1.
retv-project.eu @ReTV_EU @ReTVeu retv-project retv_project
Using TV Metadata
to optimise the re-
purposing and re-
publication of TV
Content across
online channels
EBU MDN 2019 workshop
Geneva, 11 June 2019
Lyndon Nixon, MODUL Technology GmbH
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2.
retv-project.eu @ReTV_EU @ReTVeu retv-project retv_project
https://memad.eu/ai4tv2019/
Submissions open until July 8, 2019.
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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: interaction, tracking,
personalisation and targeting.

4.
retv-project.eu @ReTV_EU @ReTVeu retv-project retv_project
TV content needs to be re-born: optimised for each
and every channel
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Global Web Index, „The State of Broadcast TV
in 2019“, Feb 2019.
Zenith Media, „Media Consumption
Forecasts 2018“, May 2018.

5.
retv-project.eu @ReTV_EU @ReTVeu retv-project retv_project
A Trans-Vector Platform for cross-channel content
analysis and publication
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6.
retv-project.eu @ReTV_EU @ReTVeu retv-project retv_project
TV Metadata aggregated from
heterogeneous sources & used
for data-driven services:
- prediction
- repurposing of content
- recommendation
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retv-project.eu @ReTV_EU @ReTVeu retv-project retv_project
TV data collection
⚫ EPG data
⚫ Social media
⚫ YouTube
⚫ Twitter
⚫ Facebook
⚫ Websites
⚫ TV/Radio sites
⚫ Hybrid sites
⚫ Video archives
⚫ Europeana
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retv-project.eu @ReTV_EU @ReTVeu retv-project retv_project
TV data annotation
⚫ Core annotations
⚫ Person
⚫ Organization
⚫ Location
⚫ ReTV specific profiles
⚫ TV channel (reference detection)
⚫ TV actors, characters & presenters
⚫ Named Entity Linking (NEL)
⚫ Local identifiers from our own Semantic Knowledge Base
⚫ Alignment to external Knowledge Graphs, e.g. Wikidata
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9.
retv-project.eu @ReTV_EU @ReTVeu retv-project retv_project
TV data analytics
⚫ Audience metrics
⚫ No of viewers by channel / program / topic
⚫ Content success metrics (by channel)
⚫ Frequency of mention of a topic
⚫ Sentiment towards a topic
⚫ Disagreement around a topic
⚫ WYSDOM
⚫ Content success metrics (by source)
⚫ Reach
⚫ Impact
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10.
retv-project.eu @ReTV_EU @ReTVeu retv-project retv_project
Video annotation
⚫ Fragmentation
⚫ Scene
⚫ Shot
⚫ Sub-shot
⚫ Labeling with visual concepts
⚫ Training sets (TRECVID-323, ConceptNet)
⚫ Self-defined (Sandmännchen)
⚫ Brand and channel logo detection
⚫ Identify spatial regions with a brand or channel logo
⚫ (Dis)appearance of channel logos -> ad break detection
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retv-project.eu @ReTV_EU @ReTVeu retv-project retv_project
Event Knowledge Base
⚫ Available events
⚫ Significant ‚global events‘
⚫ Source: Wikidata by type
⚫ Examples: sports, political, weather events
⚫ Public holidays
⚫ Future recurrences have been calculated
⚫ Country or region validity is available
⚫ Local scheduled events
⚫ Source: iCal
⚫ Examples: DE/AT/CH league football matches, Cup and European competitions
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retv-project.eu @ReTV_EU @ReTVeu retv-project retv_project
Events in predictive analytics
1. We have analysed what types of events affect TV audience patterns
2. We have associated past events with TV audience variations
3. We can use these associations in training a model for predicting future TV audience variations
1. Future events need to be comparable with past events
2. In audience prediction, we will need knowledge about where & when events are broadcast
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13.
retv-project.eu @ReTV_EU @ReTVeu retv-project retv_project
Keyword-based prediction
Weighted list of keywords
returned for a date:
e.g. News/EN documents since
1/1/19 mentioning „October 31“
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e.g. News/EN documents since
1/1/19 mentioning „September 20“
– opening of the Rugby World Cup
but also with e.g. film releases:

14.
retv-project.eu @ReTV_EU @ReTVeu retv-project retv_project
TV audience prediction
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1. Extrapolate time series data (audience metrics)
2. Factor in event knowledge

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retv-project.eu @ReTV_EU @ReTVeu retv-project retv_project
TV content success prediction
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1. Extrapolate time series data (success metrics) for a topic on a vector
2. Factor in event knowledge
3. Predict future success metric for the topic on the vector

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retv-project.eu @ReTV_EU @ReTVeu retv-project retv_project
TV content 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)

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retv-project.eu @ReTV_EU @ReTVeu retv-project retv_project
TV content 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.
retv-project.eu @ReTV_EU @ReTVeu retv-project retv_project
Topics Compass: combine analytics and prediction to
inform a more successful content publication strategy
Topic Compass (scheduling) Visualise topics
which are predicted
to be popular on
future dates
Visualises popularity, polarity and
communication success of topics
on different vectors.
?
It will predict future
popularity, polarity
and communication
success.
Visualise
associations made
in online content
with a target (such
as a TV program)

19.
retv-project.eu @ReTV_EU @ReTVeu retv-project retv_project
Content Wizard: re-purpose and re-publish content for
optimal reach on every channel
Content Wizard (repurposing)
Publish on the right
channel at the right
time for the optimal
predicted reach.
Selects video clips for combination
and re-publication, with
recommendations for the vector
and time, and adaptations of
content to that vector.
Select content for
publication based
on predictions of
popularity of that
content (Topics
Compass).
Create content
summaries based
on (a) channel (e.g.
video duration
limits), (b) topics of
interest and (c)
purpose.

20.
retv-project.eu @ReTV_EU @ReTVeu retv-project retv_project
4u2: a chatbot for recommending trending content

21.
retv-project.eu @ReTV_EU @ReTVeu retv-project retv_project
Content sWitch: dynamic insertion of personalised in-
stream content

22.
retv-project.eu @ReTV_EU @ReTVeu retv-project retv_project
@ReTV_EU Facebook: ReTVeuwww.ReTV-Project.eu Instagram: retv_project
TV isn‘t dead, it‘s reborn.
https://www.thinkwithgoogle.com/data/millennial-tv-
consumption-statistics/ Jan 2018

23.
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:
Prototypes of the scenarios can
be seen here at EBU MDN during
all the demo breaks!
Reports on the scenarios and first
evaluations with users will be
available by end of September
2019
info@retv-project.eu

24.
retv-project.eu @ReTV_EU @ReTVeu retv-project retv_project
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