2021
DOI: 10.3390/app11209487
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An Improved Multiple Features and Machine Learning-Based Approach for Detecting Clickbait News on Social Networks

Abstract: The widespread usage of social media has led to the increasing popularity of online advertisements, which have been accompanied by a disturbing spread of clickbait headlines. Clickbait dissatisfies users because the article content does not match their expectation. Detecting clickbait posts in online social networks is an important task to fight this issue. Clickbait posts use phrases that are mainly posted to attract a user’s attention in order to click onto a specific fake link/website. That means clickbait … Show more

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Cited by 15 publications
(11 citation statements)
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“…After pre-processing, the text must be converted into an understandable form. Hybrid and multiple features selection are trends in the literature of sentiment analysis research as in [53,54], In this research, the following methods were explored for feature selection and extraction.…”
Section: Feature Selection and Extractionmentioning
confidence: 99%
“…After pre-processing, the text must be converted into an understandable form. Hybrid and multiple features selection are trends in the literature of sentiment analysis research as in [53,54], In this research, the following methods were explored for feature selection and extraction.…”
Section: Feature Selection and Extractionmentioning
confidence: 99%
“…As each annotator assigned a sentimental score separately, we followed the same guideline that was used in [32,33].…”
Section: Building Ground-truth Datasetmentioning
confidence: 99%
“…They might contain unnecessary data, which can negatively affect the performance of ML classifiers. This makes preprocessing of the data a very important step for improving the performance of the ML models and reducing the training time [32,34,35]. In addition, the size of the featured set can be reduced from 50% to 30%, as stated by the authors of [36].…”
Section: Data Preprocessingmentioning
confidence: 99%
“…The existing software for detecting XSS vulnerabilities, such as the Online Web Security Scanner, search for these vulnerabilities only in the open parts of websites that do not require access authorisation [17][18][19][20][21]. Such a drawback is significant considering that XSS vulnerabilities may be located in an unsearchable part of a web resource.…”
Section: Xss Vulnerabilities Detection Softwarementioning
confidence: 99%