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retv-project.eu @ReTV_EU @ReTVeu retv-project retv_project
Using TV Metadata
to optimise the re-
purposing and re-
publica...
retv-project.eu @ReTV_EU @ReTVeu retv-project retv_project
https://memad.eu/ai4tv2019/
Submissions open until July 8, 2019...
Viewing of linear broadcast TV is
decreasing while time spent with digital
content on Catchup TV, on-demand OTT
or social ...
retv-project.eu @ReTV_EU @ReTVeu retv-project retv_project
TV content needs to be re-born: optimised for each
and every ch...
retv-project.eu @ReTV_EU @ReTVeu retv-project retv_project
A Trans-Vector Platform for cross-channel content
analysis and ...
retv-project.eu @ReTV_EU @ReTVeu retv-project retv_project
TV Metadata aggregated from
heterogeneous sources & used
for da...
retv-project.eu @ReTV_EU @ReTVeu retv-project retv_project
TV data collection
⚫ EPG data
⚫ Social media
⚫ YouTube
⚫ Twitte...
retv-project.eu @ReTV_EU @ReTVeu retv-project retv_project
TV data annotation
⚫ Core annotations
⚫ Person
⚫ Organization
⚫...
retv-project.eu @ReTV_EU @ReTVeu retv-project retv_project
TV data analytics
⚫ Audience metrics
⚫ No of viewers by channel...
retv-project.eu @ReTV_EU @ReTVeu retv-project retv_project
Video annotation
⚫ Fragmentation
⚫ Scene
⚫ Shot
⚫ Sub-shot
⚫ La...
retv-project.eu @ReTV_EU @ReTVeu retv-project retv_project
Event Knowledge Base
⚫ Available events
⚫ Significant ‚global e...
retv-project.eu @ReTV_EU @ReTVeu retv-project retv_project
Events in predictive analytics
1. We have analysed what types o...
retv-project.eu @ReTV_EU @ReTVeu retv-project retv_project
Keyword-based prediction
Weighted list of keywords
returned for...
retv-project.eu @ReTV_EU @ReTVeu retv-project retv_project
TV audience prediction
14
1. Extrapolate time series data (audi...
retv-project.eu @ReTV_EU @ReTVeu retv-project retv_project
TV content success prediction
15
1. Extrapolate time series dat...
retv-project.eu @ReTV_EU @ReTVeu retv-project retv_project
TV content re-purposing
16
1. Video length restrictions (e.g. s...
retv-project.eu @ReTV_EU @ReTVeu retv-project retv_project
TV content recommendation
17
1. Publish on a future date -> whi...
retv-project.eu @ReTV_EU @ReTVeu retv-project retv_project
Topics Compass: combine analytics and prediction to
inform a mo...
retv-project.eu @ReTV_EU @ReTVeu retv-project retv_project
Content Wizard: re-purpose and re-publish content for
optimal r...
retv-project.eu @ReTV_EU @ReTVeu retv-project retv_project
4u2: a chatbot for recommending trending content
retv-project.eu @ReTV_EU @ReTVeu retv-project retv_project
Content sWitch: dynamic insertion of personalised in-
stream co...
retv-project.eu @ReTV_EU @ReTVeu retv-project retv_project
@ReTV_EU Facebook: ReTVeuwww.ReTV-Project.eu Instagram: retv_pr...
The ReTV Stakeholder Forum is
your opportunity to engage with
us, be first to get updates and
test the services and tools!...
retv-project.eu @ReTV_EU @ReTVeu retv-project retv_project
Dr Lyndon Nixon
nixon@modultech.eu
MODUL Technology GmbH
24
Thi...
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Using TV Metadata to optimise the repurposing and republication of TV Content across online channels @ EBU MDN 2019 Slide 1 Using TV Metadata to optimise the repurposing and republication of TV Content across online channels @ EBU MDN 2019 Slide 2 Using TV Metadata to optimise the repurposing and republication of TV Content across online channels @ EBU MDN 2019 Slide 3 Using TV Metadata to optimise the repurposing and republication of TV Content across online channels @ EBU MDN 2019 Slide 4 Using TV Metadata to optimise the repurposing and republication of TV Content across online channels @ EBU MDN 2019 Slide 5 Using TV Metadata to optimise the repurposing and republication of TV Content across online channels @ EBU MDN 2019 Slide 6 Using TV Metadata to optimise the repurposing and republication of TV Content across online channels @ EBU MDN 2019 Slide 7 Using TV Metadata to optimise the repurposing and republication of TV Content across online channels @ EBU MDN 2019 Slide 8 Using TV Metadata to optimise the repurposing and republication of TV Content across online channels @ EBU MDN 2019 Slide 9 Using TV Metadata to optimise the repurposing and republication of TV Content across online channels @ EBU MDN 2019 Slide 10 Using TV Metadata to optimise the repurposing and republication of TV Content across online channels @ EBU MDN 2019 Slide 11 Using TV Metadata to optimise the repurposing and republication of TV Content across online channels @ EBU MDN 2019 Slide 12 Using TV Metadata to optimise the repurposing and republication of TV Content across online channels @ EBU MDN 2019 Slide 13 Using TV Metadata to optimise the repurposing and republication of TV Content across online channels @ EBU MDN 2019 Slide 14 Using TV Metadata to optimise the repurposing and republication of TV Content across online channels @ EBU MDN 2019 Slide 15 Using TV Metadata to optimise the repurposing and republication of TV Content across online channels @ EBU MDN 2019 Slide 16 Using TV Metadata to optimise the repurposing and republication of TV Content across online channels @ EBU MDN 2019 Slide 17 Using TV Metadata to optimise the repurposing and republication of TV Content across online channels @ EBU MDN 2019 Slide 18 Using TV Metadata to optimise the repurposing and republication of TV Content across online channels @ EBU MDN 2019 Slide 19 Using TV Metadata to optimise the repurposing and republication of TV Content across online channels @ EBU MDN 2019 Slide 20 Using TV Metadata to optimise the repurposing and republication of TV Content across online channels @ EBU MDN 2019 Slide 21 Using TV Metadata to optimise the repurposing and republication of TV Content across online channels @ EBU MDN 2019 Slide 22 Using TV Metadata to optimise the repurposing and republication of TV Content across online channels @ EBU MDN 2019 Slide 23 Using TV Metadata to optimise the repurposing and republication of TV Content across online channels @ EBU MDN 2019 Slide 24
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Using TV Metadata to optimise the repurposing and republication of TV Content across online channels. A presentation by Lyndon Nixon about the ReTV project at EBU MDN 2019.

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Using TV Metadata to optimise the repurposing and republication of TV Content across online channels @ EBU MDN 2019

  1. 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 1
  2. 2. retv-project.eu @ReTV_EU @ReTVeu retv-project retv_project https://memad.eu/ai4tv2019/ Submissions open until July 8, 2019. 2
  3. 3. 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. 4. retv-project.eu @ReTV_EU @ReTVeu retv-project retv_project TV content needs to be re-born: optimised for each and every channel 4 Global Web Index, „The State of Broadcast TV in 2019“, Feb 2019. Zenith Media, „Media Consumption Forecasts 2018“, May 2018.
  5. 5. retv-project.eu @ReTV_EU @ReTVeu retv-project retv_project A Trans-Vector Platform for cross-channel content analysis and publication 5
  6. 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 6
  7. 7. 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 7
  8. 8. 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 8
  9. 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 9
  10. 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 10
  11. 11. 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 11
  12. 12. 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 12
  13. 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“ 13 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. 14. retv-project.eu @ReTV_EU @ReTVeu retv-project retv_project TV audience prediction 14 1. Extrapolate time series data (audience metrics) 2. Factor in event knowledge
  15. 15. retv-project.eu @ReTV_EU @ReTVeu retv-project retv_project TV content success prediction 15 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
  16. 16. retv-project.eu @ReTV_EU @ReTVeu retv-project retv_project TV content re-purposing 16 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)
  17. 17. retv-project.eu @ReTV_EU @ReTVeu retv-project retv_project TV content recommendation 17 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. 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. 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. 20. retv-project.eu @ReTV_EU @ReTVeu retv-project retv_project 4u2: a chatbot for recommending trending content
  21. 21. retv-project.eu @ReTV_EU @ReTVeu retv-project retv_project Content sWitch: dynamic insertion of personalised in- stream content
  22. 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. 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. 24. retv-project.eu @ReTV_EU @ReTVeu retv-project retv_project Dr Lyndon Nixon nixon@modultech.eu MODUL Technology GmbH 24 This project has received funding from the European Union’s Horizon 2020 research and innovation programme

Using TV Metadata to optimise the repurposing and republication of TV Content across online channels. A presentation by Lyndon Nixon about the ReTV project at EBU MDN 2019.

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