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.

Introduction: The distribution of textual content is typically very fast and catches user attention for only a short period of time BIBREF0 . For this reason, proper wording of the article title may play a significant role in determining the future popularity of the article. The reflection of this phenomenon is the proliferation of click-baits - short snippets of text whose main purpose is to encourage viewers to click on the link embedded in the snippet. Although detection of click-baits is a separate research topic BIBREF1 , in this paper we address a more general problem of predicting popularity of online content based solely on its title. Predicting popularity in the Internet is a challenging and non-trivial task due to a multitude of factors impacting the distribution of the information: external context, social network of the publishing party, relevance of the video to the final user, etc. This topic has therefore attracted a lot of attention from the research community BIBREF2 , BIBREF3 , BIBREF0 , BIBREF4 . In this paper we propose a method for online content popularity prediction based on a bidirectional recurrent neural network called BiLSTM. This work is inspired by recent successful applications of deep neural networks in many natural language processing problems BIBREF5 , BIBREF6 . Our method attempts to model complex relationships between the title of an article and its popularity using novel deep network architecture that, in contrast to the previous approaches, gives highly interpretable results. Last but not least, the proposed BiLSTM method provides a significant performance boost in terms of prediction accuracy over the standard shallow approach, while outperforming the current state-of-the-art on two distinct datasets with over 40,000 samples. To summarize, the contributions presented in this paper are the following: The remainder of this paper is organized in the following manner: first, we review the relevant literature and compare our approach to existing work. Next, we formulate the problem of popularity prediction and propose a model that takes advantage of BiLSTM architecture to address it. Then, we evaluate our model on two datasets using several pre-trained word embeddings and compare it to benchmark models. We conclude this work with discussion on future research paths.

Question: Which shallow approaches did they experiment with?
Answer: SVM with linear kernel using bag-of-words features