=Paper=
{{Paper
|id=Vol-1198/vandersmissen
|storemode=property
|title=The Rise of Mobile Social Video: An In-depth Analysis of Vine
|pdfUrl=https://ceur-ws.org/Vol-1198/vandersmissen.pdf
|volume=Vol-1198
|dblpUrl=https://dblp.org/rec/conf/mir/VandersmissenGTNW14
}}
==The Rise of Mobile Social Video: An In-depth Analysis of Vine==
https://ceur-ws.org/Vol-1198/vandersmissen.pdf
The Rise of Mobile and Social Short-Form Video:
An In-depth Measurement Study of Vine
Baptist Vandersmissen Fréderic Godin
Multimedia Lab, ELIS, Ghent University Multimedia Lab, ELIS, Ghent University
iMinds, Ghent, Belgium iMinds, Ghent, Belgium
baptist.vandersmissen@ugent.be frederic.godin@ugent.be
Wesley De Neve
1
Abhineshwar Tomar Multimedia Lab, ELIS, Ghent University
Multimedia Lab, ELIS, Ghent University iMinds, Ghent, Belgium
2
iMinds, Ghent, Belgium Image and Video Systems Lab
abhineshwar.tomar@ugent.be KAIST, Daejeon, South Korea
wesley.deneve@ugent.be
Rik Van de Walle
Multimedia Lab, ELIS, Ghent University
iMinds, Ghent, Belgium
rik.vandewalle@ugent.be
In addition, we can observe that a Vine video
that is shared frequently on Twitter within
Abstract hours after its creation will have more likes
on Vine after one week, compared to a Vine
Thanks to the increasing popularity of mobile video that is not shared frequently on Twit-
devices and online social networks, mobile and ter during this same period of time. However,
social video is on the rise, calling for a better we cannot establish a clear link between the
understanding of its usage and future impact. number of tweets sharing a Vine video and
In this paper, we provide an in-depth measure- its resulting popularity. Finally, by analyzing
ment study of Vine, a mobile application that the evolution of the number of likes and the
is used for creating and sharing short looping number of shares received by a Vine video on
videos of up to six seconds in length. Based on Vine and Twitter, respectively, we can con-
a dataset of 851,039 tweets containing a Vine clude that a Vine video receives most user
URL, we investigate different aspects of Vine, attention shortly after its creation, with the
including hashtag usage, video popularity and amount of user attention received not stop-
user attention. For the dataset used, we find ping completely but remaining stable for days
that 34% of the Vine videos contain at least to weeks after its creation.
one hashtag, a percentage that is four times
higher than the percentage of tweets that is in
general annotated with at least one hashtag. 1 Introduction
With the increase in popularity of smartphones
Copyright c by the paper’s authors. Copying permitted only
for private and academic purposes.
and online social networks, mobile and social video
In: S. Papadopoulos, P. Cesar, D. A. Shamma, A. Kelliher, R.
is on the rise. Vine, established in January 2013
Jain (eds.): Proceedings of the SoMuS ICMR 2014 Workshop, and immediately acquired by Twitter, is the first
Glasgow, Scotland, 01-04-2014, published at http://ceur-ws.org well-known mobile application to focus on short-form
video. In the months following its release, Vine 2.1 Content and Audience Analysis
quickly gained an active user base and was reported
The authors of [6] performed a large-scale and in-
to be the world’s fastest growing mobile application
depth measurement study of YouTube, discovering sig-
at that time [9]. In August 2013, Vine topped 40
nificant differences between YouTube videos and tra-
million users, withstanding the successful launch of
ditional streaming videos in terms of video lengths,
Instagram Video [7].
access patterns and active life spans. Furthermore,
they looked into growth trends and social aspects
Vine enables its users to create and distribute
of YouTube. In [1], Twitter is used to analyze the
short looping videos of up to six seconds in length. In
who, what and when questions related to YouTube.
addition, its users can follow other users, re-broadcast
Through combining the user- and sharing-centric data
videos to their followers by so-called revining, com-
of Twitter with the video-centric data of YouTube,
ment on videos and embed videos on websites.
the authors are able to establish links between ini-
Furthermore, its users also have the option of sharing
tial Twitter shares and the total number of views, as
videos to followers on Twitter or other online social
well as between Twitter shares and the type of con-
networks.
tent. The authors of [17] investigated the temporal,
social and spatial dimensions of Flickr user behaviour.
The video length limitation of Vine resembles
They conclude that 50% of the photo views are gener-
the message length limitation of Twitter, relying on
ated within the first two days. Furthermore, they also
the creativity of its users to spread essential infor-
state that the social networking behaviour of users and
mation. Similar to Twitter, Vine is well suited for
photo pooling are the most important indicators of the
fast spreading of news, albeit on a visual level. This
popularity of a photo.
became clear with the Boston Marathon bombing
tragedy, seeing the use of Vine as a social news 2.2 Popularity Analysis and Prediction
platform [2]. However, the low threshold to create
and share Vine videos entails a significant amount of Several studies analyzed the popularity distribution of
noisy data. This, combined with the typical short user-generated videos and images on online social net-
video length and the limited availability of context works such as YouTube, Flickr and Instagram. The
information, makes it for instance hard to organize authors of [4] analyzed the popularity life-cycle of user-
and browse Vine videos. generated content originating from YouTube in rela-
tion to the video age and level of content aliasing.
In this paper, we present an in-depth measure- The authors of [3] investigated the impact of content-
ment study of Vine. We use Twitter as an access agnostic factors on YouTube video popularity, finding
portal to harvest Vine videos and context information, that the current view count is the most important fac-
exploiting the resulting dataset to achieve a better tor to consider when predicting the future popularity
understanding of hashtag usage, video popularity and of a video, with the exception of videos that have been
user attention, among other aspects. To the best of shortly uploaded. In the latter case, the size of the
our knowledge, this is the first academic study of Vine. social network of the uploader is more important for
future popularity prediction purposes. On Flickr, the
We organized the rest of this paper as follows. authors of [5] analyzed how information propagates
In Section 2, we discuss related work. In Section 3, throughout the network, with the aim of gaining in-
we explain the way we collected Vine videos. In sight into the viral spreading of particular items. They
Section 4, we investigate the general characteristics state that information exchanged among friends is the
of Vine, subsequently focusing on creation time and most dominant factor leading to propagation through-
origin aspects in Section 5, video popularity aspects in out the network.
Section 6, and user attention aspects in Section 7. Fi-
nally, we present conclusions and directions for future 2.3 Usage of Online Social Network Context
research in Section 8 and in Section 9, respectively. A wide range of studies is available on the use of on-
line social network context for designing new and im-
2 Related Work proved algorithms for multimedia content analysis. In
In this section, we review a number of representative [16], a face recognition method is combined with infor-
research efforts in the area of online social networks, mation derived from Facebook in order to improve the
paying particular attention to the following topics: accuracy of face recognition on personal photographs.
content and audience analysis, popularity analysis and Equivalent to the above, [13] used the collective knowl-
prediction, usage of online social network context, and edge in Flickr to build an image tag recommendation
social sensing. system.
2.4 Social Sensing
Table 1: Vine metadata.
Social multimedia systems such as Vine and Twit- Vine Vine User
ter allow supporting studies on social behaviour. In Date Fetched Username
particular, these systems can be looked upon as Par- Date Created Location
ticipatory Sensing Systems (PSSs), making it for in- Description Followers Count
stance possible to study city dynamics on a large scale. Location Following Count
In [14, 15], Instagram and Foursquare are used as Number of Likes Posts Count
PSSs, with the aim of analyzing user movement pat- Number of Revines Like Count
terns, finding points of interests and observing cultural Number of Comments Verified
behaviour. A more general overview on the way com- Explicit Content
putational analysis and visualization of PSS content
can contribute to the identification of social and cul- (i.e., the different Vine categories). Due to the
tural patterns can be found [10]. fact that the Vine metadata do not describe to what
channel a Vine video was added, we created a separate
smaller dataset for assessing the distribution of Vine
3 Data Collection
videos over the different Vine channels. By using
In this section, we briefly describe the acquisition of the unofficial Vine API, we were able to identify
Vine data. Because of the lack of an official and public the different channels and their unique IDs. We
Vine API, we used Twitter as a gateway to access subsequently crawled each channel’s list of newly
and harvest Vine videos. Furthermore, we also used added Vine videos between December 6, 2013, and
the unofficial Vine API methods to extract metadata. December 12, 2013 in a continuous manner. The
resulting dataset contains 370,410 unique videos
We harvested tweets containing Vine URLs by belonging to 16 different channels (see Section 4.3 for
tracking the keyword “vine” via the public Twitter more details).
streaming API from January 10, 2014 until January
24, 2014. This resulted in 851,039 tweets containing 4 General Vine Characteristics
Vine URLs, originating from 365,188 different Twitter
users. We then used HTML scraping to extract the In this section, we investigate various characteristics
unique Vine ID from each Vine URL. Next, we used of Vine, including the technical and metadata char-
the extracted Vine ID and the private Vine API acteristics of Vine videos. We also pay attention to
methods to fetch information regarding the specific the characteristics of Vine channels and popular Vine
properties of the Vine video and its corresponding content.
user. By making use of the aforementioned approach,
we were able to collect 425,971 unique Vine videos 4.1 Technical Characteristics
that have been created by 193,355 unique Vine
users. The key properties fetched can be found
Table 2: Vine video statistics
in Table 1. We note that Vine does not provide Min Max Mean Std. Dev.
metadata regarding the view count of a Vine video. Length (s) 1.4 7.6 6.1 0.73
In this study, we therefore make use of an aggrega- File size (MB) 0 2.23 0.82 0.24
tion of the number of likes, revines and comments Bitrate (Mbps) 0.08 2.97 1.12 0.2
to assess the popularity of a Vine video (cf. Section 6).
A Vine video can be looked upon as a visual tweet.
The size of our dataset is not representative for Characterized by its limited video length of only six
the number of videos shared on Vine during the above seconds, users are forced to be concise. Typically, a
mentioned period. However, our dataset is repre- Vine video has a square frame width and height of 480
sentative for the number of Vine shares on Twitter pixels. In Table 2, we summarize the size, bitrate and
during this period. The strong interweaving between length properties of 5, 000 videos randomly sampled
Vine and Twitter allows us to measure characteristics from our dataset. We can observe that the average file
of the dataset using both Vine and Twitter metadata. size of a Vine video is less than 1 MB. This average
The Twitter metadata consists of a tweet containing file size is much smaller than the average file size of
a Vine URL and the Twitter user sharing this tweet. YouTube videos, which was estimated to be 8.4 MB
in [6]. We can also see that the average bitrate of a
The above dataset is used in all of our experi- Vine video is about 1.12 Mbps, thus allowing for high-
ments, with the exception of Section 4.3, which quality streaming, even when the video contains a lot
analyzes the popularity of the different Vine channels of motion. Finally, we can observe that Vine contains
videos with a maximum length higher than the six
seconds allowed. This can most likely be attributed to
a hack of the application.
4.2 Metadata Characteristics
Vine videos can be given a description that may
contain hashtags or mentions of other Vine users.
Hashtags and mentions assist people and algorithms
in understanding the video content and the formation
of communities [11]. Therefore, it is important to
Figure 2: Number of hashtags per Vine video.
know to what extent users annotate videos on Vine
with hashtags and mentions.
To estimate the information content of the hashtags
used, we mapped the hashtags in our dataset onto
Given our dataset, we investigated the use of
the WordNet synsets [8], finding that 11.4% of the
hashtags and mentions that have been assigned to
hashtags used could be matched to the WordNet
Vine videos. Our analysis revealed that 34.0% of Vine
synsets. This low percentage is indicative of the use
videos contain at least one hashtag, while 9.24% of
of an uncontrolled hashtag vocabulary and of the
Vine videos contain one or more mentions. In this
presence of a high number of noisy hashtags.
context, we would like to note that the percentage
of Vine videos containing a hashtag is significantly
We additionally mapped the set of matched hashtags
higher than the percentage of tweets containing
onto the WordNet categories [8]. Figure 3 shows
a hashtag (i.e., less than 8%, according to [12]).
the distribution of the hashtags matched over the
Furthermore, our analysis revealed that a Vine video
different WordNet categories. We can observe that the
contains, on average, 0.87 hashtags and 0.13 mentions.
category people or groups is tagged most frequently
(20%), followed by objects or artifacts (19%), actions
In Figure 1, we show the distribution of the hashtag
or events (11%), locations (7%), and emotions or
frequency on a log-log scale. The x -axis refers to
cognitions (4%). The category other (39%) contains
the 94,716 unique hashtags, ordered by descending
the hashtags matched that could not be mapped
hashtag frequency, whereas the y-axis refers to the
onto the aforementioned WordNet categories. Our
hashtag frequency. This distribution can be modeled
results show that the hashtags used describe a wide
accurately by a power law, with the probability of
range of concepts (i.e., people, objects, actions,
a hashtag having frequency x being proportional to
events, locations, and so on), information that can be
x−0.934 .
leveraged by techniques for video classification and
video concept detection.
Figure 1: Hashtag frequency distribution.
Similar to the hashtag frequency, we can plot the
distribution of the number of hashtags per Vine video.
Figure 2 shows the number of Vine videos with x hash- Figure 3: Most frequent WordNet categories for Vine
tags. video hashtags.
4.3 Channel Characteristics
Vine contains multiple channels (i.e., categories) to
which a newly created video can optionally be pub-
lished to. Table 3 gives an overview of the different
channels that are currently in use. To study the pop-
ularity of these channels, we collected 370, 410 Vines
by following the procedure described in Section 3. Ta-
ble 3 makes clear that the distribution of the num-
ber of videos over the different Vine channels is highly
skewed: “Comedy” is by far the most popular channel,
followed by “Music” and “Wierd”. Clearly, the focus
is on entertaining and non-informative content. This
is comparable to the YouTube measurement study pre-
sented in [6], who similarly reported that “Entertain- Figure 4: Image collage of the top 6 most popular Vine
ment” and “Music” are the top video categories on videos, obtained from our dataset by aggregating the
YouTube. number of likes, revines, and comments.
Table 3: List of Vine channels
Rank Category Count Pct.
1. Comedy 225, 794 60.96 % Although the majority of Vine videos can be classi-
fied as entertaining and non-informative content, we
2. Music 33, 078 8.93 %
could observe that our entire dataset does contain
3. Wierd 19, 513 5.27 % Vine videos that are related to news or sports events.
4. Dogs 16, 525 4.46 % To get an impression of the nature of these Vine
5. Cats 12, 048 3.25 % videos, Figure 5 and Figure 6 show snapshots of Vine
6. Family 10, 152 2.74 % videos covering a number of recent events (e.g., the
Golden Globes, the Purdue shooting, the Australian
7. Art & Experimental 10, 141 2.74 %
Open, and so on). We retrieved these videos from
8. Sports 6, 964 1.88 % our dataset by using different hashtags (e.g., #golden-
9. Food 5, 949 1.61 % globes, #purdue, #australianopen, and so on). Note
10. Special fx 5, 642 1.52 % that these videos often give personal comments on
11. Nature 5, 458 1.47 % events, either showing news-related images or present-
ing live footage of the video creator being present at
12. Urban 5, 226 1.41 %
the event. As such, these videos could be seen as an
13. Scary 4, 691 1.27 % addition to text-based news reporting, giving different
14. Beauty & Fashion 4, 041 1.09 % insights into a global event.
15. News & Politics 2, 774 0.75 %
16. Health & Fitness 2, 414 0.65 %
4.4 Popularity Characteristics
To gain a more detailed insight into what type of
video content is popular on Vine, we collected the top
100 most popular Vine videos in our dataset, mea-
suring popularity by multiplying the number of likes,
revines and comments. Through a manual inspection,
we learned that the resulting collection contains user-
generated Vine videos that are not related to a particu-
lar event or brand. This is also illustrated by Figure 4,
presenting an image collage of the top 6 most popu-
lar Vine videos. We can thus conclude that Vine is
primarily used for producing and sharing concise and Figure 5: Image collage of Vine videos representing
creative content among its users. recent news events.
Figure 8: Number of Vine videos created per country.
Figure 6: Image collage of Vine videos representing In Figure 7, we show the number of Vine videos
recent sports events. created and binned per hour, whereas Figure 8 shows
which countries are creating the most videos on Vine.
Clearly, Figure 7 is heavily influenced by the timezones
applicable in the countries where Vine is the most pop-
ular. As such, Figure 9 also gives an overview of the
number of Vine videos created in the USA, normal-
ized per timezone. We can observe that the creation
5 Time and Place of Creation Aspects of Vine videos peaks during the afternoon and drops
during night time.
In this section, we present findings regarding the time
and place of creation of Vine videos. Recall that our
dataset contains 425,971 unique Vine videos derived
from 851,039 tweets containing a Vine URL, thus im-
plying that a major part of these tweets share the same
Vine video. Each Vine video possesses a creation time
stamp retrieved via the private Vine API and a loca-
tion field derived from the location field of the Vine
user. In 27.2% of the cases, we were able to match the
Figure 9: Number of Vine videos created by USA
location of the Vine user using the Google Geocoding
users, normalized according to the three main time-
API.
zones.
6 Video Popularity Aspects
In this section, we investigate how the popularity
of Vine videos changes over time. Since there is no
view count information of a Vine video, we quantify
the popularity of Vine videos by three parameters:
the number of likes, the number of revines, and the
number of comments on Vine. We pay particular at-
tention to the influence Twitter has on the popularity
of Vine videos.
We equate Twitter attention to the number of
shares ST fetched, and where these shares have been
produced by Twitter users distributing tweets that
contain a link to a Vine video. We hypothesize that a
higher number of shares on Twitter can be linked to a
Figure 7: Number of Vine videos created and binned more popular Vine video (i.e., a Vine video with a rel-
per hour. General UTC time is used. atively high number of likes, revines and comments).
We define the Twitter exposure ET as the sum of
the number of followers of the different Twitter users
sharing these tweets. Similarly, we define the Vine
exposure EV as the number of followers on Vine of
the original Vine video creator. We also hypothesize
that sharing a Vine video with a large user base
automatically results in a high popularity. Table 4
outlines the Pearson (first value) and Spearman
(second value) correlation values between ST , ET and
EV on the one hand, and the number of likes, revines
and comments on Vine on the other hand.
Figure 10: The number of shares of a Vine video on
Twitter one hour after its creation linked to the num-
ber of likes on Vine after one week.
Table 4: Correlation between factors influencing Vine
popularity and the number of likes, revines and com- Figure 10 shows the number of shares on Twitter
ments on Vine. of a Vine video one hour after its creation linked to
ST ET EV the number of likes this video has on Vine after one
Likes 0.288 0.537 0.069 0.288 0.602 0.666 week. We can observe that an initial correlation exists
Revines 0.318 0.597 0.075 0.325 0.459 0.666 between the number of shares on Twitter and the num-
Comments 0.308 0.597 0.073 0.310 0.394 0.661 ber of likes on Vine. In particular, this correlation is
strongest in the beginning of the curve (i.e., when the
number of shares on Twitter is less than 15) but less
obvious when the video is shared more (i.e., when the
number of shares on Twitter starts to become higher
We can observe that the number of shares on
than 15). For a higher number of Twitter shares, we
Twitter is only weakly correlated with each of the
can similarily to the findings above state that we can-
Vine popularity indicators used. This undermines
not measure the impact of Twitter. This is due to
our first hypothesis that a higher number of shares
the same reasons as stated above and also related to
on Twitter can be linked to a more popular Vine
factors such as the social impact of the Twitter users
video, showing that, despite the close relation between
sharing the Vine video (i.e., the number of retweets per
Vine and Twitter, each platform functions according
tweet) or the social network of the Vine users revining
to its own rules. Indeed, notwithstanding the fact
on Vine.
that tweeting a Vine link automatically embeds the
corresponding Vine video on Twitter, this embedding
does not allow for liking, revining, or commenting. In
7 User Attention Aspects
other words, a Twitter user cannot directly add to In this section, we investigate the amount of user
the popularity of a Vine video, except when the user attention received by Vine videos. Our analysis is
likes, revines or comments on the Vine platform itself. twofold: 1) we study the evolution of the number
Furthermore, we also find no correlation between of likes on Vine of Vine videos and 2) we study
the Twitter exposure and the different popularity the evolution of the number of shares on Twitter of
indicators. The second hypothesis that sharing a Vine Vine videos. Both aspects are studied in relation
video with a large user base automatically results to the number of hours following the creation of the
in a high popularity does show to be correct, given Vine videos. As such, we define user attention as
the relatively strong correlation between the Vine the number of likes on Vine gained or the number
exposure and the Vine popularity indicators. of shares spread on Twitter during a certain time
span. Due to the fast nature of Vine and Twitter, we
Albeit, we cannot measure the actual impact of hypothesize that the user attention span is short and
Twitter on a Vine video’s popularity due to the lack that user attention peaks shortly after the creation of
of view information and a clear correlation between a Vine video.
the number of Twitter shares and the popularity mea-
sures, we state that a Vine video that is shared more First, we analyze the evolution of the number of
than once on Twitter (i.e., not just by the creator likes given to a Vine video during the first two weeks
of the Vine video) in an early stage (i.e., within the after its creation. For this analysis, we only take into
hour) will be an indicator for the popularity of a Vine account Vine videos that have been created in the
video. USA and that have received at least five shares one
hour after their creation, resulting in the use of 3,312 8 Conclusions
Vine videos having 32.1 Twitter shares on average.
In this paper, we presented a large-scale measurement
Figure 11 shows the evolution of the average study of Vine, a popular mobile application for
number of likes per Vine video. We can observe that creating and posting short looping videos, paying
the increase in the average number of likes is highest particular attention to Vine hashtag usage, video
one day after the creation of a Vine video. However, popularity and user attention. To that end, we made
we can also observe that the average number of likes use of Twitter as an access portal to Vine, harvesting
keeps increasing steadily during subsequent days. 851,039 tweets containing a Vine URL. To the best of
our knowledge, this is the first academic study of Vine,
with the aim of achieving a better understanding of
mobile and social short-form video.
In our dataset, we could observe that Vine videos have
an average length of about 6.1 seconds and an average
file size of 0.82 MB. We could also observe that 34% of
the Vine videos in our dataset contained at least one
hashtag, a percentage that is significantly higher than
the 8% of tweets that is in general annotated with
at least one hashtag. Furthermore, we found that
11.4% of the Vine hashtags used could be matched to
Figure 11: The average number of likes received by the WordNet synsets. By subsequently mapping the
a Vine video as a function of the number of hours matched Vine hashtags onto the WordNet categories,
following its creation. we also found that the category people or groups is
tagged most frequently (20%), followed by objects
Second, we analyze the evolution of the number of or artifacts (19%), actions or events (11%), locations
Twitter shares given to a Vine video in relation to the (7%), and emotions or cognitions (4%).
number of hours following its creation. For this anal-
ysis, we only take into account Vine videos that have Through our study, we could learn that the con-
been shared on Twitter, both within one hour after tent of Vine videos is typically highly personal, mostly
their creation and after seven days of their creation, created for entertainment purposes. However, we
resulting in the use of 10,696 Vine videos. Figure 12, could also observe that Vine videos are created when
which uses a log-log scale, shows a trend that is compa- notable events take place, possibly bringing Vine
rable to the trend shown in Figure 11. We can observe forward as a visual Twitter-alike social news platform
that a Vine video receives most user attention on Twit- in the near future.
ter during the first hours after its creation. Note that
the distribution shown in Figure 12 can be modeled by We investigated the popularity of Vine videos
a power law-like distribution with α = 0.649. by making use of both Vine and Twitter metadata,
finding that Twitter cannot be used as a measure
for the popularity of Vine videos. However, we did
observe that Vine videos shared frequently on Twitter
in an early stage after their creation are more likely to
have more likes on Vine after one week, an effect that
could not be observed when the number of tweets
sharing the same Vine video becomes bigger. The
latter can be mainly attributed to the inability to
measure the amount of Twitter attention given to
Vine videos. Indeed, when an embedded Vine video
is viewed on Twitter, this is not notable in any Vine
metadata as Twitter does not allow to directly like,
revine or comment on Vine.
Figure 12: The average number of Twitter shares re- Finally, we also investigated the average amount
ceived by a Vine video as a function of the number of of user attention given to Vine videos by studying the
hours following its creation. evolution of the number of shares on Twitter and the
number of likes on Vine. We could notice that the [2] Adage. Boston Marathon Bombing Makes
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