相关论文: Machine Learning Based Detection of Clickbait Post…

Clickbait is a pejorative term describing web content that is aimed at generating online advertising revenue, especially at the expense of quality or accuracy, relying on sensationalist headlines or eye-catching thumbnail pictures to…

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…

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…

Online news media sometimes use misleading headlines to lure users to open the news article. These catchy headlines that attract users but disappointed them at the end, are called Clickbaits. Because of the importance of automatic clickbait…

This paper presents the results and conclusions of our participation in the Clickbait Challenge 2017 on automatic clickbait detection in social media. We first describe linguistically-infused neural network models and identify informative…

The purpose of a clickbait is to make a link so appealing that people click on it. However, the content of such articles is often not related to the title, shows poor quality, and at the end leaves the reader unsatisfied. To help the…

In this paper, we propose an approach for the detection of clickbait posts in online social media (OSM). Clickbait posts are short catchy phrases that attract a user's attention to click to an article. The approach is based on a machine…

Clickbait has grown to become a nuisance to social media users and social media operators alike. Malicious content publishers misuse social media to manipulate as many users as possible to visit their websites using clickbait messages.…

The use of alluring headlines (clickbait) to tempt the readers has become a growing practice nowadays. For the sake of existence in the highly competitive media industry, most of the on-line media including the mainstream ones, have started…

The emergence of social media as news sources has led to the rise of clickbait posts attempting to attract users to click on article links without informing them on the actual article content. This paper presents our efforts to create a…

Online content publishers often use catchy headlines for their articles in order to attract users to their websites. These headlines, popularly known as clickbaits, exploit a user's curiosity gap and lure them to click on links that often…

The proliferation of clickbait headlines poses significant challenges to the credibility of information and user trust in digital media. While recent advances in machine learning have improved the detection of manipulative content, the lack…

Clickbait, which aims to induce users with some surprising and even thrilling headlines for increasing click-through rates, permeates almost all online content publishers, such as news portals and social media. Recently, Large Language…

In order to expand their reach and increase website ad revenue, media outlets have started using clickbait techniques to lure readers to click on articles on their digital platform. Having successfully enticed the user to open the article,…

Unscrupulous content creators on YouTube employ deceptive techniques such as spam and clickbait to reach a broad audience and trick users into clicking on their videos to increase their advertisement revenue. Clickbait detection on YouTube…

Clickbaits are surprising social posts or deceptive news headlines that attempt to lure users for more clicks, which have posted at unprecedented rates for more profit or commercial revenue. The spread of clickbait has significant negative…

Clickbait headlines degrade the quality of online information and undermine user trust. We present a hybrid approach to clickbait detection that combines transformer-based text embeddings with linguistically motivated informativeness…

We study feature selection as a means to optimize the baseline clickbait detector employed at the Clickbait Challenge 2017. The challenge's task is to score the "clickbaitiness" of a given Twitter tweet on a scale from 0 (no clickbait) to 1…

Click counts are related to the amount of money that online advertisers paid to news sites. Such business models forced some news sites to employ a dirty trick of click-baiting, i.e., using a hyperbolic and interesting words, sometimes…

Most of the online news media outlets rely heavily on the revenues generated from the clicks made by their readers, and due to the presence of numerous such outlets, they need to compete with each other for reader attention. To attract the…