A deep learning framework for clickbait detection on social area network using natural language cues

Naeem, Bilal; Khan, Aymen; Beg, Mirza Omer; Mujtaba, Hasan

doi:10.1007/s42001-020-00063-y

Cited by 33 publications

(17 citation statements)

References 10 publications

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“…The main difference between these two types of headlines is the use of functional linguistic characteristics such as wondering, exaggerating, and questioning. In [9], two types of characteristics were used: general clickbait, and the type-related characteristics, while the main characteristics used by Naeem et al [10] for detection of clickbait were sensationalism, mystery, notions of curiosity, and shock.…”

Section: Characteristics Of Clickbait Newsmentioning

confidence: 99%

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An Improved Multiple Features and Machine Learning-Based Approach for Detecting Clickbait News on Social Networks

et al. 2021

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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 headlines utilize misleading titles, which could carry hidden important information from the target website. It is very difficult to recognize these clickbait headlines manually. Therefore, there is a need for an intelligent method to detect clickbait and fake advertisements on social networks. Several machine learning methods have been applied for this detection purpose. However, the obtained performance (accuracy) only reached 87% and still needs to be improved. In addition, most of the existing studies were conducted on English headlines and contents. Few studies focused specifically on detecting clickbait headlines in Arabic. Therefore, this study constructed the first Arabic clickbait headline news dataset and presents an improved multiple feature-based approach for detecting clickbait news on social networks in Arabic language. The proposed approach includes three main phases: data collection, data preparation, and machine learning model training and testing phases. The collected dataset included 54,893 Arabic news items from Twitter (after pre-processing). Among these news items, 23,981 were clickbait news (43.69%) and 30,912 were legitimate news (56.31%). This dataset was pre-processed and then the most important features were selected using the ANOVA F-test. Several machine learning (ML) methods were then applied with hyper-parameter tuning methods to ensure finding the optimal settings. Finally, the ML models were evaluated, and the overall performance is reported in this paper. The experimental results show that the Support Vector Machine (SVM) with the top 10% of ANOVA F-test features (user-based features (UFs) and content-based features (CFs)) obtained the best performance and achieved 92.16% of detection accuracy.

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Section: Characteristics Of Clickbait Newsmentioning

confidence: 99%

“…The obtained performance needs to be improved. [10] Dataset of head-lines from Reddit,. The datasets includes 16,000 legitimate news and 16,000 clickbait samples.…”

Section: Problem Formulation For Clickbait Detectionmentioning

confidence: 99%

An Improved Multiple Features and Machine Learning-Based Approach for Detecting Clickbait News on Social Networks

et al. 2021

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“…For example, in their recent paper Souma and colleagues [39] apply recurrent neural network (RNN) with long short-term memory (LSTM) units to forecast financial news sentiments. In another study, Naeem and colleagues [30] use a novel deep learning framework for clickbait detection on social area network. The application of deep learning methods is more technically demanding and may present an additional barrier to the wide use of UGC in public opinion research among social scientists.…”

Section: User-generated Online Datamentioning

confidence: 99%

Analyzing user-generated content using natural language processing: a case study of public satisfaction with healthcare systems

Ruelens

2021

J Comput Soc Sc

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“…[20] has worked for emotion detection in roman Urdu as these days people express their emotions on social media by posting and sharing pictures and statuses. Some work has been done for emotion detection but in roman [21] has presented a technique for detecting fake news, yellow journalism, and social media as these days it is very easy to post or share fake news and there is a huge amount of this kind of contents so to manage it intelligently there is a need for a model that detects these kinds of news and sensationalism. They have obtained an accuracy of 97% by using Long Short-Term Memory (LSTM).…”

Section: Related Workmentioning

confidence: 99%

Urdu Handwritten Text Recognition Using ResNet18

Kashif

2021

Preprint

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Handwritten text recognition is an active research area in the field of deep learning and artificial intelligence to convert handwritten text into machine-understandable. A lot of work has been done for other languages, especially for English, but work for the Urdu language is very minimal due to the cursive nature of Urdu characters. The need for Urdu HCR systems is increasing because of the advancement of technology. In this paper, we propose a ResNet18 model for handwritten text recognition using Urdu Nastaliq Handwritten Dataset (UNHD) which contains 3,12000 words written by 500 candidates.

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A deep learning framework for clickbait detection on social area network using natural language cues

Cited by 33 publications

References 10 publications

An Improved Multiple Features and Machine Learning-Based Approach for Detecting Clickbait News on Social Networks

An Improved Multiple Features and Machine Learning-Based Approach for Detecting Clickbait News on Social Networks

Analyzing user-generated content using natural language processing: a case study of public satisfaction with healthcare systems

Urdu Handwritten Text Recognition Using ResNet18

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