forecasting audience increase on youtube

Forecasting Audience Increase on YouTube

Matthew Rowe

Knowledge Media Institute, The Open University, Milton Keynes, United Kingdom

Forecasting Audience Increase on YouTube 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


Influential Social Nodes

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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?


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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


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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?


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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


www.purl.org/NET/oubo/0.23/

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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


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Forecasting Audience Increase

• How can we predict audience levels given observed features?


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Coefficient/weight

Predictor/independent variable

Error/residual vector

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• What features are good predictors?– i.e. can we induce a better model than above?– Perform model selection


Coefficient/weight

Predictor/independent variable

Error/residual vector

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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


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Model Selection I




• First Model: all features


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Model Selection I




• First Model: all features


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Model Selection II

• How can we improve upon the previous model?

• Feature selection– Exhaustive search of

all possible feature combinations

– Optimize coefficient of determination


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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



Model Selection III

• Exhaustive feature selection drops user view count


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: Results

• One-step

• Final Step


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

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QUESTIONS

Questions?people.kmi.open.ac.uk/[email protected]@mattroweshow


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Technology

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previous model

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