Identifying Clickbait: A Multi-Strategy Approach Using Neural Networks
| Autor: | Siddhartha Gairola, Yash Kumar Lal, Vaibhav Kumar, Dhruv Khattar, Vasudeva Varma |
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| Rok vydání: | 2017 |
| Předmět: |
FOS: Computer and information sciences
Computer science 0211 other engineering and technologies 050109 social psychology 02 engineering and technology Machine learning computer.software_genre Task (project management) Digital media Computer Science - Information Retrieval Computer Science - Computers and Society Computers and Society (cs.CY) 0501 psychology and cognitive sciences Social media Social and Information Networks (cs.SI) 021103 operations research Computer Science - Computation and Language Artificial neural network business.industry 05 social sciences Computer Science - Social and Information Networks Artificial intelligence F1 score business computer Computation and Language (cs.CL) Word (computer architecture) Information Retrieval (cs.IR) |
| DOI: | 10.48550/arxiv.1710.01507 |
| Popis: | 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. Comment: Accepted at SIGIR 2018 as Short Paper |
| Databáze: | OpenAIRE |
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