How Curiosity can be modeled for a Clickbait Detector

Venneti, Lasya; Alam, Aniket

doi:10.48550/arxiv.1806.04212

Cited by 2 publications

(3 citation statements)

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“…We suspect that the task of distinguishing between clickbait and non-clickbait headlines is related to pull quote extraction because both tasks may rely on identifying the catchiness of a span of text. In (Venneti and Alam, 2018), the authors found that measures of topic novelty (estimated using LDA) and surprise (based on word bigram frequency) were strong features for detecting clickbait. A set of 215 features were considered in (Potthast et al, 2016) including sentiment, length statistics, and many features based on specialized dictionary-based word occurrences, but the authors found that the most successful features were character and word n-grams.…”

Section: Clickbait Identificationmentioning

confidence: 99%

“…The problem in this work of automatically selecting PQs is distinct from, but related to previously studied problems of headline success prediction (Piotrkowicz et al, 2017;Lamprinidis et al, 2018), clickbait identification (Potthast et al, 2016;Chakraborty et al, 2016;Venneti and Alam, 2018), as well as key phrase extraction (Hasan and Ng, 2014) and document summarization (Nenkova and McKeown, 2012). In the context of convincing a reader to engage in a text, the title tells the reader what the article is about and sets the tone, clickbait makes (often unwarranted) lofty promises of what the article is about, and key phrases and summaries indicate whether the topic or constituent components are of interest to the user.…”

Section: Introductionmentioning

confidence: 99%

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Catching Attention with Automatic Pull Quote Selection

Bohn¹,

Ling²

2020

Preprint

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Pull quotes are an effective component of a captivating news article. These spans of text are selected from an article and provided with more salient presentation, with the aim of attracting readers with intriguing phrases and making the article more visually interesting. In this paper, we introduce the novel task of automatic pull quote selection, construct a dataset, and benchmark the performance of a number of approaches ranging from hand-crafted features to state-of-the-art sentence embeddings to cross-task models. We show that pre-trained Sentence-BERT embeddings outperform all other approaches, however the benefit over n-gram models is marginal. By closely examining the results of simple models, we also uncover many unexpected properties of pull quotes that should serve as inspiration for future approaches. We believe the benefits of exploring this problem further are clear: pull quotes have been found to increase enjoyment and readability, shape reader perceptions, and facilitate learning.

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Section: Clickbait Identificationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Catching Attention with Automatic Pull Quote Selection

Bohn¹,

Ling²

2020

Preprint

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“…A study found that human curiosity takes part in detecting clickbait, such with high curiosity, people are more likely to get trapped on clickbait news. The connection between curiosity and clickbait persisted on all age [8]. Indonesian is mostly curious people, the high engagement on celebrity related news and overly friendly Indonesian culture, which often followed by overly curious question (or kepo), one of the examples ISSN: 2252-8938  is how Indonesian really like to dig into other's private lives, such as asking why a couple still haven't had kids or so.…”

Section: Introductionmentioning

confidence: 99%

CekUmpanKlik: an artificial intelligence-based application to detect Indonesian clickbait

Fakhruzzaman

Gunawan

2022

IJ-AI

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This study attempted to deploy a high performing natural language processing model which specifically trained on flagging clickbait Indonesian news headline. The deployed model is accessible from any internet-connected device because it implements representational state transfer application programming interface (RESTful API). The application is useful to avoid clickbait news which often solely purposed to rack money but not delivering trustworthy news. With many online news outlets adopting the click-based advertising, clickbait headline become ubiquitous. Thus, newsworthy articles often cluttered with clickbait news. Leveraging state-of-the-art bidirectional encoder representation from transformers (BERT), a lightweight web application is developed. This study offloaded the computing resources needed to train the model on a separate instance of virtual server and then deployed the trained model on the cloud, while the client-side application only needs to send a request to the API and the cloud server will handle the rest, often known as three-layer architecture. This study designed and developed a web-based application to detect clickbait in Indonesian using IndoBERT as its language model. The application usage and potentials were discussed. The source code and running application are available for public with a performance of mean receiver operating characteristic-area under the curve (ROC-AUC) of 89%.

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How Curiosity can be modeled for a Clickbait Detector

Cited by 2 publications

References 0 publications

Catching Attention with Automatic Pull Quote Selection

Catching Attention with Automatic Pull Quote Selection

CekUmpanKlik: an artificial intelligence-based application to detect Indonesian clickbait

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