Search Results (14)

Push notifications are a digital strategy for outlets to provide news and a convenient way for audiences to absorb information. Past research shows the effectiveness of push notifications and how they are framed, but few studies have explored their relationship with clickbait. However, clickbait often has a negative connotation. Through an exploratory mixed methods study involving textual analysis of push notifications (n = 639) sent by three credible mainstream media outlets, namely The Associated Press, The New York Times, and The Wall Street Journal, and a survey of readers’ (n = 368) perception of push notifications and clickbait, this research explores how credible news outlets directly engage with their respective audiences by framing push notifications in the form of clickbait. This study builds on framing theory by proposing the concept of credible clickbait and illustrating how push notifications shape readers’ immediate perceptions of content being shared with them by news outlets they subscribe to. This research also aims to be a resource for journalists to increase audience interaction and foster sustained attention with stories. Full article

This study explores possible applications of AI technology in online journalism, given the predictions that speed and adaptation to the new medium will increase the penetration of automation in the production business. The literature shows that while the human supervision of journalistic workflow is still considered vital, the journalistic workflow is changing in nature, with the writing of micro-content being entrusted to ChatGPT-3.5 among the most visible features. This research assesses readers’ reactions to different headline styles as tested on a sample of 624 students from Timisoara, Romania, asked to evaluate the qualities of a mix of human-written vs. AI-generated headlines. The results show that AI-generated, informative headlines were perceived by more than half of the respondents as the most trustworthy and representative of the media content. Clickbait headlines, regardless of their source, were considered misleading and rated as manipulative (44.7%). In addition, 54.5% of respondents reported a decrease in trust regarding publications that frequently use clickbait techniques. A linguistic analysis was conducted to grasp the qualities of the headlines that triggered the registered responses. This study provides insights into the potential of AI-enabled tools to reshape headline writing practices in digital journalism. Full article

The growing volume of textual data generated on digital media platforms presents significant challenges for the analysis and interpretation of information. This article proposes a methodological approach that combines artificial intelligence (AI) techniques and statistical methods to explore and analyze textual data from digital media. The framework, titled DAFIM (Data Analysis Framework for Information and Media), includes strategies for data collection through APIs and web scraping, textual data processing, and data enrichment using AI solutions, including named entity recognition (people, locations, objects, and brands) and the detection of clickbait in news. Sentiment analysis and text clustering techniques are integrated to support content analysis. The potential applications of this methodology include social networks, news aggregators, news portals, and newsletters, offering a robust framework for studying digital data and supporting informed decision-making. The proposed framework is validated through a case study involving data extracted from the Google News aggregation platform, focusing on the Israel–Lebanon conflict. This demonstrates the framework’s capability to uncover narrative patterns, content trends, and clickbait detection while also highlighting its advantages and limitations. Full article

This study focuses on the case of news headlines in current online journalism, looking into the current possibilities opened by ChatGPT to generate such texts in an attention-grabbing manner. To assess the reaction of online readers to headlines (clickbait or click-worthy), an online survey was applied, involving Romanian students. A total of 100 original human-authored articles with clickbait headlines were extracted from a relevant Romanian database. ChatGPT was used to generate alternative headlines (one clickbait and one informative) based on the original texts. The resulting corpus of 100 headline triplets was offered to students for evaluation. More than 70% of the 600 participants in the survey preferred AI-generated headlines over the human-authored ones, indicating their experiences and behaviors in media consumption. The preferred headlines were further analyzed along lexical and grammatical characteristics, and stylistically, to pinpoint the features sparking readers’ curiosity and engagement. While on a cognitive level the investigated audience rejected clickbait headlines as being deceitful and frustrating, in practice less than 34% favored neutral and objective headlines. Also, the linguistic analysis provided insights into the mechanics of reader engagement and the effectiveness of various headline strategies. The results are useful to anticipate the adoption of AI as a creative partner in Romanian media practice. Full article

Critical thinking is essential when navigating, evaluating, and interacting with media; therefore, it is important to investigate if adults’ critical thinking skills can be trained. This paper describes an experiment investigating the impact of video lectures about enthymemes and critical thinking skills on participants’ (N = 176) critical thinking skills, measured by the Watson–Glaser Critical Thinking Appraisal (WGCTA) and on their ability to identify clickbait headlines. Participants were adults recruited through the Prolific Platform, and they were randomly assigned to one of three conditions: an enthymeme lecture, a general critical thinking lecture, or a control condition. The results indicated no significant improvement in critical thinking scores across the conditions, as measured by the WGCTA. Similarly, no significant differences were found in the participants’ ability to identify clickbait headlines. However, a significant positive correlation was observed between higher critical thinking scores and better clickbait recognition. These results suggest that a short lecture-based intervention may not be sufficient to significantly improve adult learners’ critical thinking. Perhaps this study indicates the need for more in-depth or interactive interventions to effectively support media literacy. The material presented here is a kind of counterexample of what should be done. For this reason, it may prove useful in future research to avoid certain experimental dead-ends. Full article

In an era dominated by the digital revolution, the distribution of information has undergone a profound transformation. The duality of “quality journalism” and “viral journalism” has become an important theme in the modern media landscape. This paper explores the scope of information dissemination, dissecting the fundamentals, challenges, characteristics, and trends associated with both “quality” and “viral” journalism. Utilizing the Greek political scene as a case study, this paper aims to examine the tensions and trade-offs inherent in journalistic practices within the context of contemporary information dissemination. Analyzing closely media coverage surrounding events such as the election of Stefanos Kasselakis, the new President of the SYRIZA-Progressive Alliance party, we seek to elucidate the delicate balance between viral and quality journalism. By shedding light on these dynamics, our study aims to provide a nuanced understanding of how journalism navigates the tension between virality and quality within the Greek political sphere in a “post-politics” era. Full article

The competition to attract audiences has led to an increase in sensational or misleading headlines and content, with the aim of garnering user clicks in the news media. This dynamic alters the journalistic manner in which news is presented, and it does so by reducing informative quality and eroding the trust of the audience. This study examines the proliferation of clickbait strategies on the front pages of reputable international ‘serious’ press and how it manifests in readers’ consumption and sharing habits. We carried out a comparative content analysis of digital news articles from four international media sources (N = 1680): The Guardian (UK), The New York Times (USA), El País (Spain) and Público (Portugal). Our results confirm the existence of clickbait (N = 516) on the front pages, the most read content and the articles most shared on social media. Most clickbait titles resort to headline strategies of containing incomplete information that affect both hard and soft news topics. This particular finding highlights the inclusion of clickbait in the agenda of ‘serious’ journalism, despite the negative implications on information quality and trust. Associated with irrelevant content, this ‘hook’ captures the attention of the online audience more than the social media audience. Full article

Clickbait headlines are misleading headiness designed to attract attention and entice users to click on the link. Links can host malware, trojans and phishing attacks. Clickbaiting is one of the more subtle methods used by hackers and scammers. For these reasons, clickbait is a serious issue that must be addressed. This paper presents a method for identifying clickbait headlines using semantic analysis and machine learning techniques. The method involves analyzing thirty unique semantic features and exploring six different machine learning classification algorithms individually and in ensemble forms. Results show that the top models have an accuracy of 98% in classifying clickbait headlines. The proposed models can serve as a template for developing practical applications to detect clickbait headlines automatically. Full article

The user, usage, and usability (3U’s) are three principal constituents for cyber security. The effective analysis of the 3U data using artificial intelligence (AI) techniques allows to deduce valuable observations, which allow domain experts to design practical strategies to alleviate cyberattacks and ensure decision support. Many internet applications, such as internet advertising and recommendation systems, rely on click-through rate (CTR) prediction to anticipate the possibility that a user would click on an ad or product, which is key for understanding human online behaviour. However, online systems are prone to click on fraud attacks. We propose a Human-Centric Cyber Security (HCCS) model that additionally includes AI techniques targeted at the key elements of user, usage, and usability. As a case study, we analyse a CTR prediction task, using deep learning methods (factorization machines) to predict online fraud through clickbait. The results of experiments on a real-world benchmark Avazu dataset show that the proposed approach outpaces (AUC is 0.8062) other CTR forecasting approaches, demonstrating the viability of the proposed framework. Full article

People who use social networks often fall prey to clickbait, which is commonly exploited by scammers. The scammer attempts to create a striking headline that attracts the majority of users to click an attached link. Users who follow the link can be redirected to a fraudulent resource, where their personal data are easily extracted. To solve this problem, a novel browser extension named ClickBaitSecurity is proposed, which helps to evaluate the security of a link. The novel extension is based on the legitimate and illegitimate list search (LILS) algorithm and the domain rating check (DRC) algorithm. Both of these algorithms incorporate binary search features to detect malicious content more quickly and more efficiently. Furthermore, ClickBaitSecurity leverages the features of a deep recurrent neural network (RNN). The proposed ClickBaitSecurity solution has greater accuracy in detecting malicious and safe links compared to existing solutions. Full article

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. Full article

Combating the spread of fake news remains a difficult problem. For this reason, it is increasingly urgent to understand the phenomenon of fake news. This review aims to see why fake news is widely shared on social media and why some people believe it. The presentation of its structure (from the images chosen, the format of the titles and the language used in the text) can explain the reasons for going viral and what factors are associated with the belief in fake news. We show that fake news explores all possible aspects to attract the reader’s attention, from the formation of the title to the language used throughout the body of the text. The proliferation and success of fake news are associated with its characteristics (more surreal, exaggerated, impressive, emotional, persuasive, clickbait, shocking images), which seem to be strategically thought out and exploited by the creators of fake news. This review shows that fake news continues to be widely shared and consumed because that is the main objective of its creators. Although some studies do not support these correlations, it appears that conservatives, right-wing people, the elderly and less educated people are more likely to believe and spread fake news. Full article

In South Korea, approximately 88.5% of online users have obtained news and information from news aggregators such as Naver and Daum. Since most users read news on the internet, a new type of tabloid journalism, referred to as “news abuse,” has emerged in South Korea. “News abuse” is jargon used in South Korea to mean the repetitive display of news by online news publishers. “News abuse” is similar to “clickbait” in its use of clickbait headline links to attract online users’ attention and encourage them to click on links. This study explores the characteristics of news abusing phenomena in South Korea. With content analysis of 2101 articles (609 stories for soft news and 1402 stories for hard news), we attempt to investigate when news abuse saliently occurs and to whom news abusing is attributed. Our results show that news abuse is prevalent among South Korean newspapers during the first three hours after initial news reports are made and when people have time to rest after lunch between noon and 3 p.m. Moreover, the highest percentage of news abuse of soft news was found among tabloid daily newspapers, while that of hard news was found among daily newspapers. In addition, intermedia news abuse was more frequently utilized than intramedia news abuse. The percentage of intermedia abuse, in particular, was higher in general daily newspapers and business newspapers than in other news media platforms. By contrast, the percentage of intramedia news abuse was significantly higher in 24-h news channels. News abuse may be a side effect of news aggregation in the division of labor of news production and news distribution. More steps are required to decrease news abuse, which will lead to maintenance of a healthy digital news ecosystem and development of the news aggregation business. Full article

With the development of online advertisements, clickbait spread wider and wider. Clickbait dissatisfies users because the article content does not match their expectation. Thus, clickbait detection has attracted more and more attention recently. Traditional clickbait-detection methods rely on heavy feature engineering and fail to distinguish clickbait from normal headlines precisely because of the limited information in headlines. A convolutional neural network is useful for clickbait detection, since it utilizes pretrained Word2Vec to understand the headlines semantically, and employs different kernels to find various characteristics of the headlines. However, different types of articles tend to use different ways to draw users’ attention, and a pretrained Word2Vec model cannot distinguish these different ways. To address this issue, we propose a clickbait convolutional neural network (CBCNN) to consider not only the overall characteristics but also specific characteristics from different article types. Our experimental results show that our method outperforms traditional clickbait-detection algorithms and the TextCNN model in terms of precision, recall and accuracy. Full article