1. Forecasting Audience Increase on YouTube Matthew Rowe Knowledge Media Institute, The Open University, Milton Keynes, United Kingdom

2. Reputation on the Social Web Reputation is: “the beliefs or opinions that are generally held about someone or something” On the Social Web, reputation = greater influence Important to information flow Control information diffusion How to quantify reputation? Greater audience = greater reputation Greater reputation = greater influence How to measure ‘reputation’? In-degree – i.e. number of ‘in links’ Audience levels, subscriber counts Forecasting Audience Increase on YouTube 1

3. Influential Social Nodes Forecasting Audience Increase on YouTube 2

4. Why Forecast? Users want to expand their audience What can users do to increase their audience? What factors contribute to increases? Solution: explore the relation between Audience levels - i.e. in-degree, and; Behaviour – of user and content Discover patterns, then use patterns for forecasting Given my behaviour, will my audience grow? 3 Forecasting Audience Increase on YouTube

5. Features User behaviour statistics In-degree – i.e. number of followers Out-degree – i.e. number follows User view count – number of posts viewed by the user Post count – number of posts uploaded by the user Content statistics Post view count – i.e. number of views Favourite count – i.e. number of likes of content 4 Forecasting Audience Increase on YouTube

6. Schema Barrier Social Web platforms provide data using bespoke schemas i.e. communicating through different languages Data from platform A == data from platform B Schema from platform A != schema from platform B Models must function across platforms Enabling portable behaviour patterns How can we interpret data from different platforms? 5 Forecasting Audience Increase on YouTube

7. Behaviour Ontology Solution: OU Behaviour Ontology Defines behaviour in a common format Extending the SIOC ontology Captures ‘impact’ Vital to capture time-stamped user statistics Two classes for impact User impact Models user features Post impact Models post statistics 6 www.purl.org/NET/oubo/0.23/ Forecasting Audience Increase on YouTube

8. Data Collection: YouTube Gathered a dataset from the video-sharing platform YouTube One aim of usage is to increase ‘channel’ popularity Gain more subscriptions For 10 days, at 4 hour intervals: Logged 100 most recently uploaded videos Stopping once 2k were logged Logged user + content stats for each video Randomly chose 10% for analysis Split dataset into 80/20 for training/testing 7 Forecasting Audience Increase on YouTube

9. Forecasting Audience Increase How can we predict audience levels given observed features? 8 Forecasting Audience Increase on YouTube

10. Forecasting Audience Increase How can we predict audience levels given observed features? 9 Error/residual vector Coefficient/weight Predictor/independent variable Forecasting Audience Increase on YouTube

11. Forecasting Audience Increase How can we predict audience levels given observed features? What features are good predictors? i.e. can we induce a better model than above? Perform model selection 10 Error/residual vector Coefficient/weight Predictor/independent variable Forecasting Audience Increase on YouTube

12. Model Selection I To perform model selection: Aim: maximise the coefficient of determination Procedure: average features within the training split in the same time period 11 Forecasting Audience Increase on YouTube

13. Model Selection I To perform model selection: Aim: maximise the coefficient of determination Procedure: average features within the training split in the same time period First Model: all features 12 Forecasting Audience Increase on YouTube

14. Model Selection I To perform model selection: Aim: maximise the coefficient of determination Procedure: average features within the training split in the same time period First Model: all features 13 Forecasting Audience Increase on YouTube

15. Model Selection II How can we improve upon the previous model? Feature selection Exhaustive search of all possible feature combinations Optimize coefficient of determination 14 Forecasting Audience Increase on YouTube

16. Model Selection II How can we improve upon the previous model? Feature selection Exhaustive search of all possible feature combinations Optimize coefficient of determination Shows improvements using certain models 15 Forecasting Audience Increase on YouTube

17. Model Selection III Exhaustive feature selection drops user view count Forecasting Audience Increase on YouTube 16

18. Model Selection III Exhaustive feature selection drops user view count Forecasting Audience Increase on YouTube 17

19. Forecasting I Now have 2 models to forecast with: All features Best features Which model is best? Two experiments to test predictive power: One-step forecast Train model on previous k-steps, predict k+1 Final-step forecast Predict t=10, train on previous k-steps Predictions are user dependent Evaluation measure: Root Mean Square Error Forecasting Audience Increase on YouTube 18

20. Forecasting: Results One-step Final Step Forecasting Audience Increase on YouTube 19

21. Conclusions and Future Work Quantified reputation by audience levels Content reception linked to increased levels: More content views = increased audience levels More favourites = increased audience levels Able to accurately predict audience levels Post feature selection improves performance Behaviour ontology captures required features Common conceptualisation of behaviour Future work: Extend analysis to a larger dataset Applying models to additional platforms Forecasting Audience Increase on YouTube 20

22. Questions Questions? people.kmi.open.ac.uk/rowe m.c.rowe@open.ac.uk @mattroweshow 21 Forecasting Audience Increase on YouTube