Scalable across platforms
    37

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  35. Momentum:
    the creator era of AI-powered canvases
    38

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  36. Connection to the metaverse
    ”You’ll own the things you create, build out and earn in the
    metaverse. Even more importantly, you will be able to monetise it.
    Creators will be highly incentivised to be present and create in this
    space.”
    39

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  37. Objectverse: 3D prototyping
    40

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  38. “We can now do generative AI for images. We can do it for videos. At the
    rate that it’s moving, you’ll do it for entire villages; 3D villages and
    landscapes and cities and so on. You’ll be able to assemble an
    example of an image and generate an entire 3D world.”
    41
    Connection to the metaverse

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  39. Key takeaway
    3D optimization works, even when you ignore
    other video SEO advice and guidelines
    42

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  40. 1. Harder to reverse
    engineer.
    1. Harder to replicate.
    1. Usually overlooked.
    1. Usually lacking strategy.
    43

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  41. We are at the festival of creativity
    living in the most interesting time ever
    44

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  42. Dominik Schwarz
    Andrea Volpini Astrid Kramer
    To the people that lift us!
    Thanks. Me. Emi.
    45

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  43. ...questions?
    *all images are found on the Internet, except the YT
    channel one and are not used for commercial purposes
    46

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