Relevant bibliographies by topics / Clickbank

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  2. Dissertations / Theses
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Academic literature on the topic 'Clickbank'

Author: Grafiati
Published: 13 December 2022
Last updated: 27 January 2023

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Journal articles on the topic "Clickbank"

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Robianes, Hilmi Ananda, Enjang AS, and Betty Tresnawaty. "Persepsi Wartawan mengenai Penggunaan Clickbait pada Judul Berita di Media Siber." Annaba: Jurnal Ilmu Jurnalistik 4, no. 1 (October 10, 2021): 15–34. http://dx.doi.org/10.15575/annaba.v4i1.2494.

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ABSTRAK Tujuan penelitian ini yaitu untuk mengetahui atensi Wartawan Kabupaten Purwakarta dalam penggunaan clickbait pada judul berita di media siber, untuk mengetahui seleksi Wartawan Kabupaten Purwakarta dalam penggunaan clickbait pada judul berita di media siber, untuk mengetahui interpretasi Wartawan Kabupaten Purwakarta dalam penggunaan clickbait pada judul berita di media siber. Penelitian ini menggunakan metode kualitatif deskriptif. Didasarkan pada teori persepsi yaitu atensi, seleksi dan interpretasi. Hasil dari penelitian ini yaitu wartawan anggota PWI Kabupaten Purwakarta memperhatikan clickbait. Dalam membuat judul yang menggunakan clickbait tetap mematuhi aturan seperti relevasi antara judul berita dengan isi berita. Penggunaan clickbait pada judul berita diperbolehkan namun tanpa mengabaikan kode etik dan tetap bersesuaian antara judul dan isi berita. Kata Kunci : Persepsi Wartawan; Clickbait; Media Siber. ABSTRACT The purpose of this study is to determine the attention of journalists in Purwakarta Regency in using clickbait on news headlines in cyber media, to find out the selection of journalists from Purwakarta Regency in using clickbait on news headlines in cyber media, to find out the interpretation of Purwakarta Journalists in using clickbait on news headlines. on cyber media. This research uses descriptive qualitative method. Based on the theory of perception, namely attention, selection and interpretation. . The results of this study are journalists who are members of the PWI Purwakarta Regency pay attention to clickbait. In making titles using clickbait, we must comply with rules such as the relevance of news headlines to news content. The use of clickbait in news headlines is allowed but without ignoring the code of ethics and it is consistent with the title and content of the news. Keywords : Journalists' Perception; Clickbait; Cyber Media.
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Sollereder, Bethany. "Climate Clickbait." Expository Times 134, no. 3 (December 2022): 144–45. http://dx.doi.org/10.1177/00145246221139035.

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Kemm, Roy. "The Linguistic and Typological Features of Clickbait in Youtube Video Titles." Social Communication 8, no. 1 (January 1, 2022): 66–80. http://dx.doi.org/10.2478/sc-2022-0007.

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Abstract This exploratory study aims to identify which linguistic and typological features commonly associated with clickbait in online news headlines are indicative of clickbait in YouTube video titles. A comparative corpus analysis is conducted to compare YouTube video titles commonly associated with clickbait to titles not associated with clickbait. Results indicate that a majority of the typological and linguistic features associated with clickbait in online news headlines are found to be indicative of clickbait in YouTube video titles. However, the role which each of the features plays seems to differ to that of online news. The findings contribute to the understanding of clickbait in non-news contexts from a linguistics perspective, an area which has been relatively unexplored in the current literature.
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Zhang, Wenping, Wei Du, Yiyang Bian, Chih-Hung Peng, and Qiqi Jiang. "Seeing is not always believing: an exploratory study of clickbait in WeChat." Internet Research 30, no. 3 (March 13, 2020): 1043–58. http://dx.doi.org/10.1108/intr-09-2019-0373.

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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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Sagita, Riska, Ultach Enri, and Aji Primajaya. "Klasifikasi Berita Clickbait Menggunakan K-Nearest Neighbor (KNN)." JOINS (Journal of Information System) 5, no. 2 (November 30, 2020): 230–39. http://dx.doi.org/10.33633/joins.v5i2.3705.

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Clickbait menjadi salah satu cara untuk mencari uang dengan meningkatkan traffic pengunjung dan pengunjung. Praktik clickbait pada saat ini sudah merambah pada dunia jurnalistik sedangkan sistem berita media online berbeda dengan media cetak. Sama halnya dengan media online lainnya, clickbait ini memberikan pengaruh besar terhadap penyedia berita karena rasa keingintahuan dari para pembaca dan sulitnya para pembaca memilih berita clickbait atau bukan clickbait. Praktik clickbait ini sendiri sangat di andalkan oleh penyedia situs berita yang menggunakan judul-judul yang menjebak untuk menarik para pembaca. Berdasarkan masalah tersebut dilakukan penelitian untuk mengklasifikasikan berita clickbait menggunakan metode K-Nearest Neighbors (K-NN).Dari hasil penelitian yang dilakukan, diperoleh hasil terbaik pada jumlah k = 11 dengan menggunakan skenario 1 pada data pembagian dengan jumlah data sebanyak 800 data dan 200 data uji yang menghasilkan akurasi sebesar 71%, ketepatan 72%, dan ingat 71%. Hal ini menunjukkan bahwa klasifikasi berita clickbait dapat di klasifikasikan menggunakan K-Nearest Neighbor.
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Munger, Kevin, Mario Luca, Jonathan Nagler, and Joshua Tucker. "The (Null) Effects of Clickbait Headlines on Polarization, Trust, and Learning." Public Opinion Quarterly 84, no. 1 (2020): 49–73. http://dx.doi.org/10.1093/poq/nfaa008.

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Abstract “Clickbait” headlines designed to entice people to click are frequently used by both legitimate and less-than-legitimate news sources. Contemporary clickbait headlines tend to use emotional partisan appeals, raising concerns about their impact on consumers of online news. This article reports the results of a pair of experiments with different sets of subject pools: one conducted using Facebook ads that explicitly target people with a high preference for clickbait, the other using a sample recruited from Amazon’s Mechanical Turk. We estimate subjects’ individual-level preference for clickbait, and randomly assign sets of subjects to read either clickbait or traditional headlines. Findings show that older people and non-Democrats have a higher “preference for clickbait,” but reading clickbait headlines does not drive affective polarization, information retention, or trust in media.
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Girinoto, Girinoto, Dhana Arvina Alwan, Gusti Agung Ngurah Gde K.T. D, Olga Geby Nabila, Arizal Arizal, and Dimas Febriyan Priambodo. "Implementasi Deteksi Judul Berita Clickbait Berbahasa Indonesia dengan pre-trained model Multilingual BERT Pada Aplikasi Berbasis Chrome Extension." Jurnal Ilmiah SINUS 20, no. 2 (July 19, 2022): 25. http://dx.doi.org/10.30646/sinus.v20i2.624.

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Clickbait news title is often used by online news portal. The purpose of clickbait is to attract reader to open and read the news. Furthermore, news containing clickbait title can give negative impact by reducing the essence of important news. Therefore, clickbait detection tool is needed to avoid the clickbait news title. Chrome extension was chosen in this study because it supports all Chrome based browsers, such as Google Chrome, Chromium, Microsoft Edge, and Opera so that many users apply this program. In this study, Chrome extension-based application was designed and integrated by using artificial intelligence model. This application also utilized the availability of pre-trained multilingual BERT model as Natural Language Processing (NLP) which will be used to predict a clickbait news title. This study used Multilingual BERT model as NLP because this model has been trained into 104 languages, including Bahasa Indonesia and it has significant performance. The result of this study can detect clickbait news along with 92% of AUC-ROC value.
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Al-Sarem, Mohammed, Faisal Saeed, Zeyad Ghaleb Al-Mekhlafi, Badiea Abdulkarem Mohammed, Mohammed Hadwan, Tawfik Al-Hadhrami, Mohammad T. Alshammari, Abdulrahman Alreshidi, and Talal Sarheed Alshammari. "An Improved Multiple Features and Machine Learning-Based Approach for Detecting Clickbait News on Social Networks." Applied Sciences 11, no. 20 (October 13, 2021): 9487. http://dx.doi.org/10.3390/app11209487.

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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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Diyah Utami Kusumaning Putri and Dinar Nugroho Pratomo. "Clickbait Detection of Indonesian News Headlines using Fine-Tune Bidirectional Encoder Representations from Transformers (BERT)." Inform : Jurnal Ilmiah Bidang Teknologi Informasi dan Komunikasi 7, no. 2 (July 30, 2022): 162–68. http://dx.doi.org/10.25139/inform.v7i2.4686.

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The problem of the existence of news article that does not match with content, called clickbait, has seriously interfered readers from getting the information they expect. The number of clickbait news continues significantly increased in recent years. According to this problem, a clickbait detector is required to automatically identify news article headlines that include clickbait and non-clickbait. Additionally, many currently existing solutions use handcrafted features and traditional machine learning methods, which limit the generalization. Therefore, this study fine-tunes the Bidirectional Encoder Representations from Transformers (BERT) and uses the Indonesian news headlines dataset CLICK-ID to predict clickbait (BERT). In this research, we use IndoBERT as the pre-trained model, a state-of-the-art BERT-based language model for Indonesian. Then, the usefulness of BERT-based classifiers is then assessed by comparing the performance of IndoBERT classifiers with different pre-trained models with that of two word-vectors-based approaches (i.e., bag-of-words and TF-IDF) and five machine learning classifiers (i.e., NB, KNN, SVM, DT, and RF). The evaluation results indicate that all fine-tuned IndoBERT classifiers outperform all word-vectors-based machine learning classifiers in classifying clickbait and non-clickbait Indonesian news headlines. The IndoBERTBASE using the two training phases model gets the highest accuracy of 0.8247, which is 0.064 (6%), outperforming the SVM classifier's accuracy with the bag-of-words model 0.7607.
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Statz, Michele, and Lauren Heidbrink. "Migration as Clickbait." Anthropology News 59, no. 1 (January 2018): e117-e122. http://dx.doi.org/10.1111/an.760.

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Dissertations / Theses on the topic "Clickbank"

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Praboda, Chathurangani Rajapaksha Rajapaksha Waththe Vidanelage. "Clickbait detection using multimodel fusion and transfer learning." Electronic Thesis or Diss., Institut polytechnique de Paris, 2020. http://www.theses.fr/2020IPPAS025.

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Presque tous les internautes sont susceptibles d'être victimes de clickbait, supposant à tort qu’il s’agit d’informations légitimes. Un type important de clickbait se présente sous la forme de spam et de publicités qui sont utilisés pour rediriger les utilisateurs vers des sites web. Un autre type de "clickbait" est conçu pour faire la une des journaux et rediriger les lecteurs vers leurs sites en ligne, mais ces nouvelles sensationnelles peuvent être trompeuses. Il est difficile de prédire le degré de click-baity d'une nouvelle donnée car les clickbait sont des messages très courts et écrits de manière souvent obscure. La principale caractéristique qui permet d'identifier les clickbait est d'explorer l'écart entre ce qui est attendu dans un post, le titre de l'information et l’information réellement présente dans l'article qui y est lié. Dans cette thèse, on propose deux approches innovantes pour explorer le clickbait généré par les médias d'information dans les médias sociaux. Les contributions 1) de proposer une approche multimodèle basée sur la fusion en incorporant des techniques d'apprentissage profond et d'exploration de texte et 2) d’adapter les modèles d'apprentissage par transfert (TL) pour étudier l'efficacité des transformateurs permettant de prédire le contenu des clickbaits
Internet users are likely to be victims to clickbait assuming as legitimate news. The notoriety of clickbait can be partially attributed to misinformation as clickbait use an attractive headline that is deceptive, misleading or sensationalized. A major type of clickbait are in the form of spam and advertisements that are used to redirect users to web sites that sells products or services (often of dubious quality). Another common type of clickbait are designed to appear as news headlines and redirect readers to their online venues intending to make revenue from page views, but these news can be deceptive, sensationalized and misleading. News media often use clickbait to propagate news using a headline which lacks greater context to represent the article. Since news media exchange information by acting as both content providers and content consumers, misinformation that is deliberately created to mislead requires serious attention. Hence, an automated mechanism is required to explore likelihood of a news item being clickbait.Predicting how clickbaity a given news item is difficult as clickbait are very short messages and written in obscured way. The main feature that can identify clickbait is to explore the gap between what is promised in the social media post, news headline and what is delivered by the article linked from it. The recent enhancement to Natural Language Processing (NLP) can be adapted to distinguish linguistic patterns and syntaxes among social media post, news headline and news article.In my Thesis, I propose two innovative approaches to explore clickbait generated by news media in social media. Contributions of my Thesis are two-fold: 1) propose a multimodel fusion-based approach by incorporating deep learning and text mining techniques and 2) adapt Transfer Learning (TL) models to investigate the efficacy of transformers for predicting clickbait contents.In the first contribution, the fusion model is built on using three main features, namely similarity between post and headline, sentiment of the post and headline and topical similarity between news article and post. The fusion model uses three different algorithms to generate output for each feature mentioned above and fuse them at the output to generate the final classifier.In addition to implementing the fusion classifier, we conducted four extended experiments mainly focusing on news media in social media. The first experiment is on exploring content originality of a social media post by amalgamating the features extracted from author's writing style and online circadian rhythm. This originality detection approach is used to identify news dissemination patterns among news media community in Facebook and Twitter by observing news originators and news consumers. For this experiment, dataset is collected with our implemented crawlers from Facebook and Twitter streaming APIs. The next experiment is on exploring flaming events in the news media in Twitter by using an improved sentiment classification model. The final experiment is focused on detecting topics that are discussed in a meeting real-time aiming to generate a brief summary at the end.The second contribution is to adapt TL models for clickbait detection. We evaluate the performance of three TL models (BERT, XLNet and RoBERTa) and delivered a set of architectural changes to optimize these models.We believe that these models are the representatives of most of the other TL models in terms of their architectural properties (Autoregressive model vs Autoencoding model) and training datasets. The experiments are conducted by introducing advanced fine-tuning approaches to each model such as layer pruning, attention pruning, weight pruning, model expansion and generalization. To the best of authors' knowledge, there have been an insignificant number of attempts to use TL models on clickbait detection tasks and no any comparative analysis of multiple TL models focused on this task
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Lönnqvist, Oliver. "Fake news, clickbait och konkurrens : En kvantitativ studie om nyhetsorganisationers etiska samhällsansvar." Thesis, Uppsala universitet, Företagsekonomiska institutionen, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-448339.

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Fake news has long been a part of the news landscape but has in recent times, as the market for online news has grown, become an increasingly occurring problem in contemporary news media and society. By analyzing the content published by the largest American news organizations this thesis provides an overview of the occurrence of fake news and clickbait on the competitive news market. Competition for the readers' limited attention has been known to create a thirst to be first and may lead to the consequences of unethical behavior by the organizations. But what does that mean? Fake news can be categorized as biased information which in turn can be divided into two parts, misinformation and disinformation. Disinformation is the intentional spread of wrong or biased information and in the scope of this thesis a news site’s intention is determined through its use of clickbait. This study also evaluates the fake news and clickbait present in the most popular news articles by readers in order to see if the competition is increasing the use of tools for competitive advantages within the market for news. With this information it is possible to categorize the news article as one of three stages of morality which in turn is used to evaluate to what degree the organization is fulfilling the ethical aspect of their corporate social responsibility. The study finds that all but one of the explored organizations are fulfilling their ethical responsibility.
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Fromholtz, Victoria, and Olivia Magnusson. ""Du kan inte ana vad som kommer hända härnäst" : En kvalitativ textanalys av clickbaits utformning på Youtube." Thesis, Högskolan för lärande och kommunikation, Högskolan i Jönköping, HLK, Medie- och kommunikationsvetenskap, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:hj:diva-38746.

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För att uppmärksammas bland dagens stora utbud av videoklipp på Youtube skapar vloggare rubrik- och bildsättningar som innehåller clickbaits. Begreppet innebär att rubrik- och bildsättningar är av uppseendeväckande karaktär, innehåller lite information och enbart syftar till att locka mottagare att klicka sig in på videoklipp. Syftet med denna studie är därför att öka medvetenheten om hur clickbaits är utformade på Youtube. Studien bygger på en kvalitativ kritisk textanalys. Vi har studerat en av Sveriges största Youtube-kanaler där sammanlagt 22 av de populäraste videoklippen från 2017 studerats utifrån retoriken och semiotiken. Detta för att synliggöra vilka attribut clickbaitsen är utformade med. Därefter har 4 slumpmässigt utvalda videoklipp analyserats utifrån relevansteorin för att undersöka hur relevant clickbaitsen är för innehållet. Studiens resultat visar att sex, starka känslor, välvilja och personlighet är vanligt förekommande i utformningen av clickbaits på Youtube, där personligt varumärke visar sig vara av stor betydelse. Resultatet visar även att videoklippens rubrik- och bildsättningar till stor del inte är relevanta för innehållet. Studien diskuterar även att clickbaits är något som gynnar vloggarna eftersom det ger dem en hög klickfrekvens, men missgynnar oss mottagare eftersom vi tar del av information som många gånger inte är den mest relevanta.
To be noticed among today's large supply of video clips on Youtube, the creators are using both headline and thumbnails that contain clickbaits. The concept means that the headline and thumbnails are of a ostentatious character, they seldom contain information about the video you are about to watch. The clickbaits only purpose is to entice the watcher to click on videos. Therefore the purpose of this study is to raise awareness of how clickbaits are built up on Youtube. The study is built upon a qualitative critical text analysis. We have studied one of Sweden's largest Youtube-channels, where 22 of the channels most popular clips from 2017 have been studied through the rhetoric and semiotic. We want to make visible on which attributes the clickbaits are built upon. Subsequently has 4 randomly picked videoclips been analysed through the relevance theory to examine how relevant the clickbaits are for the actual content of the video. The studies result show that sexual content, strong emotions, goodwill and personality are common traits within clickbaits on Youtube, where the personal brand show to be of great meaning. The result also show that the headlines and thumbnails are not relevant to the content of the video. The study indicates that the use clickbaits benefits the creator because it gives them more views on their video. From the viewers point of view are they not beneficial, because the information that we receive from the clickbait is not the most relevant when you compare it to the actual content of the video.
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Lazauskas, Darius, Julia Jacka, and Ingrida Kažemėkaitė. "Rage, giggles and fishing for clicks : A qualitative study on how clickbaiting affects perceived online news content quality." Thesis, Linnéuniversitetet, Institutionen för marknadsföring (MF), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-75384.

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Purpose The purpose of this study is to explore how the use of clickbait marketing strategies by online news outlets affects consumer perceptions of online news content quality.   Design/methodology/approach A qualitative, deductive, exploratory and cross-sectional method, wherein data was collected through semi-structured interviews to understand consumer perceptions of how the use of clickbait strategies affects perceived online news content quality. The gathered data was then analyzed with the help of a directed content analysis supported by qualitative content analysis software Atlas.Ti. Findings This study found that the use of clickbait content strategies affected the perceived quality of online news content. Furthermore, it was found that there are two primary consumer groups, one of which seeks to avoid clickbait and one of which seems to engage with clickbait. Finally, several new variables were found for perceived news content quality that applied in the online space. Research limitations/implications The main implications of this research are that clickbait strategies should be utilized cautiously as they are often found misleading or displeasing, and may cause damage to the publisher. Furthermore, despite clickbait sometimes being entertaining, most clickbait was found to be highly context sensitive. Finally, a number of new variables were found that expanded previous understandings of perceived news quality. This study was limited by several factors - firstly, a lack of english as a first language which may have resulted in misunderstandings. Secondly, the articles utilized were found to have several shortcomings during the research procedure. Finally, due to the qualitative nature of this study, its findings are non-generalizable.
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Melgar, Sandoval Héctor Alberto. "El clickbait como estrategia del sensacionalismo en Depor.com para informar sobre el caso Paolo Guerrero y su vinculación a Boca Juniors entre noviembre de 2019 y enero de 2020." Bachelor's thesis, Universidad Peruana de Ciencias Aplicadas (UPC), 2020. http://hdl.handle.net/10757/655067.

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La convergencia tradicional al contexto digital en los años noventa produjo que técnicas informativas como el sensacionalismo se trasladen a un ámbito moderno o ecosistema digital y puedan disponer de nuevas herramientas para extender su estructura de desinformación. En esa extensión, se aproxima el clickbait como un recurso importante y protagonista, que se define como un anzuelo de atención para el público, quien al leer un titular llamativo ingresa a la noticia difundida y se da con la sorpresa que el desglose resulta no ser tan relevante, en consecuencia, el titular termina siendo una exageración o una mentira. A lo largo de los capítulos observaremos cómo desde la evolución del periodismo al contexto moderno, el protagonismo del fútbol en los medios de comunicación y el sensacionalismo relacionado al clickbait han generado un nuevo lenguaje desinformativo, el cual se va asentando cada vez más en una normalización que podría estar generando una especie de estrategia supuestamente válida para diversos medios de comunicación escritos. Por supuesto, el balompié no termina de perder importancia y a raíz de un contexto nacional, donde es una disciplina que influye en aspectos económicos, sociales y antropológicos, su estudio es más que relevante. En la presente investigación, analizamos a profundidad cómo se utiliza el clickbait en Depor.com para informar sobre el probable traspaso de Paolo Guerrero a Boca Juniors entre 2019 y 2020.
The traditional convergence to the digital context in the 1990s produced that informational techniques such as sensationalism were transferred to a modern environment or digital ecosystem and could have new tools to extend their disinformation structure. In this extension, clickbait is approaching as an important resource and protagonist, which is defined as a hook of attention for the public, who when reading a striking headline enters the news broadcast and is surprised that the resulting breakdown is not So relevant, consequently, the headline ends up being an exaggeration or a lie. Throughout the chapters we will observe how, from the evolution of journalism to the modern context, the prominence of football in the media and the sensationalism related to clickbait have generated a new disinformative language, which is increasingly settling into a normalization that could be generating a kind of strategy that is supossed to valid for various written media. Of course, football does not end up losing importance and as a result of a national context, where it is a discipline that influences economic, social and anthropological aspects, the study is more than relevant. In this research, we analyze in depth how clickbait is used on Depor.com to inform about the probable transfer of Paolo Guerrero to Boca Juniors between 2019 and 2020.
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Berggren, Alva, Samuel Danielsson, and Bernadina Ogvall. "Manipulerade streams och dess roll i musikbranschen : - En kvalitativ studie om hur manipulerade streams färgar dagens musikbransch (ur ett marknadsföringsperspektiv)." Thesis, Linnéuniversitetet, Institutionen för marknadsföring (MF), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-101413.

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The digitalization and emergence of the Internet have given rise to new platforms providing marketing, distribution and revenues to happen in the same place, a significant difference from the past. New platforms have become new places for artists to compete for, for instance, positions on toplists. This is the fundamental background to the occurrence of manipulated streams in the music industry. The purpose of this study has been to examine and create a deeper understanding of manipulated streams and its role in the music industry, from a marketing perspective. Thus, the study aims to provide knowledge on the meaning of having high streaming numbers, why streams are being manipulated and how they are used in marketing.  The empirical data is based on six interviews with people from different parts of the music business. The results indicate that manipulated streams affect expectations of the artist and provide an opportunity for the artist to stand out in today's information overload. The artists have gained more power and influence as they are now being compared with influencers, and can influence the way the audience listens to music. Moreover, the findings also indicate that actors and platforms of the music industry need adjustments to encounter the phenomenon of manipulated streams.  Lastly, we suggest that future research should focus on how to prevent the occurrence of manipulated streams, in addition to social media's effect on cognitive behaviours of the users in relation to high streaming numbers.
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Peña, Salas Juan Pablo. "Clickbait como recurso que altera los criterios de jerarquización en el periodismo digital informativo. Caso: COVID-19 en el Perú (6 al 15 de marzo del 2020), en la web del Diario La República, RPP Noticias y América Noticias." Bachelor's thesis, Universidad Peruana de Ciencias Aplicadas (UPC), 2020. http://hdl.handle.net/10757/655066.

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El tema de la presente tesis es la influencia de las nuevas tecnologías en la evolución del periodismo digital informativo y cómo su uso ha alterado sus criterios de jerarquización tradicionales (en forma de pirámide invertida, en donde lo principal iba al inicio y el resto de la nota era su desarrollo o descripción). En este caso, la investigación se basará en cómo emplean el clickbait los medios de comunicación peruanos para el desarrollo de sus notas informativas y cómo este uso ha cambiado la estructura de la noticia. Se revisaron las plataformas en línea de tres medios: el Diario La República como prensa escrita, Radio RPP como prensa radial, y América Noticias como prensa televisiva; escogidos al haber sido pioneros en sus rubros en la transición al mundo digital y además por el alto nivel de lectoría y audiencia. Para el logro del objetivo planteado, se realizó un análisis de contenido con un periodo de tiempo delimitado por la aparición del paciente cero de COVID-19 en el Perú (6 de marzo), que fue el momento en que el público peruano tomó conciencia de que la enfermedad había llegado a nuestro país; hasta que se decretó el inicio de la cuarentena (15 de marzo), fecha en el que el Estado reaccionó y tomó medidas para combatir y/o prevenir la expansión del virus. El hallazgo más importante en los tres medios es que sí existe una variación en la estructura de las notas informativas que contienen un clickbait, utilizando una jerarquización principalmente romboide (estructura de redacción donde la información principal o la respuesta al titular de la nota está al medio y no al inicio).
The subject of this thesis is the influence of new technologies in the evolution of digital journalism and how its use has altered its traditional hierarchical criteria (in the form of an inverted pyramid, where the main thing was at the beginning and the rest of the note is its development or description). In this case, the research will be based on how the Peruvian media use clickbait for the development of their informative notes and how this use has changed the structure of the news. The online platforms of three media were reviewed: Diario La República as the written press, Radio RPP as the radio press, and América Noticias as the television press; chosen for having been pioneers in their fields in the transition to the digital world and also for the high level of readership and audience. To achieve the proposed objective, a content analysis was carried out with a period of time delimited by the appearance of the zero COVID-19 patient in Peru (March 6), which was the moment when the Peruvian public became aware of that the disease had reached our country; until the beginning of the quarantine was decreed (March 15), the date on which the State reacted and took measures to combat and / or prevent the spread of the virus. The most important finding in the three media is that there is a variation in the structure of the informative notes that contain a clickbait, using a mainly rhomboid hierarchization (writing structure where the main information or the response to the note holder is in the middle and not at the beginning).
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Byhlin, Victor. "Svenska klickbeten, så ser de ut : En undersökning av klickbeten och dess tematik hos Dagens Nyheter, Aftonbladet och SVT Nyheter." Thesis, Uppsala universitet, Avdelningen för retorik, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-323093.

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Fraser, Henry. "Copyright and culture : a qualitative theory." Thesis, University of Oxford, 2018. https://ora.ox.ac.uk/objects/uuid:cd4e645a-7e45-4309-bc68-e115e1fa306d.

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Copyright is conventionally justified as an incentive to produce and disseminate works of authorship. We can justify and theorise copyright more richly, not least because empirical evidence does not support the incentive narrative. Rather than focussing on quantitative matters such as the number of works incentivised and produced, we should consider copyright's qualitative influence on culture. A threshold objection to such an approach is the risk of cultural paternalism. This objection can be overcome. Rather than specifying paternalistic standards of merit for works, we can target the conditions under which their creation and consumption takes place. I argue, firstly, that we should adopt the following high-level principles: (i) that the conditions of creation and consumption of works should be conducive to democratic deliberation (democracy) and (ii) that they should facilitate the development of human capabilities (autonomy). Secondly, I propose that we pursue three mid-level objectives, which are helpful indicia of democracy and autonomy: - a fair and wide distribution of communicative and cultural power (inclusiveness); - diversity in the content and perspectives available to the public (diversity); and - conditions that permit authors and users of works to engage rigorously with the conventions of the media in which they operate (rigour). It is often said that copyright obstructs important qualitative objectives, like freedom of expression, and that we could better pursue these goals by weakening copyright and relying on non-proprietary alternatives. My approach produces a more optimistic, but also more complicated, view of copyright. While copyright's qualitative influence is not optimal, reductions in the strength and scope of copyright sometimes produces conditions and incentive structures that are worse for inclusiveness, diversity and rigour than stronger copyright. For example, both attention and wealth are highly concentrated in networked information economies driven by free sharing of content, and this is bad for diversity or inclusiveness. Online business models, based on surveillance of users' consumption of free works, are corrosive of autonomy and democracy. Merely removing copyright-based restrictions on the sharing of works is not a panacea for copyright's ills. A qualitative theory such as mine equips us to better understand and calibrate more richly the trade-offs involved in copyright policy decisions, and encourages us to treat copyright as part of a broader, qualitatively-oriented information and cultural policy.
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LIN, YU-DE, and 林育德. "Clickbait: Advertising with a Quality Bait." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/z9cabz.

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碩士
國立臺北大學
企業管理學系
106
Clickbait is inevitable to be used in nowadays online advertising. However, its effect and how it is perceived by viewers have not been well studied. The present study aims to provide guidelines to help advertisers and content providers utilize such technique to attract viewers without disappointing them. A model is proposed to explain the effect of clickbait on ad attitude based on the expectation-disconfirmation theory and the incongruity-resolution model of humor. How the incongruity caused by clickbait influences the viewer's satisfaction and attitude toward the ad and how incongruity resolution moderates this influence are investigated. Then an experiment is conducted to collect data and test the proposed model. To our knowledge, it is the first study to systematically understand viewers’ reactions to clickbait advertisements. The results show that incongruity between ad headline and content can generate surprise and happiness that leads to favorable ad effectiveness. The viewers react with more happiness if the incongruity can be resolved. The research findings can provide guidelines for designing the combinations of clickbait headline and content.
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Books on the topic "Clickbank"

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Appel, Markus, ed. Die Psychologie des Postfaktischen: Über Fake News, „Lügenpresse“, Clickbait & Co. Berlin, Heidelberg: Springer Berlin Heidelberg, 2020. http://dx.doi.org/10.1007/978-3-662-58695-2.

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Publishing, The Midnight Dream. ClickBank : How to Make Money with ClickBank: How You Can Make Money with ClickBank. Independently Published, 2018.

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The Clickbank Code. John Girling, 2011.

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Saghir, Muhammad. Clickbank Marketing Secrets. Independently Published, 2022.

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Saghir, Muhammad. ClickBank Marketing Essentials. Independently Published, 2022.

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Clickbank Marketing secrets. Danny journey, 2022.

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Gesmondi, Palmer. Clickbank Secrets : the Foundations of Clickbank Affiliate Marketing to Help You Succeed: Clickbank Affiliate Marketing for Beginners. Independently Published, 2021.

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Wenger, Seth. Industries Related to Clickbank: Understanding Clickbank and Its Focal Points and Bothers. Independently Published, 2019.

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Hawkins, John. Introduction to Clickbank: A Proven Clickbank Affiliate Marketing Secret Without a Website. Independently Published, 2018.

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Osei, David A. How to Make Big Money on Clickbank Marketplace: Ultimate Clickbank Secrets Guide. Independently Published, 2019.

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Book chapters on the topic "Clickbank"

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Potthast, Martin, Sebastian Köpsel, Benno Stein, and Matthias Hagen. "Clickbait Detection." In Lecture Notes in Computer Science, 810–17. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-30671-1_72.

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Harte, David. "Clickbait and banal news." In The Routledge Companion to Journalism Ethics, 346–53. London: Routledge, 2021. http://dx.doi.org/10.4324/9780429262708-45.

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Pandey, Deepanshu, Garimendra Verma, and Sushama Nagpal. "Clickbait Detection Using Swarm Intelligence." In Communications in Computer and Information Science, 64–76. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-5758-9_6.

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Gothankar, Ruchira, Fabio Di Troia, and Mark Stamp. "Clickbait Detection for YouTube Videos." In Advances in Information Security, 261–84. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-97087-1_11.

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Geçkil, Ayşe, Ahmet Anıl Müngen, Esra Gündoğan, and Mehmet Kaya. "Detecting Clickbait on Online News Sites." In Lecture Notes in Social Networks, 199–211. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-33698-1_11.

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Vorakitphan, Vorakit, Fang-Yie Leu, and Yao-Chung Fan. "Clickbait Detection Based on Word Embedding Models." In Innovative Mobile and Internet Services in Ubiquitous Computing, 557–64. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-93554-6_54.

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Hoffmann, Christine. "What Is Clickbait? (Check All that Apply)." In Stupid Humanism, 109–28. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-63751-8_5.

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Dong, Manqing, Lina Yao, Xianzhi Wang, Boualem Benatallah, and Chaoran Huang. "Similarity-Aware Deep Attentive Model for Clickbait Detection." In Advances in Knowledge Discovery and Data Mining, 56–69. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-16145-3_5.

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Zheng, Hai-Tao, Xin Yao, Yong Jiang, Shu-Tao Xia, and Xi Xiao. "Boost Clickbait Detection Based on User Behavior Analysis." In Web and Big Data, 73–80. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-63564-4_6.

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Rajput, Adil E. "Clickbait in Education—Positive or Negative? Machine Learning Answers." In Research & Innovation Forum 2019, 101–8. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-30809-4_10.

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Conference papers on the topic "Clickbank"

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Kumar, Vaibhav, Dhruv Khattar, Siddhartha Gairola, Yash Kumar Lal, and Vasudeva Varma. "Identifying Clickbait." In SIGIR '18: The 41st International ACM SIGIR conference on research and development in Information Retrieval. New York, NY, USA: ACM, 2018. http://dx.doi.org/10.1145/3209978.3210144.

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Khater, Suhaib R., Oraib H. Al-sahlee, Daoud M. Daoud, and M. Samir Abou El-Seoud. "Clickbait Detection." In the 7th International Conference. New York, New York, USA: ACM Press, 2018. http://dx.doi.org/10.1145/3220267.3220287.

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Yi, Xiaoyuan, Jiarui Zhang, Wenhao Li, Xiting Wang, and Xing Xie. "Clickbait Detection via Contrastive Variational Modelling of Text and Label." In Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}. California: International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/ijcai.2022/621.

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Clickbait refers to deliberately created sensational or deceptive text for tricking readers into clicking, which severely hurts the web ecosystem. With a growing number of clickbaits on social media, developing automatic detection methods becomes essential. Nonetheless, the performance of existing neural classifiers is limited due to the underutilization of small labelled datasets. Inspired by related pedagogy theories that learning to write can promote comprehension ability, we propose a novel Contrastive Variational Modelling (CVM) framework to exploit the labelled data better. CVM models the conditional distributions of text and clickbait labels by predicting labels from text and generating text from labels simultaneously with Variational AutoEncoder and further differentiates the learned spaces under each label by a mixed contrastive learning loss. In this way, CVM can capture more underlying textual properties and hence utilize label information to its full potential, boosting detection performance. We theoretically demonstrate CVM as learning a joint distribution of text, clickbait label, and latent variable. Experiments on three clickbait detection datasets show our method's robustness to inadequate and biased labels, outperforming several recent strong baselines.
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D. Molina, Maria, S. Shyam Sundar, Md Main Uddin Rony, Naeemul Hassan, Thai Le, and Dongwon Lee. "Does Clickbait Actually Attract More Clicks? Three Clickbait Studies You Must Read." In CHI '21: CHI Conference on Human Factors in Computing Systems. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3411764.3445753.

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Agrawal, Amol. "Clickbait detection using deep learning." In 2016 2nd International Conference on Next Generation Computing Technologies (NGCT). IEEE, 2016. http://dx.doi.org/10.1109/ngct.2016.7877426.

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Shu, Kai, Suhang Wang, Thai Le, Dongwon Lee, and Huan Liu. "Deep Headline Generation for Clickbait Detection." In 2018 IEEE International Conference on Data Mining (ICDM). IEEE, 2018. http://dx.doi.org/10.1109/icdm.2018.00062.

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Chawda, Sarjak, Aditi Patil, Abhishek Singh, and Ashwini Save. "A Novel Approach for Clickbait Detection." In 2019 3rd International Conference on Trends in Electronics and Informatics (ICOEI). IEEE, 2019. http://dx.doi.org/10.1109/icoei.2019.8862781.

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Zeng, Eric, Miranda Wei, Theo Gregersen, Tadayoshi Kohno, and Franziska Roesner. "Polls, clickbait, and commemorative $2 bills." In IMC '21: ACM Internet Measurement Conference. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3487552.3487850.

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Geckil, Ayse, Ahmet Anil Mungen, Esra Gundogan, and Mehmet Kaya. "A Clickbait Detection Method on News Sites." In 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). IEEE, 2018. http://dx.doi.org/10.1109/asonam.2018.8508452.

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Fu, Junfeng, Liang Liang, Xin Zhou, and Jinkun Zheng. "A Convolutional Neural Network for Clickbait Detection." In 2017 4th International Conference on Information Science and Control Engineering (ICISCE). IEEE, 2017. http://dx.doi.org/10.1109/icisce.2017.11.

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Reports on the topic "Clickbank"

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Bazaco, A., M. Redondo, and P. Sánchez-García. El clickbait, como estrategia del periodismo viral: concepto y metodología. Revista Latina de Comunicación Social, January 2019. http://dx.doi.org/10.4185/rlcs-2019-1323.

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Bazaco, A., M. Redondo, and P. Sánchez-García. Clickbait as a strategy of viral journalism: conceptualisation and methods. Revista Latina de Comunicación Social, January 2019. http://dx.doi.org/10.4185/rlcs-2019-1323en.

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García Orosa, B., S. Gallur Santorun, and X. López García. El uso del clickbait en cibermedios de los 28 países de la Unión Europea. Revista Latina de Comunicación Social, November 2017. http://dx.doi.org/10.4185/rlcs-2017-1218.

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García Orosa, B., S. Gallur Santorun, and X. López García. Use of clickbait in the online news media of the 28 EU member countries. Revista Latina de Comunicación Social, November 2017. http://dx.doi.org/10.4185/rlcs-2017-1218en.

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