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Radostina Zhekova

on 23 October 2013

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Transcript of Youtube

Time series
Data set
Conceptual Framework
of the research
Final Sample
20 videos which dynamics were analyzed

- 14 352 comments generated in 30 days

- 2 091 839 views of the total views generated by the 30 videos in 30 days

Initial Sample
videos in 6 categories

Data accumulated on daily basis for a time period of 30 days

Exogenous variables:
VAR analysis
For the purpose of the present study a panel Vector Autoregressive analysis (VAR) will be used.
By Radostina Zhekova
Viral marketing

"unpaid peer-to-peer communication of provocative content originating from an identified sponsor using Internet to persuade audience to pass along..." Porter&Golan (2006)
Word of mouth
WOM has 2 important attributes:
WOM Valence
WOM Volume
Friends network
The WOM valence captures the natures of WOM messages and determines whether they are negative or positive.
In order to control for the user's friends network effect the number of subscribers gained by the video's poster was included in the analysis.

Why viral videos?
Jenna Marbles
WOM influence on viral videos
YouTube statistics
More than 1 billion unique users visit YouTube every month

Over 6 billions of hours of video are watched every month

100 hours are uploaded every minute

Thousands of channels are making 6 figures a year

Millions of subscribers every day
Definitions as:
"...Persuasive messages distributed trough unpaid communication among peers..." Eckler&Rodgers (2010)
WOM volume
WOM valence
The positive valence of WOM has significant positive effect on virality and negative valence has insignificant effect.
The size of the user's friends network has positive effect on virality.
The virality of a video is measured by the number of views that the given video generated on daily basis.

Positive valence
- expressed by the percentage of positive comments a certain video gained per day
Negative valence
- expressed by the percentage of negative comments a certain video gained per day
The WOM volume is used as a measure for the total amount of interactions and in the present study it is defined by the number of comments per video per day.
Video's length
Days since release
The study focused only on the data generated in the first 2 weeks

-14 441 comments, which are 89% of the comments generated for the whole period

- represents 65% of the views generated during the whole period
Flexible model for performing an analysis of multivariate time series

Very useful for describing the dynamic behavior of economic and financial time series and for forecasting

Suitable for the study because it is aim of the study is to capture the dynamic structure of the variables

VAR is able to capture linear relationship among multiple time series
Unit Root Test
Augmented Dickey-Filler Test (ADF) featured in the analysis which included only intercept and employed automatic lag length selection.
Results :
- All the variables evolve as stationary process
Granger causality test
Examines how much of the current y can be explained by past values of y and then check whether adding lagged values of x can improve the explanation
Endogenous variables:
Virality, WOM volume, WOM valence, Number of subscribers
Exogenous variables:
Video's category, Video's length, Days since video's release
Panel VAR specifications and estimation
Lag length determination - 7 lags
Dependent variable - Virality
Independent variables - WOM volume, WOM Valence, Number of subscribers, Category, Video's length and Days since release
Thank You!
WOM Volume has significant positive effect on virality in short term perspective.
(Beta1= 114.39, p<0.05 and Beta2=85.38, p<0.09)
Insignificant effect of WOM volume around the 3rd and the 4th period, but in longer perspective the responses of views on comments are positive and permanent
WOM Valence has inconsistent results
Both negative and positive WOM have negative effect on virality, but the results are insignificant in long term perspective
The WOM volume has a significant positive effect on virality, while WOM valence has insignificant.
Negative WOM could be determined to be more positively related to the video's virality than the positive in long term perspective
User's friends network seems not to be an important factor for a video to become viral as the subscribers turned to be statistically insignificant.
It seems that the WOM volume and neutral comments drive virality.
The number of views affects the accumulation of new subscribers more significantly => the more viral a video gets, the bigger is the chance for a new user to subsctibe
Stimulating discussion in YouTube should have stronger impact on the video's popularity.
The more viewed videos have longer "hot" period of intensive discussion
Full transcript