  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.
