Shallow reading with Deep Learning: Predicting popularity of online content using only its title
This addresses the need for better content engagement prediction for online publishers and marketers, though it is incremental as it builds on existing LSTM and word embedding techniques.
The paper tackles the problem of predicting online content popularity using only titles, achieving a 15% performance improvement over traditional shallow methods on datasets of over 40,000 news articles and videos.
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.