Optimising audiovisual content for online publication and maximising user engagement. A presentation by Lyndon Nixon at Joint Technical Symposium 2019.

1. retv-project.eu @ReTV_EU /ReTVproject retv-project
Bringing
archives to 21st
century
audiences
JTS 2019 conference
Hilversum, 5 October 2019
Lyndon Nixon, MODUL Technology GmbH
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2. Viewing of linear broadcast TV is
decreasing while time spent with digital
content on Catchup TV, on-demand OTT
or social media rises.
Broadcaster audiences are fragmented
across digital channels and digital channels
are full of competing content offers for
their limited attention.
The TV industry is still catching up with
their online competition in the use of Web
technology: user tracking, personalisation
and targeting.

3. retv-project.eu @ReTV_EU /ReTVproject retv-project
Archive content needs to be re-born for the 21st century
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Zenith Media, „Media Consumption
Forecasts 2018“, May 2018.
https://www.thinkwithgoogle.com/data/millennial-tv-
consumption-statistics/ Jan 2018

4. retv-project.eu @ReTV_EU /ReTVproject retv-project
A Trans-Vector Platform for cross-channel content
analysis and publication
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5. retv-project.eu @ReTV_EU /ReTVproject retv-project
Metadata aggregated from
heterogeneous sources & used for
data-driven services:
- topic prediction (SELECT)
- content repurposing (OPTIMISE)
- content recommendation
(PUBLISH)
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SELECT
OPTIMISE
PUBLISH

6. retv-project.eu @ReTV_EU /ReTVproject retv-project
1. Topic prediction
⚫ What will our Audience be
interested in?
⚫ Topical trends
⚫ Future references
⚫ Events
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SELECT
OPTIMISE
PUBLISH
Topic Compass (scheduling)
Visualises popularity, polarity and
communication success of topics
on different vectors.
?
It will predict future
popularity, polarity
and communication
success of a topic.

7. retv-project.eu @ReTV_EU /ReTVproject retv-project
1. Topic prediction
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8. retv-project.eu @ReTV_EU /ReTVproject retv-project
1. Topic prediction
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9. retv-project.eu @ReTV_EU /ReTVproject retv-project
Predictive analytics
Data sources for prediction:
⚫ Textual annotation (keyword
extraction, NER)
⚫ Temporal annotation (absolute
and relative refs)
⚫ Event extraction (WikiData,
iCal)
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10. retv-project.eu @ReTV_EU /ReTVproject retv-project
2. Content re-purposing
⚫ What content in which form
achieves optimal attention?
⚫ Topical focus
⚫ Summarization
⚫ Storytelling
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SELECT
OPTIMISE
PUBLISH
Content Wizard (repurposing)
Selects video clips for combination
and re-publication, with
recommendations for the vector
and time, and adaptations of
content to that vector.
Create content
summaries based
on (a) channel (e.g.
video duration
limits), (b) topics of
interest and (c)
purpose.

11. retv-project.eu @ReTV_EU /ReTVproject retv-project
2. Content re-purposing
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12. retv-project.eu @ReTV_EU /ReTVproject retv-project
2. Content re-purposing
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13. retv-project.eu @ReTV_EU /ReTVproject retv-project
Video understanding
⚫ Fragmentation
⚫ Scene
⚫ Shot
⚫ Sub-shot
⚫ Labeling with visual concepts
⚫ Training sets (TRECVID-323, ConceptNet)
⚫ Self-defined (e.g. Sandmännchen)
⚫ Brand and channel logo detection
⚫ Identify spatial regions with a brand or channel logo
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14. retv-project.eu @ReTV_EU /ReTVproject retv-project
Video re-purposing
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1. Video length restrictions (e.g. social media)
2. According to topic(s) (predicted to optimise success)
3. Guided by purpose (e.g. trailer to promote future content, highlights of past content)
The SUM-GAN model
•Idea: learn keyframe selection by minimizing the distance between
the deep feature representations of the original video and a
reconstructed version
•Problem: how to define a good distance?
•Solution: train a discriminator network (GAN)!
•Goal: train Summarizer to maximally confuse the discriminator when
distinguishing the original from the reconstructed video.

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3. Content recommendation
⚫ When to publish the content and on
what vector?
⚫ Audience segmentation
⚫ Optimal vector (reach)
⚫ Optimal time (engagement)
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SELECT
OPTIMISE
PUBLISH

16. retv-project.eu @ReTV_EU /ReTVproject retv-project
3. Content recommendation
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17. retv-project.eu @ReTV_EU /ReTVproject retv-project
Approaches to recommendation
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1. Publish on a future date -> which content will have optimal success?
2. Publish selected content -> when and on what channel will it have optimal success?
3. Promote content to consumers -> which content are they most likely to watch?
1. Introduce consumers to content they wouldn‘t have otherwise watched
2. Keep consumers engaged with the content when they would otherwise not be

18. The ReTV Stakeholder Forum is
your opportunity to engage with
us, be first to get updates and
test the services and tools!
Send a mail to:
Demos of all scenarios are
available to try out today!
Deliverables and other project
results are published at
https://www.retv-project.eu
info@retv-project.eu

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Dr Lyndon Nixon
nixon@modultech.eu
MODUL Technology GmbH
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This project has received
funding from the European
Union’s Horizon 2020 research
and innovation programme