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

TL;DR

This paper introduces a bidirectional LSTM model that predicts online content popularity solely from titles, demonstrating significant improvements over traditional methods and highlighting the benefits of pre-trained word vectors.

Contribution

The paper presents the first application of deep learning to predict content popularity using only titles, achieving better accuracy than shallow approaches.

Findings

LSTM-based approach improves prediction accuracy by 15% over traditional methods.

Pre-trained word vectors enhance model performance, especially with small datasets.

First study to use only textual titles for popularity prediction.

Abstract

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.