Video sharing platforms are one of the most popular and engaging platforms on the Internet today. Despite the increasing levels of user activity on these video platforms, current research on digital platforms have largely focused on social media and networking websites like Facebook and Twitter. We depart from previous work that have focused primarily on user demands (i.e. activity of viewers), and instead focus our attention to the supply-side activities on the platform (i.e. activity of video uploaders). We perform a large-scale empirical study by leveraging longitudinal video upload data from a major online video platform, demonstrating (i) heterogeneity of video types (e.g. presence of popular vs. niche genres), and (ii) inherent seasonality effects associated with video uploads. Through our analyses, we uncover a set of informative genre-clusters and estimate a self-exciting Hawkes point-process model on each of these clusters, to fully specify and estimate the video upload process. Additionally, we disentangle potential factors that govern user engagement and determine the video upload rates, which help supplement our analysis with additional explanatory power. Our results emphasize that using a parsimonious and relatively simple point-process model, we were able to obtain a high model fit, as well as perform prediction of video upload volumes with a higher accuracy than a number of competing models. The findings from this study can benefit platform owners in better understanding how their supply-side users engage with their site over time. We also offer a robust method for performing media upload prediction that is likely to be generalizable across media platforms which demonstrate similar temporal and genre-level heterogeneity.

1. Predicting Supply-side Engagement on
Video Sharing Platforms
Rishabh Mehrotra, Prasanta Bhattacharya
University College London
National University of Singapore
3rd October 2017

2. Video Sharing Platforms
• Exponential growth of video sharing & delivery sites:
• Netflix, 9Gag.TV, YouTube are one of the most visited sites on internet
• Mobile video traffic accounted for 60 percent of total mobile data traffic in 2016
• Approx 4.4 million TB per month
• Will increase 9-fold and account for over 3/4th of mobile traffic (78%) by 2021
• Heavy user engagement
• Number of digital video users in US will reach 232M
• Higher number of hours spent per user,
with more engagement

3. User Engagement is Important!
• User Engagement: “emotional, cognitive and behavioral connection
that exists between a user and a resource”
• Most users engage as content consumers
• … sustained by a steady upload of videos
• from a relatively smaller proportion of video uploaders (content producers)
Content Producers Content ConsumersMarketplace

4. Prior Art
• Most academic research focus on content consumers:
• Interests, user modelling, personalization
• Next query prediction, recommendations etc
• Network-oriented sites - Social networks, Messaging
• Video based content research:
• Video downloads
• Consumption patterns
Few work on supply side of content:
• User generated content:
• Textual / pictorial content

5. What’s missing?
Supply side engagement!

6. Current Work
“Modeling the supply side engagement pattern of video uploaders”
Goals:
1. Predict upload rates
• Inherent heterogeneity based on abundance of tags & categories
• Temporal variability: Seasonal trends, Popularity influences
2. Disentangle potential factors which govern uploader’s motivation

7. Data Context
• Longitudinal video upload data
from an adult video-sharing
website
• April 2007 – Feb 2013
• 800,000 videos / 85000
uploaders
• Content arranged in the form of
diverse categories or tags for
ease of access and use

8. Outline
• Motivation
• Part I: Modelling Video Uploads
• Part II: Disentangling Contributing Factors
• Experimental Evaluation

9. Part I: Modelling Video Uploads

10. Part I: Modelling Video Uploads
• Consumption & generation behavior changes based
on category:
• Funny cats – immensely popular
• Astronomy/black holes -- niche interest
•No one size fits all model!

11. Part I: Modelling Video Uploads
• Consumption & generation behavior changes based
on category:
• Funny cats – immensely popular
• Astronomy/black holes -- niche interest
Graph base tag-clusters formation:
• Leverage tag-network to extract genre-clusters
• Genre-clusters = vertex partitions induced by
connected components
• Graph Pruning: drop weak edges
• Find connected components

12. • Obtained 37 genre-clusters
• Cluster stats:
• Avg no of videos: 19460
• Min no of videos: 337
• Max no of videos: 264509
• Significant rate-heterogeneity
depending on the specific video genre
Time
FrequencyofVideoUploads
0 200 400 600 800
051525
Cluster 1
Time
FrequencyofVideoUploads
0 500 1000 1500
0102030
Cluster 6
Time
FrequencyofVideoUploads
0 50 100 150 200 250
261014
Cluster 12
Time
FrequencyofVideoUploads
0 500 1000 1500 2000
0200400
Cluster 33
0"
50000"
100000"
150000"
200000"
250000"
300000"
Cluster"ID" No."of"videos" No."of"views"
Analysis of Genre-Clusters

13. Part I: Modelling Video Uploads

14. Cluster specific rate-heterogeneity warrants the need to perform
predictive modeling on each of the clusters
Part I: Modelling Video Uploads

15. Cluster specific rate-heterogeneity warrants the need to perform
predictive modeling on each of the clusters
Part I: Modelling Video Uploads
… Hawkes Process

16. Multivariate Hawkes Process
• Real world interactions often
exhibit the self-exciting and
mutually-exciting property
• Point process with conditional
intensity
• Background intensity
• Correlation between events A & B
• Linear self-exciting process:
Mehrotra et al.; Modeling the Evolution of User-generated Content
on a Large Video Sharing Platform; WWW 2015

17. Multivariate Hawkes Process
• Real world interactions often
exhibit the self-exciting and
mutually-exciting property
• Point process with conditional
intensity
• Background intensity
• Correlation between events A & B
• Linear self-exciting process:
Mehrotra et al.; Modeling the Evolution of User-generated Content
on a Large Video Sharing Platform; WWW 2015

18. • One Hawkes model for each genre cluster
• Each video upload in a genre cluster treated as an event
• Intensity function: rate at which video uploads in a cluster occur
• Parameter estimation: MLE estimation of following log-likelihood
function
Hawkes Process Model for Video Uploads

19. Outline
• Motivation
• Part I: Modelling Video Uploads
• Part II: Disentangling Contributing Factors
• Experimental Evaluation

20. Part II: Disentangling contributing factors

21. Why do users upload content?
• Different factors trigger users to contribute new media
• We describe three primary mechanisms to describe the video upload
process:
• self-reinforcing behavior of users
• trend-burst influence, also called here as the ”popularity effect,”
• other exogenous factors

22. • Hawkes model à flexibility to characterize the relationships between
two events
• We can infer the strength of the ties between two events by
examining the intensity function
• a current event can potentially be triggered by any of the historical events.
• Probabilistic measure: j-th event is triggered by the i-th event:
Prerequisite:
Inferring Correlations between events

23. Contributing Factors:
Self-reinforcing Behavior
• Users often exhibit strong predictable behaviors in affinity towards a
particular genre
• Users are more likely to upload in same category again
• conversely, a paucity of uploads strongly predicts fewer uploads in the future
• Self-reinforcing tendency measured using the event correlation
equation:
for all uploads in a cluster, summed over upload events initiated by
the same user
Uploaded by same user?

24. • Popularity of cluster is important
• Perceived popularity of content lends a sense of validation
• Users more likely to upload if they perceive it will be well-received
• Different standards of quantifying popularity per user:
• different notions of baseline popularity thresholds
• Past uploads which are popular than user’s own popularity average
could positively impact the uploader’s decision
Contributing Factors:
Popularity Effect
Is it more popular
than me?

25. • Alternatively, event could be caused by some external (exogenous)
factors
• website streaming quality
• consumer demand
• ease of content creation process
• etc
• Modeling these effects individually à stronger cues and insights
but for the current study, we accumulate them
Contributing Factors:
Exogenous Effects

26. Outline
• Motivation
• Part I: Modelling Video Uploads
• Part II: Disentangling Contributing Factors
• Experimental Evaluation

27. Experimental Evaluation
Baselines:
• Baseline 1: piecewise-constant NHPP (non-
homogenous Poisson process)
• Baseline 2: NHPP with drifting
• Baseline 3: Hawkes process with no clusters
• Baseline 4: ARIMA modelling

28. 1. Model Selection
• Evaluate goodness
of fit
• AIC Score:
• Penalizes models
with higher no of
parameters to
prevent overfit
• Plot: AIC(Hawkes) –
AIC (Poisson)
• Differences in AIC scores are negative for most clusters:
• Hawkes process scored a lower value
• Lower AIC score è better model fit
Takeaway:Hawkes model provides better fit for most clusters!

29. 2. Predicting Video Uploads
• We want to be able to correctly predict the number of uploads in any
genre-cluster
• Perform MLE for each Hawkes model per cluster
• Use parameters to predict no of uploads in a future time frame:
• Number of events between time interval t and t + Δt can be computed using the
counting process as:
• Experiment:
• Data from past 15 days, 20 days, 45 days, 60 days & 90 days
• Predict no of uploads in future window of 2 weeks

30. • Lower the prediction error, better
the model
• Impact of clustering:
• Cluster specific model performs
better
• Benefits over time-series models:
• ARIMA performs worse
• Not generic enough to incorporate
variations
• Hawkes – better model for
temporal variations
2. Predicting Video Uploads
Takeaway: Hawkes model better predicts video upload counts!

31. 3. Impact of Contributing Factors
• Goal: disentangle the effects of various factors contributing to upload
behavior
• Evidence of Self-reinforcing &
popularity effects
• Variations across clusters:
• Strong genre-level dependency
• Few clusters have self-reinforcing behavior more prominent than
popularity effect
Takeaway: Interplay b/w genre-clusters & factors!

32. Thank You!
Rishabh Mehrotra
PhD candidate @ UCL
http://rishabhmehrotra.com
@erishabh
r.mehrotra@cs.ucl.ac.uk
Summary:
- Supply side view of user
engagement
- Upload prediction
- Disentangling motivating factors
Caveats/Future Work:
- Limited set of trigger factors
- Leverage domain insights (Quora upvotes,
Reddit karma, etc)
- Generality to other content domains?
- Reddit, 9Gag.TV, etc
- Marketplace incentivization
- Airbnb, Uber etc
Thanks to ACM SIGIR for the grant!