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Work in Progress

Predicting Your Future Audience: Experiments in Picking the Best Topic for Future Content

Published: 17 June 2020 Publication History

Abstract

This work in progress reports on ongoing experimentation with machine learning approaches on time series data, where the time series is a quantification of the success of content about a certain topic published on a certain digital channel over a past time period. The experiment tests how accurate predictive analytical approaches can be to predict the future success of a piece of media content published on the Web or social media platform according to its topics. Our intention is to enable a new innovation in media organizations’ content publication strategies, where the choice of media for a future publication can be informed by such predictive capabilities in order to maximize the potential content's reach to a digital audience.

References

[1]
ReTV Deliverable 1.1 “Data Ingestion, Analysis and Annotation”, available from https://retv-project.eu/deliverables/
[2]
ReTV Deliverable 2.2 “Metrics-based Success Factors and Predictive Analytics”, available from https://retv-project.eu/deliverables/
[3]
Lyndon Nixon, Krzysztof Ciesielski and Basil Philipp. “AI for Audience Prediction and Profiling to Power Innovative TV Content Recommendation Services“, in Proc. 1st Int. Workshop on AI for Smart TV Content Production, Access and Delivery (AI4TV ’19) at ACM Multimedia 2019, October 2019, Nice, France.
[4]
Xiaofeng Gao, Zhenhao Cao, Sha Li, Bin Yao, Guihai Chen, and Shaojie Tang. “Taxonomy and Evaluation for Microblog Popularity Prediction”. In ACM Trans. Knowl. Discov. Data 13, 2, Article 15 (June 2019).
[5]
Lyndon Nixon, Miggi Zwicklbauer, Lizzy Komen and Basil Philipp, “The Trans-Vector Platform for optimised Re-purposing and Re-publication of TV Content”. Proceedings of the DataTV workshop, at ACM TVX 2019, Manchester, UK, June 2019.

Cited By

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  • (2024)AI and data-driven media analysis of TV content for optimised digital content marketingMultimedia Systems10.1007/s00530-023-01195-730:1Online publication date: 19-Jan-2024
  • (2020)Online News Monitoring for Enhanced Reuse of Audiovisual ArchivesDigital Libraries for Open Knowledge10.1007/978-3-030-54956-5_18(243-248)Online publication date: 25-Aug-2020

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Published In

cover image ACM Conferences
IMX '20: Proceedings of the 2020 ACM International Conference on Interactive Media Experiences
June 2020
211 pages
ISBN:9781450379762
DOI:10.1145/3391614
Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 17 June 2020

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Author Tags

  1. Event Extraction
  2. Machine Learning
  3. Media Annotation
  4. Predictive Analytics
  5. TV Recommendation

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  • Work in progress
  • Research
  • Refereed limited

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IMX '20

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Overall Acceptance Rate 69 of 245 submissions, 28%

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IMX '25

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Cited By

View all
  • (2024)AI and data-driven media analysis of TV content for optimised digital content marketingMultimedia Systems10.1007/s00530-023-01195-730:1Online publication date: 19-Jan-2024
  • (2020)Online News Monitoring for Enhanced Reuse of Audiovisual ArchivesDigital Libraries for Open Knowledge10.1007/978-3-030-54956-5_18(243-248)Online publication date: 25-Aug-2020

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