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.
It was not until @cite_0 that neural networks were tried out for the task as the authors used the same news dataset as @cite_6 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, @cite_11 has also experimented with Twitter data @cite_16 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 @cite_1 , the attention mechanism has been used for a variety of text-classification tasks, such as fake news detection and aspect-based sentiment analysis. @cite_8 used a self-attentive BiGRU to infer the importance of tweet tokens in predicting the annotation distribution of the task.
@cite_8 @cite_1 @cite_6 @cite_0 @cite_16 @cite_11
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Clickbait detection in tweets remains an elusive challenge. In this paper, we describe the solution for the Zingel Clickbait Detector at the Clickbait Challenge 2017, which is capable of evaluating each tweet's level of click baiting. We first reformat the regression problem as a multi-classification problem, based on the annotation scheme. To perform multi-classification, we apply a token-level, self-attentive mechanism on the hidden states of bi-directional Gated Recurrent Units (biGRU), which enables the model to generate tweets' task-specific vector representations by attending to important tokens. The self-attentive neural network can be trained end-to-end, without involving any manual feature engineering. Our detector ranked first in the final evaluation of Clickbait Challenge 2017. Targeted sentiment analysis classifies the sentiment polarity towards each target entity mention in given text documents. Seminal methods extract manual discrete features from automatic syntactic parse trees in order to capture semantic information of the enclosing sentence with respect to a target entity mention. Recently, it has been shown that competitive accuracies can be achieved without using syntactic parsers, which can be highly inaccurate on noisy text such as tweets. This is achieved by applying distributed word representations and rich neural pooling functions over a simple and intuitive segmentation of tweets according to target entity mentions. In this paper, we extend this idea by proposing a sentence-level neural model to address the limitation of pooling functions, which do not explicitly model tweet-level semantics. First, a bi-directional gated neural network is used to connect the words in a tweet so that pooling functions can be applied over the hidden layer instead of words for better representing the target and its contexts. Second, a three-way gated neural network structure is used to model the interaction between the target mention and its surrounding contexts. Experiments show that our proposed model gives significantly higher accuracies compared to the current best method for targeted sentiment analysis. We are interested in organizing a continuous stream of sparse and noisy texts, known as "tweets", in real time into an ontology of hundreds of topics with measurable and stringently high precision. This inference is performed over a full-scale stream of Twitter data, whose statistical distribution evolves rapidly over time. The implementation in an industrial setting with the potential of affecting and being visible to real users made it necessary to overcome a host of practical challenges. We present a spectrum of topic modeling techniques that contribute to a deployed system. These include non-topical tweet detection, automatic labeled data acquisition, evaluation with human computation, diagnostic and corrective learning and, most importantly, high-precision topic inference. The latter represents a novel two-stage training algorithm for tweet text classification and a close-loop inference mechanism for combining texts with additional sources of information. The resulting system achieves 93 precision at substantial overall coverage. This paper describes our deep learning system for sentiment analysis of tweets. The main contribution of this work is a new model for initializing the parameter weights of the convolutional neural network, which is crucial to train an accurate model while avoiding the need to inject any additional features. Briefly, we use an unsupervised neural language model to train initial word embeddings that are further tuned by our deep learning model on a distant supervised corpus. At a final stage, the pre-trained parameters of the network are used to initialize the model. We train the latter on the supervised training data recently made available by the official system evaluation campaign on Twitter Sentiment Analysis organized by Semeval-2015. A comparison between the results of our approach and the systems participating in the challenge on the official test sets, suggests that our model could be ranked in the first two positions in both the phrase-level subtask A (among 11 teams) and on the message-level subtask B (among 40 teams). This is an important evidence on the practical value of our solution.