Shallow reading with Deep Learning: Predicting popularity of online content using only its title

With the ever decreasing attention span of contemporary Internet users, the title of online content (such as a news article or video) can be a major factor in determining its popularity. To take advantage of this phenomenon, we propose a new method based on a bidirectional Long Short-Term Memory (LSTM) neural network designed to predict the popularity of online content using only its title. We evaluate the proposed architecture on two distinct datasets of news articles and news videos distributed in social media that contain over 40,000 samples in total. On those datasets, our approach improves the performance over traditional shallow approaches by a margin of 15%. Additionally, we show that using pre-trained word vectors in the embedding layer improves the results of LSTM models, especially when the training set is small. To our knowledge, this is the first attempt of applying popularity prediction using only textual information from the title

Intrinsic Image Popularity Assessment

The goal of research in automatic image popularity assessment (IPA) is to develop computational models that can accurately predict the potential of a social image to go viral on the Internet. Here, we aim to single out the contribution of visual content to image popularity, i.e., intrinsic image popularity. Specifically, we first describe a probabilistic method to generate massive popularity-discriminable image pairs, based on which the first large-scale image database for intrinsic IPA (I$^2$PA) is established. We then develop computational models for I$^2$PA based on deep neural networks, optimizing for ranking consistency with millions of popularity-discriminable image pairs. Experiments on Instagram and other social platforms demonstrate that the optimized model performs favorably against existing methods, exhibits reasonable generalizability on different databases, and even surpasses human-level performance on Instagram. In addition, we conduct a psychophysical experiment to analyze various aspects of human behavior in I$^2$PA.Comment: Accepted by ACM Multimedia 201

A Vocabulary for Growth: Topic Modeling of Content Popularity Evolution

In this paper, we present a novel method to predict the long-term popularity of user-generated content (UGC). At first, the method clusters the dynamics of UGC popularity into a vocabulary of growth in popularity (sequence) by using a mixture model. Eventually, the method assigns to each sequence a topic model to describe the dynamics of the sequence in a compact way. We then use this topic model to identify similar patterns of growth in popularity of newly observed UGC. The proposed method has two key features: First, it considers the historical dynamics of the UGC popularity, and second it provides long-term popularity prediction. Results on the real dataset of UGC show that the proposed method is flexible, and able to accurately forecast the complete growth in popularity of a given UGC

A Hierarchical Attention-based Contrastive Learning Method for Micro Video Popularity Prediction

Micro videos popularity prediction (MVPP) has recently attracted widespread research interests given the increasing prevalence of video-based social platforms. However, previous studies have overlooked the unique patterns between popular and unpopular videos and the interactions between asynchronous features different data dimensions. To address this, we propose a novel hierarchical attention contrastive learning method named HACL, which extracts explainable representation features, learns their asynchronous interactions from both temporal and spatial levels, and separates the positive and negative embeddings identities. This reveals video popularity in a contrastive and interrelated view, and thus can be responsible for a better MVPP. Dual neural networks account for separate positive and negative patterns via contrastive learning. To obtain the temporal-wise interaction coefficients, we propose a Hadamard-product based attention approach to optimize the trainable attention-map matrices. Results from our experiments on a TikTok micro video dataset show that HACL outperforms benchmarks and provides insightful managerial implications

Popularity prediction of instagram posts

Predicting the popularity of posts on social networks has taken on significant importance in recent years, and several social media management tools now offer solutions to improve and optimize the quality of published content and to enhance the attractiveness of companies and organizations. Scientific research has recently moved in this direction, with the aim of exploiting advanced techniques such as machine learning, deep learning, natural language processing, etc., to support such tools. In light of the above, in this work we aim to address the challenge of predicting the popularity of a future post on Instagram, by defining the problem as a classification task and by proposing an original approach based on Gradient Boosting and feature engineering, which led us to promising experimental results. The proposed approach exploits big data technologies for scalability and efficiency, and it is general enough to be applied to other social media as well