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ReTV: Bringing Broadcaster Archives to the 21st-century Audiences

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

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ReTV: Bringing Broadcaster Archives to the 21st-century Audiences

  1. 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 1
  2. 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. 3. retv-project.eu @ReTV_EU /ReTVproject retv-project Archive content needs to be re-born for the 21st century 3 Zenith Media, „Media Consumption Forecasts 2018“, May 2018. https://www.thinkwithgoogle.com/data/millennial-tv- consumption-statistics/ Jan 2018
  4. 4. retv-project.eu @ReTV_EU /ReTVproject retv-project A Trans-Vector Platform for cross-channel content analysis and publication 4
  5. 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) 5 SELECT OPTIMISE PUBLISH
  6. 6. retv-project.eu @ReTV_EU /ReTVproject retv-project 1. Topic prediction ⚫ What will our Audience be interested in? ⚫ Topical trends ⚫ Future references ⚫ Events 6 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. 7. retv-project.eu @ReTV_EU /ReTVproject retv-project 1. Topic prediction 7
  8. 8. retv-project.eu @ReTV_EU /ReTVproject retv-project 1. Topic prediction 8
  9. 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) 9
  10. 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 10 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. 11. retv-project.eu @ReTV_EU /ReTVproject retv-project 2. Content re-purposing 11
  12. 12. retv-project.eu @ReTV_EU /ReTVproject retv-project 2. Content re-purposing 12
  13. 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 13
  14. 14. retv-project.eu @ReTV_EU /ReTVproject retv-project Video re-purposing 14 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.
  15. 15. retv-project.eu @ReTV_EU /ReTVproject retv-project 3. Content recommendation ⚫ When to publish the content and on what vector? ⚫ Audience segmentation ⚫ Optimal vector (reach) ⚫ Optimal time (engagement) 15 SELECT OPTIMISE PUBLISH
  16. 16. retv-project.eu @ReTV_EU /ReTVproject retv-project 3. Content recommendation 16
  17. 17. retv-project.eu @ReTV_EU /ReTVproject retv-project Approaches to 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. 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
  19. 19. retv-project.eu @ReTV_EU /ReTVproject retv-project Dr Lyndon Nixon nixon@modultech.eu MODUL Technology GmbH 19 This project has received funding from the European Union’s Horizon 2020 research and innovation programme

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