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Predicting Popularity of Online Content


A look at predicting what makes online content popular, with a particular focus on images, especially selfies.



Selfies

 
When talking about pictures online one cannot forget about one of the most popular type of pictures (if not THE most popular one): #selfies. So how should we take our selfie to make it popular? Andrej Karpathy tackled this exciting problem in his blogpost, where he trained a deep convolutional neural network to analyse hundreds of thousands of selfies and figure out which ones are popular and why.

He gathered a dataset of over 2 million selfie images from the Internet and split them into two categories: good and bad, according to their popularity. The resulting sample of pictures looks like that:

Selfies

Source: What a Deep Neural Network thinks about your #selfie.
A. Karpathy's Blog.

He then fine-tuned a pre-trained deep convolutional neural network that won one of the recent ImageNet challenges, namely VGG-net, to perform a binary classification of good/bad selfie. He held out a test dataset of 50 000 pictures and asked the neural network what its opinion on those pictures was. Theses are the results:

Selfies

Source: What a Deep Neural Network thinks about your #selfie.
A. Karpathy's Blog.

In the top-100 images according to the neural network, all of them features females. Most of them took more than 1/3 part of the image and, what is especially interesting, most of the people shown in the pictures had their foreheads cut off. When looking into male selfies, the network picked pictures of guys with fancy hair style combed upwards, significant part of them naked with both shoulders within the frame. Well… apparently that’s what makes you look better!

Selfies

Source: What a Deep Neural Network thinks about your #selfie.
A. Karpathy's Blog.

Also, Karpathy looked at the worst 100 selfies and quickly discovered that they share several characteristics - most of them were under-exposed with too large heads and group shots.

Selfies

Source: What a Deep Neural Network thinks about your #selfie.
A. Karpathy's Blog.

The network was also implemented as a twitter bot to help users take the best crop of their selfie (according to the network). The results show that for some cases, the best selfie... is a selfie without the author at all! :)


Source: What a Deep Neural Network thinks about your #selfie.
A. Karpathy's Blog.

Data Science @ Tooploox

 
In Tooploox, we work with companies that span multiple domains, countries and continents. One of our client is a media company focused on providing the best video content to its users via social media. Therefore, we also looked into the problem of popularity prediction of online content taking into account videos.

Similarly to photos, some videos get popular, while the others - don’t. Well, a good example of the former is a video of baby panda bear sneezing that was watched over 220 million times.

Now, what can we do to predict the popularity of the video published online? Some early works suggest that one way is to look at the popularity just after publication, as early view patterns reflect long-term interest. For example, Szabo et al. in their paper entitled Predicting the popularity of online content analysed the view counts of YouTube videos within 30 days after publication. The resulting correlation suggest that the prediction of the popularity after 30 days can be done just after 7 days with fairly high precision.

Popularity

Source: Predicting the popularity of online content.
G. Szabo and B. A. Huberman.

In our work, we extended this analysis and computed several visual features of the video, such as dominant color, scene dynamics, clutter metrics and textual features present in the video. Since the videos that we analysed are distributed via social media, we also looked into social features such as number of comments, shares and likes. We have plugged all the available data (view counts, social features and visual features) into a Support Vector Regression(SVR) method and compare the resulting prediction accuracy against the state-of-the-art methods. As a matter of fact, SVR based on view counts provides a fairly good prediction estimate, but it can be improved when extending input features with social and visual cues.

To put it into a nutshell, if you want to make predictions in the world of online content, don’t forget that it’s not only who takes a picture/video, but also who shares it or who comments on it. Thank you for reading and good luck with making your predictions! As they say: it’s not that difficult to make predictions, especially about the images and videos online ;)

Bio: Tomasz Trzcinski is a Chief Scientist at Tooploox and an Associate Professor at Warsaw University of Technology. He holds a PhD in computer vision from EPFL and has worked with Google, Qualcomm and Telefonica. Tomasz can be reached by email at tomasz.trzcinski@tooploox.com.

Original. Reposted with permission.

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