Predicting the popularity of instagram posts for a lifestyle magazine using deep learning

De, Shaunak; Maity, Abhishek; Goel, Vritti; Shitole, Sanjay; Bhattacharya, Avik

doi:10.1109/cscita.2017.8066548

Cited by 34 publications

(24 citation statements)

References 4 publications

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“…DNN architecture was a four layer Stacked Auto-Encoder network, including four hidden layers followed by a multi-layer perceptron (MLP) with a sigmoid layer as the output for computing the final score and ReLU was used as the activation function. At the end, the average accuracy of the network was 88% [19].…”

Section: Related Workmentioning

confidence: 99%

Social Media Posts Popularity Prediction During Long-Running Live Events A case study on Fashion Week

Sabet¹

2021

Preprint

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In the last few years, social media has dominated various aspects of people’s life including social events. Users participate more and more in long-running periodical events in social media, by sharing their experiences and preferences. This information provides unprecedented opportunities allowing businesses to promote their brands coverage by using word-of-mouth (WOM), that is enabled by the user generated contents (UGCs). Studying social media content popularity by considering the societies’ behavioral patterns is, therefore, paramount. In this thesis, we inspect users’ engagement motives in long-running events by means of a comprehensive statistical analysis of fashion week events on Instagram. Additionally, we develop a multi-modal approach to solve the problem of post popularity prediction that exploits potentially influential factors and apply it on fashion week events. We employ two metrics for implementing a filter feature selection technique, together with an automated grid search for optimizing hyper-parameters in four regression methods: ridge, support vector regressor, gradient tree boosting and neural networks.

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Section: Related Workmentioning

confidence: 99%

Social Media Posts Popularity Prediction During Long-Running Live Events A case study on Fashion Week

Sabet¹

2021

Preprint

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“…Finally, a support vector regression model determined image popularity by utilizing the initial likelihood scores and user's features (number of posts, followers) for the specific category image. De et al [17] utilized deep learning models to determine the popularity of Instagram image posts of a lifestyle magazine. Image context and metadata features were used primarily in this work and fed into a deep neural network to predict the image's popularity.…”

Section: Image Popularity Predictionmentioning

confidence: 99%

“…Intuitively, we can say that the more likes and comments an image receives, the more popular it will be since more people are attracted to the images of interest, which leads them to like the images and then write short messages of admiration in the form of comments. Moreover, works in [15][16][17]36] used likes and comments as the popularity measure. Therefore, by following their work, we adopted the number of likes and comments as the popularity measure in our paper.…”

Section: Popularity Measuresmentioning

confidence: 99%

Exploring the Power of Multimodal Features for Predicting the Popularity of Social Media Image in a Tourist Destination

Gupta

Jung

Yoo

2020

MTI

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Social media platforms are widely used nowadays by various businesses to promote their products and services through multimedia content. Instagram is one of those platforms, which is used not only by companies to promote their products but also by local governments to promote tourist destinations. Predicting the popularity of the promotional tourist destination images helps marketers to plan strategically. However, given the abundance of images posted on Instagram daily, identifying the factors that determine the popularity of an image is a big challenge, due to informal and noisy visual content, frequent content evolution, a lack of explicit visual elements, and people’s informal behavior in liking, commenting on, and viewing the images. We present an approach to identify the factors most responsible for the popularity of tourist destinations-related images on Instagram. Our approach provides a proof of concept for an artificial intelligence (AI)-based real-time content management system, which will help to promote a tourist destination. The experiments on a collection of posts crawled from the official Instagram account of Jeju Island, which is one of the most popular tourist destinations in Korea, show that the recency of the post is the most important predictor of the number of likes and comments it will receive. Moreover, the combination of visual content and context features is an excellent predictor of popularity. The number of likes and comments are found to be complementary to each other for predicting image popularity.

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“…A practical application of our method is to analyze the effects of product advertisement on Instagram users. Previous works attempted to predict the popularity of Instagram posts by using surrogate signals such as number of likes or followers (Almgren et al, 2016;De et al, 2017). Others used social media data in order to model the popularity of fashion industry icons (Park et al, 2016).…”

Section: Related Work and Motivationmentioning

confidence: 99%

EmojiGAN: learning emojis distributions with a generative model

Mazoure

Doan

Ray³

2018

Proceedings of the 9th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis

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Generative models have recently experienced a surge in popularity due to the development of more efficient training algorithms and increasing computational power. Models such as adversarial generative networks (GANs) have been successfully used in various areas such as computer vision, medical imaging, style transfer and natural language generation. Adversarial nets were recently shown to yield results in the image-to-text task, where given a set of images, one has to provide their corresponding text description. In this paper, we take a similar approach and propose a image-to-emoji architecture, which is trained on data from social networks and can be used to score a given picture using ideograms. We show empirical results of our algorithm on data obtained from the most influential Instagram accounts.

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Predicting the popularity of instagram posts for a lifestyle magazine using deep learning

Cited by 34 publications

References 4 publications

Social Media Posts Popularity Prediction During Long-Running Live Events A case study on Fashion Week

Social Media Posts Popularity Prediction During Long-Running Live Events A case study on Fashion Week

Exploring the Power of Multimodal Features for Predicting the Popularity of Social Media Image in a Tourist Destination

EmojiGAN: learning emojis distributions with a generative model

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