Abstract: Online media outlets, in a bid to expand their reach and subsequently increase revenue through ad monetisation, have begun adopting clickbait techniques to lure readers to click on articles. The article fails to fulfill the promise made by the headline. Traditional methods for clickbait detection have relied heavily on feature engineering which, in turn, is dependent on the dataset it is built for. The application of neural networks for this task has only been explored partially. We propose a novel approach considering all information found in a social media post. We train a bidirectional LSTM with an attention mechanism to learn the extent to which a word contributes to the post's clickbait score in a differential manner. We also employ a Siamese net to capture the similarity between source and target information. Information gleaned from images has not been considered in previous approaches. We learn image embeddings from large amounts of data using Convolutional Neural Networks to add another layer of complexity to our model. Finally, we concatenate the outputs from the three separate components, serving it as input to a fully connected layer. We conduct experiments over a test corpus of 19538 social media posts, attaining an F1 score of 65.37% on the dataset bettering the previous state-of-the-art, as well as other proposed approaches, feature engineering or otherwise.

Introduction: The Internet provides instant access to a wide variety of online content, news included. Formerly, users had static preferences, gravitating towards their trusted sources, incurring an unwavering sense of loyalty. The same cannot be said for current trends since users are likely to go with any source readily available to them. In order to stay in business, news agencies have switched, in part, to a digital front. Usually, they generate revenue by (1) advertisements on their websites, or (2) a subscription based model for articles that might interest users. However, since the same information is available via multiple sources, no comment can be made on the preference of the reader. To lure in more readers and increase the number of clicks on their content, subsequently increasing their agency's revenue, writers have begun adopting a new technique - clickbait. The concept of clickbait is formalised as something to encourage readers to click on hyperlinks based on snippets of information accompanying it, especially when those links lead to content of dubious value or interest. Clickbaiting is the intentional act of over-promising or purposely misrepresenting - in a headline, on social media, in an image, or some combination - what can be expected while reading a story on the web. It is designed to create and, consequently, capitalise on the Loewenstein information gap BIBREF0 . Sometimes, especially in cases where such headlines are found on social media, the links can redirect to a page with an unoriginal story which contains repeated or distorted facts from the original article itself. Our engine is built on three components. The first leverages neural networks for sequential modeling of text. Article title is represented as a sequence of word vectors and each word of the title is further converted into character level embeddings. These features serve as input to a bidirectional LSTM model. An affixed attention layer allows the network to treat each word in the title in a differential manner. The next component focuses on the similarity between the article title and its actual content. For this, we generate Doc2Vec embeddings for the pair and act as input for a Siamese net, projecting them into a highly structured space whose geometry reflects complex semantic relationships. The last part of this system attempts to quantify the similarity of the attached image, if any, to the article title. Finally, the output of each component is concatenated and sent as input to a fully connected layer to generate a score for the task.

Question: What are the differences with previous applications of neural networks for this task?
Answer: This approach considers related images