Thanks to the increasing popularity of mobile devices and online social networks, mobile and social video is on the rise, calling for a better understanding of its usage and future impact. In this paper, we provide an in-depth measurement study of Vine, a mobile application that is used for creating and sharing short looping videos of up to six seconds in length. Based on a dataset of 851,039 tweets containing a Vine URL, we investigate di erent aspects of Vine, including hashtag usage, video popularity and user attention. For the dataset used, we nd that 34% of the Vine videos contain at least one hashtag, a percentage that is four times higher than the percentage of tweets that is in general annotated with at least one hashtag. In addition, we can observe that a Vine video that is shared frequently on Twitter within hours after its creation will have more likes on Vine after one week, compared to a Vine video that is not shared frequently on Twitter during this same period of time. However, we cannot establish a clear link between the number of tweets sharing a Vine video and its resulting popularity. Finally, by analyzing the evolution of the number of likes and the number of shares received by a Vine video on Vine and Twitter, respectively, we can conclude that a Vine video receives most user attention shortly after its creation, with the amount of user attention received not stopping completely but remaining stable for days to weeks after its creation. 1
With the increase in popularity of smartphones
and online social networks, mobile and social video
is on the rise. Vine, established in January 2013
and immediately acquired by Twitter, is the rst
well-known mobile application to focus on short-form
video. In the months following its release, Vine
quickly gained an active user base and was reported
to be the world's fastest growing mobile application
at that time [
Vine enables its users to create and distribute short looping videos of up to six seconds in length. In addition, its users can follow other users, re-broadcast videos to their followers by so-called revining, comment on videos and embed videos on websites. Furthermore, its users also have the option of sharing videos to followers on Twitter or other online social networks.
The video length limitation of Vine resembles
the message length limitation of Twitter, relying on
the creativity of its users to spread essential
information. Similar to Twitter, Vine is well suited for
fast spreading of news, albeit on a visual level. This
became clear with the Boston Marathon bombing
tragedy, seeing the use of Vine as a social news
platform [
In this paper, we present an in-depth measurement study of Vine. We use Twitter as an access portal to harvest Vine videos and context information, exploiting the resulting dataset to achieve a better understanding of hashtag usage, video popularity and user attention, among other aspects. To the best of our knowledge, this is the rst academic study of Vine. We organized the rest of this paper as follows. In Section 2, we discuss related work. In Section 3, we explain the way we collected Vine videos. In Section 4, we investigate the general characteristics of Vine, subsequently focusing on creation time and origin aspects in Section 5, video popularity aspects in Section 6, and user attention aspects in Section 7. Finally, we present conclusions and directions for future research in Section 8 and in Section 9, respectively. 2
In this section, we review a number of representative research e orts in the area of online social networks, paying particular attention to the following topics: content and audience analysis, popularity analysis and prediction, usage of online social network context, and social sensing. 2.1
The authors of [
Several studies analyzed the popularity distribution of
user-generated videos and images on online social
networks such as YouTube, Flickr and Instagram. The
authors of [
A wide range of studies is available on the use of
online social network context for designing new and
improved algorithms for multimedia content analysis. In
[
In this section, we brie y describe the acquisition of Vine data. Because of the lack of an o cial and public Vine API, we used Twitter as a gateway to access and harvest Vine videos. Furthermore, we also used the uno cial Vine API methods to extract metadata. We harvested tweets containing Vine URLs by tracking the keyword \vine" via the public Twitter streaming API from January 10, 2014 until January 24, 2014. This resulted in 851,039 tweets containing Vine URLs, originating from 365,188 di erent Twitter users. We then used HTML scraping to extract the unique Vine ID from each Vine URL. Next, we used the extracted Vine ID and the private Vine API methods to fetch information regarding the speci c properties of the Vine video and its corresponding user. By making use of the aforementioned approach, we were able to collect 425,971 unique Vine videos that have been created by 193,355 unique Vine users. The key properties fetched can be found in Table 1. We note that Vine does not provide metadata regarding the view count of a Vine video. In this study, we therefore make use of an aggregation of the number of likes, revines and comments to assess the popularity of a Vine video (cf. Section 6). The size of our dataset is not representative for the number of videos shared on Vine during the above mentioned period. However, our dataset is representative for the number of Vine shares on Twitter during this period. The strong interweaving between Vine and Twitter allows us to measure characteristics of the dataset using both Vine and Twitter metadata. The Twitter metadata consists of a tweet containing a Vine URL and the Twitter user sharing this tweet. The above dataset is used in all of our experiments, with the exception of Section 4.3, which analyzes the popularity of the di erent Vine channels
Date Fetched Date Created Description Location Number of Likes Number of Revines Number of Comments Explicit Content (i.e., the di erent Vine categories). Due to the 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 videos over the di erent Vine channels. By using the uno cial Vine API, we were able to identify the di erent channels and their unique IDs. We subsequently crawled each channel's list of newly added Vine videos between December 6, 2013, and December 12, 2013 in a continuous manner. The resulting dataset contains 370,410 unique videos belonging to 16 di erent channels (see Section 4.3 for more details). 4
In this section, we investigate various characteristics of Vine, including the technical and metadata characteristics of Vine videos. We also pay attention to the characteristics of Vine channels and popular Vine content. 4.1
Length (s)
File size (MB)
Bitrate (Mbps)
A Vine video can be looked upon as a visual tweet.
Characterized by its limited video length of only six
seconds, users are forced to be concise. Typically, a
Vine video has a square frame width and height of 480
pixels. In Table 2, we summarize the size, bitrate and
length properties of 5; 000 videos randomly sampled
from our dataset. We can observe that the average le
size of a Vine video is less than 1 MB. This average
le size is much smaller than the average le size of
YouTube videos, which was estimated to be 8.4 MB
in [
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 [
Given our dataset, we investigated the use of
hashtags and mentions that have been assigned to
Vine videos. Our analysis revealed that 34.0% of Vine
videos contain at least one hashtag, while 9.24% of
Vine videos contain one or more mentions. In this
context, we would like to note that the percentage
of Vine videos containing a hashtag is signi cantly
higher than the percentage of tweets containing
a hashtag (i.e., less than 8%, according to [
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 hashtags.
To estimate the information content of the hashtags
used, we mapped the hashtags in our dataset onto
the WordNet synsets [
Although the majority of Vine videos can be classied as entertaining and non-informative content, we could observe that our entire dataset does contain Vine videos that are related to news or sports events. To get an impression of the nature of these Vine videos, Figure 5 and Figure 6 show snapshots of Vine videos covering a number of recent events (e.g., the Golden Globes, the Purdue shooting, the Australian Open, and so on). We retrieved these videos from our dataset by using di erent hashtags (e.g., #goldenglobes, #purdue, #australianopen, and so on). Note that these videos often give personal comments on events, either showing news-related images or presenting live footage of the video creator being present at the event. As such, these videos could be seen as an addition to text-based news reporting, giving di erent insights into a global event.
In this section, we present ndings 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 implying 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 location eld derived from the location eld of the Vine user. In 27:2% of the cases, we were able to match the location of the Vine user using the Google Geocoding API.
In Figure 7, we show the number of Vine videos created and binned per hour, whereas Figure 8 shows which countries are creating the most videos on Vine. Clearly, Figure 7 is heavily in uenced by the timezones applicable in the countries where Vine is the most popular. As such, Figure 9 also gives an overview of the number of Vine videos created in the USA, normalized per timezone. We can observe that the creation of Vine videos peaks during the afternoon and drops during night time.
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 more popular Vine video (i.e., a Vine video with a relatively high number of likes, revines and comments). We de ne the Twitter exposure ET as the sum of the number of followers of the di erent Twitter users sharing these tweets. Similarly, we de ne 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 ( rst 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.
We can observe that the number of shares on Twitter is only weakly correlated with each of the Vine popularity indicators used. This undermines our rst hypothesis that a higher number of shares on Twitter can be linked to a more popular Vine video, showing that, despite the close relation between Vine and Twitter, each platform functions according to its own rules. Indeed, notwithstanding the fact that tweeting a Vine link automatically embeds the corresponding Vine video on Twitter, this embedding does not allow for liking, revining, or commenting. In other words, a Twitter user cannot directly add to the popularity of a Vine video, except when the user likes, revines or comments on the Vine platform itself. Furthermore, we also nd no correlation between the Twitter exposure and the di erent popularity indicators. The second hypothesis that sharing a Vine video with a large user base automatically results in a high popularity does show to be correct, given the relatively strong correlation between the Vine exposure and the Vine popularity indicators. Albeit, we cannot measure the actual impact of Twitter on a Vine video's popularity due to the lack of view information and a clear correlation between the number of Twitter shares and the popularity measures, we state that a Vine video that is shared more than once on Twitter (i.e., not just by the creator of the Vine video) in an early stage (i.e., within the hour) will be an indicator for the popularity of a Vine video.
Figure 10 shows the number of shares on Twitter of a Vine video one hour after its creation linked to the number of likes this video has on Vine after one week. We can observe that an initial correlation exists between the number of shares on Twitter and the number 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 than 15). For a higher number of Twitter shares, we can similarily to the ndings above state that we cannot measure the impact of Twitter. This is due to the same reasons as stated above and also related to factors such as the social impact of the Twitter users sharing the Vine video (i.e., the number of retweets per tweet) or the social network of the Vine users revining on Vine. 7
In this section, we investigate the amount of user attention received by Vine videos. Our analysis is twofold: 1) we study the evolution of the number of likes on Vine of Vine videos and 2) we study the evolution of the number of shares on Twitter of Vine videos. Both aspects are studied in relation to the number of hours following the creation of the Vine videos. As such, we de ne user attention as the number of likes on Vine gained or the number of shares spread on Twitter during a certain time span. Due to the fast nature of Vine and Twitter, we hypothesize that the user attention span is short and that user attention peaks shortly after the creation of a Vine video.
First, we analyze the evolution of the number of likes given to a Vine video during the rst two weeks after its creation. For this analysis, we only take into account Vine videos that have been created in the USA and that have received at least ve shares one hour after their creation, resulting in the use of 3,312 Vine videos having 32:1 Twitter shares on average. Figure 11 shows the evolution of the average number of likes per Vine video. We can observe that the increase in the average number of likes is highest one day after the creation of a Vine video. However, we can also observe that the average number of likes keeps increasing steadily during subsequent days.
Second, we analyze the evolution of the number of Twitter shares given to a Vine video in relation to the number of hours following its creation. For this analysis, we only take into account Vine videos that have been shared on Twitter, both within one hour after their creation and after seven days of their creation, resulting in the use of 10,696 Vine videos. Figure 12, which uses a log-log scale, shows a trend that is comparable to the trend shown in Figure 11. We can observe that a Vine video receives most user attention on Twitter during the rst hours after its creation. Note that the distribution shown in Figure 12 can be modeled by a power law-like distribution with = 0:649.
In this paper, we presented a large-scale measurement study of Vine, a popular mobile application for creating and posting short looping videos, paying particular attention to Vine hashtag usage, video popularity and user attention. To that end, we made use of Twitter as an access portal to Vine, harvesting 851,039 tweets containing a Vine URL. To the best of our knowledge, this is the rst 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 le 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 signi cantly 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 the WordNet synsets. By subsequently mapping the matched Vine hashtags onto the WordNet categories, we also found that the category people or groups is tagged most frequently (20%), followed by objects or artifacts (19%), actions or events (11%), locations (7%), and emotions or cognitions (4%).
Through our study, we could learn that the content of Vine videos is typically highly personal, mostly created for entertainment purposes. However, we could also observe that Vine videos are created when notable events take place, possibly bringing Vine forward as a visual Twitter-alike social news platform in the near future.
We investigated the popularity of Vine videos by making use of both Vine and Twitter metadata, nding 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 e ect 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.
Finally, we also investigated the average amount of user attention given to Vine videos by studying the evolution of the number of shares on Twitter and the number of likes on Vine. We could notice that the number of shares of Vine videos on Twitter is highest in the hours after their creation and then drops signi cantly, following a power law-like distribution. We expected that the user attention span of Vine videos would be short, in the order of a couple days to a week, but found that, although most user activity indeed occurs shortly after their creation, the number of likes still keeps increasing after the rst week of their creation. 9
Given that the ndings of our measurement study of Vine are meant to tailor future technological research in the domain of mobile and social video, we present a number of directions for future research we plan to work and collaborate on.
To investigate what speci c portion of Vine videos is news- or event-related and in what way these videos can be used to enhance news stories on the Internet.
To create a (geo-based) hashtag recommendation and categorization system for Vine videos by making use of both content (visual) and context (textual) information.
To leverage user mentions to detect community formation.
To identify user segments based on categorization of the content and the context of Vine videos. To create personalized television channels based on hashtags and user preferences.
To compare short-form video usage on Vine with short-form video usage on YouTube (MixBit) and Instagram Video.
To make use of Vine as a dataset for the creation of robust video face detection and recognition algorithms. 10
The research activities described in this paper were funded by Ghent University, iMinds, the Institute for Promotion of Innovation by Science and Technology in Flanders (IWT), the FWO-Flanders, and the European Union.