Similarity-Aware Deep Attentive Model for Clickbait Detection

Dong, Manqing; Yao, Lina; Wang, Xianzhi; Benatallah, Boualem; Huang, Chaoran

doi:10.1007/978-3-030-16145-3_5

Cited by 27 publications

(51 citation statements)

References 15 publications

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“…Such similarity is assumed to be positively correlated to the sensationalism and negatively correlated to the newsworthiness of news headlines. Both figures reveal that, in general, fake news headlines are less similar to their bodytext compared to true news headlines, which matches with the characteristic of clickbaits [13]. • Figures in the middle column present the box-plot of the average sentiment score of words within a news headline.…”

Section: E4mentioning

confidence: 74%

“…• Similarity. Similarity between the headline of a news article and its body-text is assumed to be positively correlated to the degree of relative sensationalism expressed in the news headline [13]. Capturing such similarity requires first embedding the headline and body-text for each news article into the same space.…”

Section: Semantic-levelmentioning

confidence: 99%

“…• I. Quality: The title of high-quality news articles is often a summary of the whole news event described in the body-text [13]. To capture this property, one can assess the similarity between the headline of a news article and its body-text, which has been already captured when analyzing sensationalism.…”

Section: Semantic-levelmentioning

confidence: 99%

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Fake News Early Detection

et al. 2020

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Massive dissemination of fake news and its potential to erode democracy has increased the demand for accurate fake news detection. Recent advancements in this area have proposed novel techniques that aim to detect fake news by exploring how it propagates on social networks. Nevertheless, to detect fake news at an early stage, i.e., when it is published on a news outlet but not yet spread on social media, one cannot rely on news propagation information as it does not exist. Hence, there is a strong need to develop approaches that can detect fake news by focusing on news content. In this article, a theorydriven model is proposed for fake news detection. The method investigates news content at various levels: lexicon-level, syntax-level, semantic-level, and discourse-level. We represent news at each level, relying on well-established theories in social and forensic psychology. Fake news detection is then conducted within a supervised machine learning framework. As an interdisciplinary research, our work explores potential fake news patterns, enhances the interpretability in fake news feature engineering, and studies the relationships among fake news, deception/disinformation, and clickbaits. Experiments conducted on two real-world datasets indicate the proposed method can outperform the state-of-the-art and enable fake news early detection when there is limited content information. CCS Concepts: • Human-centered computing → Collaborative and social computing theory, concepts and paradigms; • Computing methodologies → Natural language processing; Machine learning; • Security and privacy → Social aspects of security and privacy; • Applied computing → Sociology; Computer forensics;

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Section: E4mentioning

confidence: 74%

Section: Semantic-levelmentioning

confidence: 99%

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Fake News Early Detection

et al. 2020

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“…Tests were carried out to determine the performance of the model. c. State of The Art Dong et al [8] analyzed the similarities between the title and content of a story. This study used a deep learning approach that is carried out through the following stages: 1) Analysis of latent representation At this stage, researchers conducted data preprocessing and transformed it into vectors (word embedding) with BiGRU used to determine hidden representations of the hidden layer.…”

Section: ) Testingmentioning

confidence: 99%

“…A recent study on the detection of clickbait was conducted by Dong et al [8]. The study measured the similarity/consistency between titles and news.…”

Section: Introductionmentioning

confidence: 99%

Clickbait detection: A literature review of the methods used

Zuhroh

Rakhmawati

2019

register. jurnal. ilm. teknologi. sistem. inf.

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Online news portals are currently one of the fastest sources of information used by people. Its impact is due to the credibility of the news produced by actors from the media industry, which is sometimes questioned. However, one of the problems associated with this medium used to obtain information is clickbait. This technique aims to attract users to click hyperbolic headlines with content that often disappoints the reader. This study was, therefore, conducted to determine: 1) existing dataset available. 2) The method used in clickbait detection which consists of data preprocessing, analysis of features, and classification. 3) Difference steps from the method used.

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Clickbait Detection with Style-Aware Title Modeling and Co-attention

et al. 2020

Lecture Notes in Computer Science

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Clickbait is a form of web content designed to attract attention and entice users to click on specific hyperlinks. The detection of clickbaits is an important task for online platforms to improve the quality of web content and the satisfaction of users. Clickbait detection is typically formed as a binary classification task based on the title and body of a webpage, and existing methods are mainly based on the content of title and the relevance between title and body. However, these methods ignore the stylistic patterns of titles, which can provide important clues on identifying clickbaits. In addition, they do not consider the interactions between the contexts within title and body, which are very important for measuring their relevance for clickbait detection. In this paper, we propose a clickbait detection approach with style-aware title modeling and coattention. Specifically, we use Transformers to learn content representations of title and body, and respectively compute two content-based clickbait scores for title and body based on their representations. In addition, we propose to use a character-level Transformer to learn a style-aware title representation by capturing the stylistic patterns of title, and we compute a title stylistic score based on this representation. Besides, we propose to use a co-attention network to model the relatedness between the contexts within title and body, and further enhance their representations by encoding the interaction information. We compute a title-body matching score based on the representations of title and body enhanced by their interactions. The final clickbait score is predicted by a weighted summation of the aforementioned four kinds of scores. Extensive experiments on two benchmark datasets show that our approach can effectively improve the performance of clickbait detection and consistently outperform many baseline methods.

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Similarity-Aware Deep Attentive Model for Clickbait Detection

Cited by 27 publications

References 15 publications

Fake News Early Detection

Fake News Early Detection

Clickbait detection: A literature review of the methods used

Clickbait Detection with Style-Aware Title Modeling and Co-attention

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