A News Verification Browser for the Detection of Clickbait, Satire, and Falsified News

Rubin, Victoria L.; Brogly, Chris; Conroy, Nadia; Chen, Yimin; Cornwell, Sarah; Asubiaro, Toluwase Victor

doi:10.21105/joss.01208

Cited by 12 publications

(12 citation statements)

References 8 publications

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“…Building upon the algorithmic design of clickbait detection, certain studies developed applications of such detection (Rubin et al , 2019). For instance, Chakraborty et al (2016) designed a browser extension that provided a warning on potential clickbait by comparing the feature distributions between trained clickbait and non-clickbait datasets; Rony et al (2017) designed and developed an automatic bot and integrated it into a web browser to help users avoid clicking baited headlines on social media.…”

Section: Research Conjecturesmentioning

confidence: 99%

“…Several extant studies have confounded the concept of clickbait and fake news though they are completely different in essence. In particular, clickbait is targeted for attracting users to click and read whereas fake news is created to disseminate fictitious information for malicious purposes (Bakir and McStay, 2018; Lazer et al , 2018; Rubin et al , 2019). Notably, differing from fake news that contain little factual basis, the content under clickbait is simply valueless.…”

Section: Introductionmentioning

confidence: 99%

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Seeing is not always believing: an exploratory study of clickbait in WeChat

Zhang

Bian

et al. 2020

INTR

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PurposeThe purpose of this study is to unpack the antecedents and consequences of clickbait prevalence in online media at two different levels, namely, (1) Headline-level: what characteristics of clickbait headlines attract user clicks and (2) Publisher-level: what happens to publishers who create clickbait on a prolonged basis.Design/methodology/approachTo test the proposed conjectures, the authors collected longitudinal data in collaboration with a leading company that operates more than 500 WeChat official accounts in China. This study proposed a text mining framework to extract and quantify clickbait rhetorical features (i.e. hyperbole, insinuation, puzzle, and visual rhetoric). Econometric analysis was employed for empirical validation.FindingsThe findings revealed that (1) hyperbole, insinuation, and visual rhetoric entice users to click the baited headlines, (2) there is an inverted U-shaped relationship between the number of clickbait headlines posted by a publisher and its visit traffic, and (3) this non-linear relationship is moderated by the publisher's age.Research limitations/implicationsThis research contributes to current literature on clickbait detection and clickbait consequences. Future studies can design more sophisticated methods for extracting rhetorical characteristics and implement in different languages.Practical implicationsThe findings could aid online media publishers to design attractive headlines and develop clickbait strategies to avoid user churn, and help managers enact appropriate regulations and policies to control clickbait prevalence.Originality/valueThe authors propose a novel text mining framework to quantify rhetoric embedded in clickbait. This study empirically investigates antecedents and consequences of clickbait prevalence through an exploratory study of WeChat in China.

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Section: Research Conjecturesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Seeing is not always believing: an exploratory study of clickbait in WeChat

Zhang

Bian

et al. 2020

INTR

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“…Another recent proof of concept implementation, the news verification (NV) Browser (Rubin et al , 2019), is envisioned as a research tool for news readers, journalists, editors or information professionals. The tool analyzes the language used in digital news web pages to determine if they are clickbait, satirical news or falsified news, and visualizes the results by highlighting content in color-coded categories.…”

Section: Proposed Interventions: Automation Education and Regulationmentioning

confidence: 99%

Disinformation and misinformation triangle

Rubin

2019

Self Cite

102

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Purpose The purpose of this paper is to treat disinformation and misinformation (intentionally deceptive and unintentionally inaccurate misleading information, respectively) as a socio-cultural technology-enabled epidemic in digital news, propagated via social media. Design/methodology/approach The proposed disinformation and misinformation triangle is a conceptual model that identifies the three minimal causal factors occurring simultaneously to facilitate the spread of the epidemic at the societal level. Findings Following the epidemiological disease triangle model, the three interacting causal factors are translated into the digital news context: the virulent pathogens are falsifications, clickbait, satirical “fakes” and other deceptive or misleading news content; the susceptible hosts are information-overloaded, time-pressed news readers lacking media literacy skills; and the conducive environments are polluted poorly regulated social media platforms that propagate and encourage the spread of various “fakes.” Originality/value The three types of interventions – automation, education and regulation – are proposed as a set of holistic measures to reveal, and potentially control, predict and prevent further proliferation of the epidemic. Partial automated solutions with natural language processing, machine learning and various automated detection techniques are currently available, as exemplified here briefly. Automated solutions assist (but not replace) human judgments about whether news is truthful and credible. Information literacy efforts require further in-depth understanding of the phenomenon and interdisciplinary collaboration outside of the traditional library and information science, incorporating media studies, journalism, interpersonal psychology and communication perspectives.

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“…We demonstrate a recently developed system that uses supervised Machine Learning methodology to identify clickbait in digital news (Rubin et al, 2019). "Clickbait" is a hyperlinked headline that primarily attracts readers' attention but leads to uninformative content, and it can be contrasted with traditional more informative 'headlinese' (Chen and Rubin, 2017).…”

Section: Towards a More Transparent Nlp System Design: Clickbait Detementioning

confidence: 99%

AI Opaqueness: What Makes AI Systems More Transparent?

Rubin¹

2020

CAIS/ACSI

Self Cite

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Artificially Intelligent (AI) systems are pervasive, but poorly understood by their users and, at times, developers. It is often unclear how and why certain algorithms make choices, predictions, or conclusions. What does AI transparency mean? What explanations do AI system users desire? This panel discusses AI opaqueness with examples in applied context such as natural language processing, people categorization, judicial decision explanations, and system recommendations. We offer insights from interviews with AI system users about their perceptions and developers’ lessons learned. What steps should be taken towards AI transparency and accountability for its decisions? Les systèmes artificiellement intelligents (IA) sont omniprésents, mais mal compris par leurs utilisateurs et, parfois, par les développeurs. On ne sait souvent pas comment et pourquoi certains algorithmes font des choix, des prédictions ou des conclusions. Que signifie la transparence de l'IA? Quelles explications les utilisateurs du système d'IA souhaitent-ils? Ce panel examine l'opacité de l'IA avec des exemples dans un contexte appliqué tels que le traitement du langage naturel, la catégorisation des personnes, les explications des décisions judiciaires et les recommandations système. Nous proposons des informations issues d'entretiens avec des utilisateurs de systèmes d'IA sur leurs perceptions et les leçons apprises par les développeurs. Quelles mesures devraient être prises pour assurer la transparence et la responsabilité de l'IA pour ses décisions?

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A News Verification Browser for the Detection of Clickbait, Satire, and Falsified News

Cited by 12 publications

References 8 publications

Seeing is not always believing: an exploratory study of clickbait in WeChat

Seeing is not always believing: an exploratory study of clickbait in WeChat

Disinformation and misinformation triangle

AI Opaqueness: What Makes AI Systems More Transparent?

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