Predicting Clickbait Strength in Online Social Media

Indurthi, Vijayasaradhi; Syed, Bakhtiyar; Gupta, Manish; Varma, Vasudeva

doi:10.18653/v1/2020.coling-main.425

Cited by 8 publications

(5 citation statements)

References 33 publications

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“…Article [17] approach is to treat the clickbait problem as a regression one to predict the intensity of the clickbait. The dataset used was the one provided by The Clickbait Challenge, and they are proud to say that their method is the best compared to The Clickbait Challenge results.…”

Section: Related Workmentioning

confidence: 99%

“…The related work analysis reflects that the most frequently applied intelligent classification algorithms are: Convolutional Neural Networks [18], LSTM (Long Short Term Memory), biGRU networks [14], Gradient Boosted Decision Trees [6], Random Forest, Logistic Regression, Naive Bayes [9] and Support Vector Machine. Considering this analysis, we chose to use Naive Bayes [9], Logistic Regression [9], [17], [31], Decision Tree [6], [7], Random Forest [9], [31], [7], [15], [17] and Support Vector Machine [15], [7].…”

Section: Clickbait Detection Strategymentioning

confidence: 99%

See 1 more Smart Citation

Advances in Clickbait and Fake News Detection Using New Language-independent Strategies

Coste

Bufnea

2021

JCOMSS

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Online publishers rely on different techniques to trap web visitors, clickbait being one such technique. Besides being a bad habit, clickbait is also a strong indicator for fake news spreading. Its presence in online media leads to an overall bad browsing experience for the web consumer. Recently, big players on the Internet scene, such as search engines and social networks, have turned their attention towards this negative phenomenon that is increasingly present in our everyday browsing experience. The research community has also joined in this effort, a broad band of detection techniques being developed. These techniques are usually based on intelligent classifiers, for which feature selection is of great importance. The work presented in this paper brings our own contributions to the field of clickbait detection. We present a new language-independent strategy for clickbait detection that takes into consideration only features that are general enough to be independent of any particular language. The methods presented in this paper could be applied to web content written in different languages. In addition, we present the results of a complex experiment that we performed to evaluate our proposed method and we compare our results with the most relevant results previously obtained in the field.

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Section: Related Workmentioning

confidence: 99%

Section: Clickbait Detection Strategymentioning

confidence: 99%

Advances in Clickbait and Fake News Detection Using New Language-independent Strategies

Coste

Bufnea

2021

JCOMSS

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“…Owing to a lack of knowledge about the subject, students get a low grade in the class. Students mostly use slang phrases and shorten the shape of phrases during school, and these phrases are often used in class and on paper (Indurthi, Syed, Gupta, & Varma, 2020). Tik-Tok is a big website that influences students' academic performance as they use Tik-Tok more on the Internet.…”

Section: Time Wastagementioning

confidence: 99%

RELATIONSHIP BETWEEN ONLINE SOCIAL NETWORKING SITES & APPLICATIONS AND UNIVERSITY STUDENTS’ ACHIEVEMENT

Haider

Munawar

Bibi

et al. 2021

HSSR

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Purpose of the study: This study analysed the relationship between Online Social Networking websites and Applications (OSNSA) and university students' academic achievement. Methodology: In the present research, a quantitative research design was followed, and a survey research type of descriptive research was used in this study. Data were collected from 150 university students using a modified version of The Online Social Networking Sites scale (Paul, Baker, & Cochran, 2012). The correlation was calculated by using the SPSS 22nd version. Main Findings: The findings revealed a connection between students' time spent on OSNSA and their academic outcomes. The time spent on OSNSA was observed to be strongly influenced by the attention span of the students. In particular, we find that a lower academic output is provided by the greater time spent on OSNSA. Also, attention span was strongly associated with features that influence students' academic success, such as their perceptions of society's social networking outlook, their advantages, and disadvantages of OSNSA, use of OSNSA, etc. Applications of this study: Many students spend most of their hours on online networking pages, like Facebook, Tok-Tok, and WhatsApp, which are the most common, considering the harmful impact of social media. In certain eyes, social media has embedded a detrimental effect, but it is also used to acquire vital knowledge and social skills for pupils. Novelty/Originality of this study: This research is different from other studies because it explores the relationship between social networking sites & the application and academic performance of university students belonging from South Punjab by exploring various facets of OSNSA which ultimately promote university students’ academic performance.

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“…Using a method called Stylized Headline Generation (SHG) Framework. Other research also predicts the strength of clickbait in online social media [12]. This research modelled clickbait strength prediction as a regression problem.…”

Section: Introductionmentioning

confidence: 99%

Clickbait Classification Model on Online News with Semantic Similarity Calculation Between News Title and Content

Ahmadi

Chowanda

2023

bits

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Clickbait is a sensational title that makes us click internet links to an article, image, or video. Online content providers use clickbait to gain user traffic, that leads to increasing income from the placed ads in their page. To attract more and more traffic, online content providers write sensational and hyperbolic titles, and even misleading and not telling the whole story. This can give us, the internet consumer, wrong perspective, and half-truth. And nowadays, clickbait titles are worse than ever. Modern clickbait titles are not hyperbolic nor ambiguous enough, and sometimes very hard to identify. This paper aims to classify clickbait titles, to help humans identify clickbait and stop sharing more online content that contains clickbait and misleading titles. This model classifies clickbait by calculating semantic similarity between the article title and the summary of the article content. The article content is summarized by T5 (Text-to-text Transfer Transformer) model. IndoBERT is then used to calculate semantic similarity score between generated summary and the article title. The article title, content, summary, and semantic similarity score are used for clickbait classification with various algorithms. The result shows that by adding article content alongside article title in the classification process improves F1-score by 7% when classified with IndoBERT. In another future research, this model can be integrated with another application such as twitter or telegram bot to send us warning every time a user consumes online content with clickbait title. Thus, it can prevent online communities from sharing misleading information caused by clickbait

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Predicting Clickbait Strength in Online Social Media

Cited by 8 publications

References 33 publications

Advances in Clickbait and Fake News Detection Using New Language-independent Strategies

Advances in Clickbait and Fake News Detection Using New Language-independent Strategies

RELATIONSHIP BETWEEN ONLINE SOCIAL NETWORKING SITES & APPLICATIONS AND UNIVERSITY STUDENTS’ ACHIEVEMENT

Clickbait Classification Model on Online News with Semantic Similarity Calculation Between News Title and Content

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