Hybridizing metric learning and case-based reasoning for adaptable clickbait detection

López-Sánchez, Daniel; Herrero, Juan F.; Arrieta, Angélica González; Corchado, Juan M.

doi:10.1007/s10489-017-1109-7

Cited by 27 publications

(23 citation statements)

References 18 publications

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“…A lot of work has been done to combat the clickbait titles. Some tools are available in different leading media sites, which automatically block such articles [8,9]. Bauhaus-Universität Weimar organised a clickbait challenge 1 to detect clickbait by providing their datasets, which draws a lot of attraction in this domain of research.…”

Section: Introductionmentioning

confidence: 99%

Clickbait detection using multiple categorisation techniques

Pujahari

Sisodia

2019

Journal of Information Science

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Clickbaits are online articles with deliberately designed misleading titles for luring more and more readers to open the intended web page. Clickbaits are used to tempted visitors to click on a particular link either to monetize the landing page or to spread the false news for sensationalization. The presence of clickbaits on any news aggregator portal may lead to unpleasant experience to readers. Automatic detection of clickbait headlines from news headlines has been a challenging issue for the machine learning community. A lot of methods have been proposed for preventing clickbait articles in recent past. However, the recent techniques available in detecting clickbaits are not much robust. This paper proposes a hybrid categorization technique for separating clickbait and non-clickbait articles by integrating different features, sentence structure, and clustering. During preliminary categorization, the headlines are separated using eleven features. After that, the headlines are recategorized using sentence formality, syntactic similarity measures. In the last phase, the headlines are again recategorized by applying clustering using word vector similarity based on t-Stochastic Neighbourhood Embedding (t-SNE) approach. After categorization of these headlines, machine learning models are applied to the data set to evaluate machine learning algorithms. The obtained experimental results indicate the proposed hybrid model is more robust, reliable and efficient than any individual categorization techniques for the real-world dataset we used.

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Section: Introductionmentioning

confidence: 99%

Clickbait detection using multiple categorisation techniques

Pujahari

Sisodia

2019

Journal of Information Science

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“…A deep generative variational autoencoder model was used for classification clickbaits (Zannettou, Chatzis, Papadamou, & Sirivianos, 2018). A new deep learning and metric learning based hybrid techniques integrated with a case based reasoning methodology are proposed for adaptable clickbait detection (López-Sánchez, Herrero, Arrieta, & Corchado, 2018). A deep generative model (Liu, Le, Shu, Wang, & Lee, 2018) is proposed to address the issue of non-availability of large scale labeled data required to train the supervised learning models.…”

Section: Related Workmentioning

confidence: 99%

Ensemble Learning Approach for Clickbait Detection Using Article Headline Features

Sisodia

2019

InformingSciJ

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Aim/Purpose: The aim of this paper is to propose an ensemble learners based classification model for classification clickbaits from genuine article headlines. Background: Clickbaits are online articles with deliberately designed misleading titles for luring more and more readers to open the intended web page. Clickbaits are used to tempted visitors to click on a particular link either to monetize the landing page or to spread the false news for sensationalization. The presence of clickbaits on any news aggregator portal may lead to an unpleasant experience for readers. Therefore, it is essential to distinguish clickbaits from authentic headlines to mitigate their impact on readers’ perception. Methodology: A total of one hundred thousand article headlines are collected from news aggregator sites consists of clickbaits and authentic news headlines. The collected data samples are divided into five training sets of balanced and unbalanced data. The natural language processing techniques are used to extract 19 manually selected features from article headlines. Contribution: Three ensemble learning techniques including bagging, boosting, and random forests are used to design a classifier model for classifying a given headline into the clickbait or non-clickbait. The performances of learners are evaluated using accuracy, precision, recall, and F-measures. Findings: It is observed that the random forest classifier detects clickbaits better than the other classifiers with an accuracy of 91.16 %, a total precision, recall, and f-measure of 91 %.

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“…The learned embeddings from the embedding layer should not be confused with the embeddings that Glove [26] or word2vec [27] learn. These related embeddings are trained to capture semantic similarity whilst the Embedding layer in this work outputs embeddings that are configured purely for classification purposes on the dataset itself [28].…”

Section: -D Cnn Architecturementioning

confidence: 99%

Explaining Sentiment Classification

et al. 2019

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This paper presents a novel 1-D sentiment classifier trained on the benchmark IMDB dataset. The classifier is a 1-D convolutional neural network with repeated convolution and max pooling layers. The main contribution of this work is the demonstration of a deconvolution technique for 1-D convolutional neural networks that is agnostic to specific architecture types. This deconvolution technique enables text classification to be explained, a feature that is important for NLP-based decision support systems, as well as being an invaluable diagnostic tool.

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Hybridizing metric learning and case-based reasoning for adaptable clickbait detection

Cited by 27 publications

References 18 publications

Clickbait detection using multiple categorisation techniques

Clickbait detection using multiple categorisation techniques

Ensemble Learning Approach for Clickbait Detection Using Article Headline Features

Explaining Sentiment Classification

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