Vine Measurement Study

ELIS – Multimedia Lab
Baptist Vandersmissen, Fréderic Godin, Abhineshwar Tomar,
Wesley De Neve, Rik Van de Walle
Multimedia Lab
Department of Electronics and Information Systems
Faculty of Engineering and Architecture
Ghent University – iMinds
Belgium
The Rise of Mobile and Social Short-Form Video:
An In-depth Measurement Study of Vine

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Short-form Video on Vine: Overview and Applications
Baptist Vandersmissen
01/04/2014
1. Introduction
2. Vine
3. Data Collection
4. Characteristics & Popularity Aspects
5. Conclusions
6. Future Research Directions
Outline

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01/04/2014
1. Introduction
2. Vine
3. Data Collection
5. Conclusions
Outline

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Introduction
Widespread use of smartphones Popular social video platforms
Expected that by 2017 more than
two-third of mobile data traffic will
be video
Huge amount of video data Internet revenue model based on ads
What content is popular?
Automatic content understanding?

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Introduction
annotated?
noisy?
people?
event-related?
First step is
measurement study
Numerous challenges and ambiguities
Huge amount of video data

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State of the Art
1. Content and Audience Analysis
Answering the who, what and when questions
2. Popularity Analysis and Prediction
Analysis of the popularity distribution regarding videos
3. Social Sensing
Analysis of cultural behaviour, finding points of interests or
movement patterns

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1. Introduction
2. Vine
3. Data Collection
5. Conclusions
Outline

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What is Vine?
Social & mobile video platform, established in January 2013.
Create & distribute looping videos of up to 6 seconds.
Acquired by Twitter and now plus 40 million users.
Max video length resembles Twitter’s character limitation.

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01/04/2014
What is Vine?

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What is Vine?

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What is Vine?

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What is Vine?

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Why Vine?
New and first large scale platform focusing on mobile video
Potential to become social news platform
Strong connection with Twitter
Open nature and easy accessible through URLs in tweets

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1. Introduction
2. Vine
3. Data Collection
5. Conclusions
Outline

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Data Collection
No official Vine API
Use of Twitter as a gateway to access Vine videos
Use of unofficial Vine API methods to extract metadata
Date fetched
Date created
Tweet
Description
Location
Comments
Revines
Likes
Explicit
User
User
Hashtags Mentions
Followers
Following
Posts

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Data Collection
Tracked keyword “vine” on Twitter Streaming API
Collected 851,039 tweets containing a Vine URL
Originating from 365,188 Twitter users
425,971 unique Vine videos fetched
Created by 193,355 different Vine users

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1. Introduction
2. Vine
3. Data Collection
5. Conclusions
Outline

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Metadata Characteristics
Vine videos can have a description
containing #hashtags or @mentions
34% contains at least one hashtag (only 8% on Twitter)
9% contains at least one mention

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Metadata Characteristics
Matching of hashtags with WordNet categories
11% of hashtags can be matched to a WordNet synset

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Channel Characteristics
Vine videos can be added to a channel (i.e., a category)
Most popular categories are
comedy and music
Similar to YouTube’s popular
categories which are
entertainment and music
Channel Count (%)
comedy 60.96%
music 8.93%
wierd 5.27%
dogs 4.46%
cats 3.25%
family 2.74%
art & experimental 2.74%
sports 1.88%
food 1.61%
special fx 1.52%
nature 1.47%
urban 1.41%
scary 1.27%
beauty & fashion 1.09%
news & politics 0.75%
health & fitness 0.65%

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Popularity Characteristics
Emperical analysis of (popular) content
Popularity is measured based on revines, likes, comment count
Popular content is not event- or news-related
However
Many “big” events are described by Vine videos
These can for example be used to enhance textual news articles

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Event-related Example

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Event-related Example

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Popularity Aspects
Analyze Vine video popularity based on content-agnostic factors:
1. Twitter shares: number of tweets sharing same Vine video
2. Twitter exposure: sum of followers of these Twitter users
3. Vine exposure: number of followers of Vine creator

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Popularity Aspects
Hypothesis: high amount of shares on Twitter can be linked to more
popular Vine video
Correlations between factors measured based on Pearson method
Twitter shares Twitter exposure Vine exposure
Likes 0.29 0.07 0.60
Revines 0.32 0.08 0.46
Comments 0.31 0.07 0.39
Low correlation between amount of shares on Twitter and Vine
popularity measures.

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Popularity Aspects
Hypothesis: number of shares on Twitter shortly after the video’s creation
can be linked to a higher video popularity
Figure depicts number of shares on Twitter after one hour and likes
on Vine after one week

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Attention Aspects
Analyze amount of user attention received by a Vine video
Hypothesis: attention span is short and peaks shortly after its creation
Evolution of number of likes on Vine

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Attention Aspects
Evolution of number of shares on Twitter
Analyze amount of user attention received by a Vine video
Hypothesis: attention span is short and peaks shortly after its creation

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1. Introduction
2. Vine
3. Data Collection
5. Conclusions
Outline

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Conclusions
First large-scale academic measurement study on Vine
Collection of ~ 850,000 tweets containing ~425,000 unique Vine videos
34% of Vine videos in our dataset contain atleast one hashtag
Content is highly personal, created for entertainment purposes
Twitter cannot be used as a measure for the popularity of a
Vine video
User attention peaks shortly after the creation but continues
even after weeks

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1. Introduction
2. Vine
3. Data Collection
5. Conclusions
Outline

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Future Research Directions
Investigate what portion of Vine videos is news- or event-related
How can these be used to enhance news stories.
Create a (geo-based) hashtag recommendation and categorization system
Make use of content (visual) and context (textual) information
Leverage user mentions to detect community formation
By using mentions in description and faces in video

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Future Research Directions
Create personalized television channels
Based on hashtags and user preferences
Compare short-form video usage on Vine with short-form video usage on
Instagram and YouTube

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Questions?

Vine Measurement Study

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