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.
Related Work
The task of automating clickbait detection has risen to prominence fairly recently. Initial attempts for the same have worked on (1) news headlines, and (2) heavy feature engineering for the particular dataset. BIBREF1 's work is one of the earliest pieces of literature available in the field, focusing on an aggregation of news headlines from previously categorised clickbait and non-clickbait sources. Apart from defining different types of clickbait, they emphasise on the presence of language peculiarities exploited by writers for this purpose. These include qualitative informality metrics and use of forward references in the title to keep the reader on the hook. The first instance of detecting clickbait across social media can be traced to BIBREF2 , hand-crafting linguistic features, including a reference dictionary of clickbait phrases, over a dataset of crowdsourced tweets BIBREF3 . However, BIBREF4 argued that work done specifically for Twitter had to be expanded since clickbait was available throughout the Internet, and not just social networks.
It was not until BIBREF5 that neural networks were tried out for the task as the authors used the same news dataset as BIBREF4 to develop a deep learning based model to detect clickbait. They used distributional semantics to represent article titles, and BiLSTM to model sequential data and its dependencies. Since then, BIBREF6 has also experimented with Twitter data BIBREF3 deploying a BiLSTM for each of the textual features (post-text, target-title, target-paragraphs, target-description, target-keywords, post-time) available in the corpus, and finally concatenating the dense output layers of the network before forwarding it to a fully connected layer. Since it was proposed in BIBREF7 , the attention mechanism has been used for a variety of text-classification tasks, such as fake news detection and aspect-based sentiment analysis. BIBREF8 used a self-attentive BiGRU to infer the importance of tweet tokens in predicting the annotation distribution of the task.
One common point in all the approaches yet has been the use of only textual features available in the dataset. Our model not only incorporates textual features, modeled using BiLSTM and augmented with an attention mechanism, but also considers related images for the task.
Model Architecture
In this section, we present our hybrid approach to clickbait detection. We first explain the three individual components followed by their fusion, which is our proposed model. These components are (1) BiLSTM with attention, (2) Siamese Network on Text Embeddings, and (3) Siamese Network on Visual Embeddings. An overview of the architecture can be seen in Figure 1.
We start with an explanation of the features used in the first component of the model.
Distributed Word Embeddings
Considering the effectiveness of distributional semantics in modeling language data, we use a pre-trained 300 dimensional Word2Vec BIBREF9 model trained over 100 billion words in the Google News corpus using the Continuous Bag of Words architecture. These map the words in a language to a high dimensional real-valued vectors to capture hidden semantic and syntactic properties of words, and are typically learned from large, unannotated text corpora. For each word in the title, we obtain its equivalent Word2Vec embeddings using the model described above.
Character Level Word Embeddings
Character level word embeddings BIBREF10 capture the orthographic and morphological features of a word. Apart from this, using them is a step toward mitigating the problem of out-of-vocabulary (OoV) words. In such a case, the word can be embedded by its characters using character level embedding. We follow BIBREF5 and first initialize a vector for every character in the corpus. The vector representation of each word is learned by applying 3 layers of a 1-dimensional Convolutional Neural Network BIBREF11 with ReLU non-linearity on each vector of character sequence of that word and finally max-pooling the sequence for each convolutional feature.
Document Embeddings
Doc2Vec BIBREF12 is an unsupervised approach to generate vector representations for slightly larger bodies of text, such as sentences, paragraphs and documents. It has been adapted from Word2Vec BIBREF9 which is used to generate vectors for words in large unlabeled corpora. The vectors generated by this approach come handy in tasks like calculating similarity metrics for sentences, paragraphs and documents. In sequential models like RNNs, the word sequence is captured in the generated sentence vectors. However, in Doc2Vec, the representations are order independent. We use GenSim BIBREF13 to learn 300 dimensional Doc2Vec embeddings for each target description and post title available.
Pre-trained CNN Features
As seen in various visual understanding problems recently, image descriptors trained using Convolutional Neural Networks over large amounts of data such as ImageNet have proven to be very effective. The implicit learning of spatial layout and object semantics in the later layers of the network from very large datasets has contributed to the success of these features. We use a pre-trained network of VGG-19 architecture BIBREF14 trained over the ImageNet database (ILSVRC-2012) and extract CNN features. We use the output of the fully-connected layer (FC7), which has 4096 dimensions, as feature representations for our architecture.
We now go into detail about the components of the model, individual and combined, and how the parameters are learned.
Bidirectional LSTM with Attention
Recurrent Neural Network (RNN) is a class of artificial neural networks which utilizes sequential information and maintains history through its intermediate layers. A standard RNN has an internal state whose output at every time-step which can be expressed in terms of that of previous time-steps. However, it has been seen that standard RNNs suffer from a problem of vanishing gradients BIBREF15 . This means it will not be able to efficiently model dependencies and interactions between words that are a few steps apart. LSTMs are able to tackle this issue by their use of gating mechanisms. For each record in the dataset, the content of the post as well as the content of the related web page is available. We convert the words from the title of both attributes into the previously mentioned types of embeddings to act as input to our bidirectional LSTMs.
$(\overrightarrow{h}_1, \overrightarrow{h}_2, \dots , \overrightarrow{h}_R)$ represent forward states of the LSTM and its state updates satisfy the following equations:
$$\big [\overrightarrow{f_t},\overrightarrow{i_t},\overrightarrow{o_t}\big ] = \sigma \big [ \overrightarrow{W} \big [\overrightarrow{h}_{t-1},\overrightarrow{r_t}\big ] + \overrightarrow{b}\big ]$$   (Eq. 3)
$$\overrightarrow{l_t} = \tanh \big [\overrightarrow{V} \big [\overrightarrow{h}_{t-1}, \overrightarrow{r_t}\big ] + \overrightarrow{d}\big ]$$   (Eq. 4)
here $\sigma $ is the logistic sigmoid function, $\overrightarrow{f_t}$ , $\overrightarrow{i_t}$ , $\overrightarrow{o_t}$ represent the forget, input and output gates respectively. $\overrightarrow{r_t}$ denotes the input at time $t$ and $\overrightarrow{h_t}$ denotes the latent state, $\overrightarrow{b_t}$ and $\overrightarrow{d_t}$ represent the bias terms. The forget, input and output gates control the flow of information throughout the sequence. $\overrightarrow{W}$ and $\overrightarrow{f_t}$0 are matrices which represent the weights associated with the connections.
$(\overleftarrow{h}_1, \overleftarrow{h}_2, \dots , \overleftarrow{h}_R)$ denote the backward states and its updates can be computed similarly.
The number of bidirectional LSTM units is set to a constant K, which is the maximum length of all title lengths of records used in training. The forward and backward states are then concatenated to obtain $(h_1, h_2, \dots , h_K)$ , where
$$h_i = \begin{bmatrix} \overrightarrow{h}_i \\ \overleftarrow{h}_i \end{bmatrix}$$   (Eq. 7)
Finally, we are left with the task of figuring out the significance of each word in the sequence i.e. how much a particular word influences the clickbait-y nature of the post. The effectiveness of attention mechanisms have been proven for the task of neural machine translation BIBREF7 and it has the same effect in this case. The goal of attention mechanisms in such tasks is to derive context vectors which capture relevant source side information and help predict the current target word. The sequence of annotations generated by the encoder to come up with a context vector capturing how each word contributes to the record's clickbait quotient is of paramount importance to this model. In a typical RNN encoder-decoder framework BIBREF7 , a context vector is generated at each time-step to predict the target word. However, we only need it for calculation of context vector for a single time-step.
$$c_{attention} = \sum _{j=1}^{K}\alpha _jh_j$$   (Eq. 8)
where, $h_1$ ,..., $h_K$ represents the sequence of annotations to which the encoder maps the post title vector and each $\alpha _j$ represents the respective weight corresponding to each annotation $h_j$ . This component is represented on the leftmost in Figure 1.
Siamese Net with Text Embeddings
The second component of our model is a Siamese net BIBREF16 over two textual features in the dataset. Siamese networks are designed around having symmetry and it is important because it's required for learning a distance metric. We use them to find the similarity between the title of the record and its target description. The words in the title and in the target description are converted into their respective Doc2Vec embeddings and concatenated, after which they are considered as input into a Siamese network. A visual representation of this can be found in the middle of Figure 1.
Siamese Neural Network with Visual Embeddings
The final component of our hybrid model is also a Siamese net. However, it considers visual information available in the dataset, and sets our model apart from other approaches in this field. The relevance of the image attached to the post can be quantified by capturing its similarity with the target description. The VGG-19 architecture outputs a 4096 dimensional vector for each image which, in turn, is fed as input into a dense layer to convert each representation to a 300 dimensional vector. This serves as one input to the visual Siamese net. The target description is converted into its 300 dimensional vector representation by passing it through the pre-trained Doc2Vec model, which acts as the second input for the network. It is the rightmost part of Figure 1.
Fusion of the components
To combine the components and complete our hybrid model, the output from each of the three parts is concatenated and subsequently acts as input for a fully connected layer. This layer finally gives as its output the probability/extent that a post, together with its related information, can be considered clickbait.
Learning the Parameters
We use binary cross-entropy as the loss optimization function for our model. The cross-entropy method BIBREF17 is an iterative procedure where each iteration can be divided into two stages:
(1) Generate a random data sample (vectors, trajectories etc.) according to a specified mechanism.
(2) Update the parameters of the random mechanism based on the data to produce a "better" sample in the next iteration.
Evaluation Results
The model was evaluated over a collection of 19538 social media posts BIBREF3 , each containing supplementary information like target description, target keywords and linked images. We performed our experiments with the aim of increasing the accuracy and F1 score of the model. Other metrics like mean squared error (MSE) were also considered.
Training
We randomly partition the training set into training and validation set in a 4:1 ratio. This ensures that the two sets do not overlap. The model hyperparameters are tuned over the validation set. We initialise the fully connected network weights with the uniform distribution in the range $-\sqrt{{6}/{(fanin + fanout)}}$ and $\sqrt{{6}/{(fanin + fanout)}}$ BIBREF18 . We used a batch size of 256 and adadelta BIBREF19 as a gradient based optimizer for learning the parameters of the model.
Comparison with other models
In Table 1, we compare our model with the existing state-of-the-art for the dataset used and other models which have employed similar techniques to accomplish the task. Calculation and comparison across these metrics was conducted on TIRA BIBREF2 , a platform that offers evaluation as a service. It is clear that our proposed model outperforms the previous feature engineering benchmark and other work done in the field both in terms of F1 score and accuracy of detection.
Conclusion
In this work, we have come up with a multi-strategy approach to tackle the problem of clickbait detection across the Internet. Our model takes into account both textual and image features, a multimedia approach, to score the classify headlines. A neural attention mechanism is utilised over BIBREF5 to improve its performance, simultaneously adding Siamese nets for scoring similarity between different attributes of the post. To build on this approach, we would like to explore better image embedding techniques to better relate it to the article.

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