Forecasting Audience Increase on Youtube

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  • Forecasting Audience Increase on Youtube

    1. 1. Forecasting Audience Increase on YouTube<br />Matthew Rowe<br />Knowledge Media Institute, The Open University, Milton Keynes, United Kingdom<br />
    2. 2. Reputation on the Social Web<br />Reputation is:<br />“the beliefs or opinions that are generally held about someone or something”<br />On the Social Web, reputation = greater influence<br />Important to information flow<br />Control information diffusion<br />How to quantify reputation?<br />Greater audience = greater reputation<br />Greater reputation = greater influence<br />How to measure ‘reputation’?<br />In-degree – i.e. number of ‘in links’<br />Audience levels, subscriber counts<br />Forecasting Audience Increase on YouTube<br />1<br />
    3. 3. Influential Social Nodes<br />Forecasting Audience Increase on YouTube<br />2<br />
    4. 4. Why Forecast?<br />Users want to expand their audience<br />What can users do to increase their audience?<br />What factors contribute to increases?<br />Solution: explore the relation between<br />Audience levels - i.e. in-degree, and;<br />Behaviour – of user and content<br />Discover patterns, then use patterns for forecasting<br />Given my behaviour, will my audience grow?<br />3<br />Forecasting Audience Increase on YouTube<br />
    5. 5. Features<br />User behaviour statistics<br />In-degree – i.e. number of followers<br />Out-degree – i.e. number follows<br />User view count – number of posts viewed by the user<br />Post count – number of posts uploaded by the user<br />Content statistics<br />Post view count – i.e. number of views<br />Favourite count – i.e. number of likes of content<br />4<br />Forecasting Audience Increase on YouTube<br />
    6. 6. Schema Barrier<br />Social Web platforms provide data using bespoke schemas<br />i.e. communicating through different languages<br />Data from platform A == data from platform B<br />Schema from platform A != schema from platform B<br />Models must function across platforms<br />Enabling portable behaviour patterns<br />How can we interpret data from different platforms? <br />5<br />Forecasting Audience Increase on YouTube<br />
    7. 7. Behaviour Ontology<br />Solution: OU Behaviour Ontology<br />Defines behaviour in a common format<br />Extending the SIOC ontology<br />Captures ‘impact’<br />Vital to capture time-stamped user statistics<br />Two classes for impact<br />User impact<br />Models user features<br />Post impact<br />Models post statistics<br />6<br />www.purl.org/NET/oubo/0.23/<br />Forecasting Audience Increase on YouTube<br />
    8. 8. Data Collection: YouTube<br />Gathered a dataset from the video-sharing platform YouTube<br />One aim of usage is to increase ‘channel’ popularity<br />Gain more subscriptions<br />For 10 days, at 4 hour intervals:<br />Logged 100 most recently uploaded videos<br />Stopping once 2k were logged<br />Logged user + content stats for each video<br />Randomly chose 10% for analysis<br />Split dataset into 80/20 for training/testing<br />7<br />Forecasting Audience Increase on YouTube<br />
    9. 9. Forecasting Audience Increase<br />How can we predict audience levels given observed features?<br />8<br />Forecasting Audience Increase on YouTube<br />
    10. 10. Forecasting Audience Increase<br />How can we predict audience levels given observed features?<br />9<br />Error/residual vector<br />Coefficient/weight<br />Predictor/independent variable<br />Forecasting Audience Increase on YouTube<br />
    11. 11. Forecasting Audience Increase<br />How can we predict audience levels given observed features?<br />What features are good predictors?<br />i.e. can we induce a better model than above?<br />Perform model selection<br />10<br />Error/residual vector<br />Coefficient/weight<br />Predictor/independent variable<br />Forecasting Audience Increase on YouTube<br />
    12. 12. Model Selection I<br />To perform model selection:<br />Aim: maximise the coefficient of determination<br />Procedure: average features within the training split in the same time period<br />11<br />Forecasting Audience Increase on YouTube<br />
    13. 13. Model Selection I<br />To perform model selection:<br />Aim: maximise the coefficient of determination<br />Procedure: average features within the training split in the same time period<br />First Model: all features<br />12<br />Forecasting Audience Increase on YouTube<br />
    14. 14. Model Selection I<br />To perform model selection:<br />Aim: maximise the coefficient of determination<br />Procedure: average features within the training split in the same time period<br />First Model: all features<br />13<br />Forecasting Audience Increase on YouTube<br />
    15. 15. Model Selection II<br />How can we improve upon the previous model?<br />Feature selection<br />Exhaustive search of all possible feature combinations<br />Optimize coefficient of determination<br />14<br />Forecasting Audience Increase on YouTube<br />
    16. 16. Model Selection II<br />How can we improve upon the previous model?<br />Feature selection<br />Exhaustive search of all possible feature combinations<br />Optimize coefficient of determination<br />Shows improvements using certain models<br />15<br />Forecasting Audience Increase on YouTube<br />
    17. 17. Model Selection III<br />Exhaustive feature selection drops user view count <br />Forecasting Audience Increase on YouTube<br />16<br />
    18. 18. Model Selection III<br />Exhaustive feature selection drops user view count <br />Forecasting Audience Increase on YouTube<br />17<br />
    19. 19. Forecasting I<br />Now have 2 models to forecast with:<br />All features<br />Best features<br />Which model is best?<br />Two experiments to test predictive power:<br />One-step forecast<br />Train model on previous k-steps, predict k+1<br />Final-step forecast<br />Predict t=10, train on previous k-steps<br />Predictions are user dependent<br />Evaluation measure: Root Mean Square Error<br />Forecasting Audience Increase on YouTube<br />18<br />
    20. 20. Forecasting: Results<br />One-step<br />Final Step<br />Forecasting Audience Increase on YouTube<br />19<br />
    21. 21. Conclusions and Future Work<br />Quantified reputation by audience levels<br />Content reception linked to increased levels:<br />More content views = increased audience levels<br />More favourites = increased audience levels<br />Able to accurately predict audience levels<br />Post feature selection improves performance<br />Behaviour ontology captures required features<br />Common conceptualisation of behaviour<br />Future work:<br />Extend analysis to a larger dataset<br />Applying models to additional platforms<br />Forecasting Audience Increase on YouTube<br />20<br />
    22. 22. Questions<br />Questions?<br />people.kmi.open.ac.uk/rowe<br />m.c.rowe@open.ac.uk<br />@mattroweshow<br />21<br />Forecasting Audience Increase on YouTube<br />

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