Tabloids in the Era of Social Media? Understanding the Production and Consumption of Clickbaits in Twitter

Chakraborty, Abhijnan; Sarkar, Rajdeep; Mrigen, Ayushi; Ganguly, Niloy

doi:10.2139/ssrn.3034591

Cited by 20 publications

(12 citation statements)

References 20 publications

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“…Stroud et al [15] analyzed demographics, attitudes and behaviors of user population who comment on different sections. Similar analysis has also been done by Chakraborty et al [16,17] for social media posts. Mullick et al [18,19] classified online comments into opinion and fact and respective subcategories.…”

Section: Related Worksupporting

confidence: 55%

Public Sphere 2.0: Targeted Commenting in Online News Media

Mullick

Ghosh

Dutt

et al. 2019

Lecture Notes in Computer Science

Self Cite

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With the increase in online news consumption, to maximize advertisement revenue, news media websites try to attract and retain their readers on their sites. One of the most effective tools for reader engagement is commenting, where news readers post their views as comments against the news articles. Traditionally, it has been assumed that the comments are mostly made against the full article. In this work, we show that present commenting landscape is far from this assumption. Because the readers lack the time to go over an entire article, most of the comments are relevant to only particular sections of an article. In this paper, we build a system which can automatically classify comments against relevant sections of an article. To implement that, we develop a deep neural network based mechanism to find comments relevant to any section and a paragraph wise commenting interface to showcase them. We believe that such a data driven commenting system can help news websites to further increase reader engagement.

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Section: Related Worksupporting

confidence: 55%

Public Sphere 2.0: Targeted Commenting in Online News Media

Mullick

Ghosh

Dutt

et al. 2019

Lecture Notes in Computer Science

Self Cite

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“…Due to the length of news headlines, here the frequency of each clickbait phrase or expression is not considered in our feature set as it leads to many zeros in our feature matrix. Such dictionaries have been successfully applied in clickbait detection [9,21,42].…”

Section: Semantic-levelmentioning

confidence: 99%

Fake News Early Detection

et al. 2020

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Massive dissemination of fake news and its potential to erode democracy has increased the demand for accurate fake news detection. Recent advancements in this area have proposed novel techniques that aim to detect fake news by exploring how it propagates on social networks. Nevertheless, to detect fake news at an early stage, i.e., when it is published on a news outlet but not yet spread on social media, one cannot rely on news propagation information as it does not exist. Hence, there is a strong need to develop approaches that can detect fake news by focusing on news content. In this article, a theorydriven model is proposed for fake news detection. The method investigates news content at various levels: lexicon-level, syntax-level, semantic-level, and discourse-level. We represent news at each level, relying on well-established theories in social and forensic psychology. Fake news detection is then conducted within a supervised machine learning framework. As an interdisciplinary research, our work explores potential fake news patterns, enhances the interpretability in fake news feature engineering, and studies the relationships among fake news, deception/disinformation, and clickbaits. Experiments conducted on two real-world datasets indicate the proposed method can outperform the state-of-the-art and enable fake news early detection when there is limited content information. CCS Concepts: • Human-centered computing → Collaborative and social computing theory, concepts and paradigms; • Computing methodologies → Natural language processing; Machine learning; • Security and privacy → Social aspects of security and privacy; • Applied computing → Sociology; Computer forensics;

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“…Apesar de as matérias "caça-clique" serem definidas pelo aspecto de chamada de atenção para a leitura, tal prática de edição de texto com vistas à ação de seleção pela audiência acontece em variadas plataformas utilizadas por canais jornalísticos, como mídias sociais. Nestes ambientes, o esforço por atrair o usuário também trabalha com gatilhos que despertam atenção, curiosidade ou envolvimento emocional (CHAKRABORTY et al, 2017).…”

Section: Considerações Finaisunclassified

Fragmentação visual e serialização na narrativa informacional de sites caça-cliques

Klein

2021

Disp

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O texto analisa o modo como a narrativa é apropriada para as lógicas de leitura em sites de caça-clique, pela fragmentação do conteúdo, imagem como componente informativo e emocional (e gatilho para a ação de clique pelo usuário), hiperligação de páginas. Para a análise, consideramos a repercussão em um site caça-clique da notícia da recuperação de tartarugas nativas que eram mantidas aprisionadas em uma casa comum. O padrão narrativo transforma a lógica de enunciação informativa, com valorização da busca de imaginação e envolvimento do leitor, exploração de fragmentação imagética e textual com elementos de serialidade e reiterada busca de atenção por contínua progressão por hiperlinks -- o que não impede de haver o trato de questões de interesse público e relevância.

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Tabloids in the Era of Social Media? Understanding the Production and Consumption of Clickbaits in Twitter

Cited by 20 publications

References 20 publications

Public Sphere 2.0: Targeted Commenting in Online News Media

Public Sphere 2.0: Targeted Commenting in Online News Media

Fake News Early Detection

Fragmentação visual e serialização na narrativa informacional de sites caça-cliques

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