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Journal articles on the topic 'News headline generator'

Author: Grafiati

Published: 7 June 2025

Last updated: 2 August 2025

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1

Tsann Phua, Yeong, Kwang Hooi Yew, Mohd Fadzil Hassan, and Matthew Teow Yok Wooi. "Malay phoneme-based subword news headline generator for low-resource language." IAES International Journal of Artificial Intelligence (IJ-AI) 13, no. 4 (2024): 4965. http://dx.doi.org/10.11591/ijai.v13.i4.pp4965-4975.

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The booming of technology has significantly increased the amount of news articles for readers. The headline of news plays an essential role in attracting readers. Traditionally, crafting the news headline is a manual task at the news desk. The motivation of this paper is to address the issues faced in low resource languages, such as the Malay language. The main contribution of this paper is a new hybrid model based on extractive- and abstractive-based text summarization with the integration of a geographical linguistics model; a Malay phoneme-based subword embedding has been developed to solve

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Yeong, Tsann Phua, Hooi Yew Kwang, Fadzil Hassan Mohd, and Teow Yok Wooi Matthew. "Malay phoneme-based subword news headline generator for low-resource language." IAES International Journal of Artificial Intelligence (IJ-AI) 13, no. 4 (2024): 4965–75. https://doi.org/10.11591/ijai.v13.i4.pp4965-4975.

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The booming of technology has significantly increased the amount of news articles for readers. The headline of news plays an essential role in attracting readers. Traditionally, crafting the news headline is a manual task at the news desk. The motivation of this paper is to address the issues faced in low resource languages, such as the Malay language. The main contribution of this paper is a new hybrid model based on extractive- and abstractive-based text summarization with the integration of a geographical linguistics model; a Malay phoneme-based subword embedding has been developed to solve

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Shamigov, Fedor, and Zoya Rezanova. "Automatic Generation of News Headlines Using RuGPT-3 Neural Network: Effect of Training Dataset on Model Performance." Virtual Communication and Social Networks 4, no. 1 (2025): 62–70. https://doi.org/10.21603/2782-4799-2025-4-1-62-70.

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News agencies compete in the digital space, where the success often depends on the promptness of publication, which can be provided by automatic headline generation technologies. This study examined the effect of dataset types on the quality of headline generation, i.e., the impact of dataset type (individual news categories vs. their combination) on the quality of automatic news headlines. The initial hypothesis was that training the RuGPT-3 model on thematic sets of articles and on their totality would give different generated headlines. The authors used the RuGPT-3 model and news articles p

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Lieb, Anna, Maneesh Arora, and Eni Mustafaraj. "Online News Coverage of Critical Race Theory Controversies: A Dataset of Annotated Headlines." Proceedings of the International AAAI Conference on Web and Social Media 18 (May 28, 2024): 1979–90. http://dx.doi.org/10.1609/icwsm.v18i1.31441.

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In this paper, we introduce an annotated dataset of 11,704 unique U.S. news headlines related to critical race theory and its controversies from August 2020 through December 2022. Annotations generated by GPT-4 specify the headline stance and the primary actor in the headline. GPT-4 annotations performed well on the validation dataset, with weighted average F-scores of 0.8339 for headline stance annotations and 0.7625 for primary actor annotations. Along with the annotated headlines and URLs to the full article, we augment the dataset with metrics that are relevant to future research on politi

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Shrawankar, Urmila, and Kranti Wankhede. "News Headline Building using Hybrid Headline Generation Technique for Quick Gist." International Journal of Natural Computing Research 6, no. 1 (2017): 36–52. http://dx.doi.org/10.4018/ijncr.2017010103.

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A considerable amount of time is required to interpret whole news article to get the gist of it. Therefore, in order to reduce the reading and interpretation time, headlines are necessary. The available techniques for news headline construction mainly includes extractive and abstractive headline generation techniques. In this paper, context based news headline is formed from long news article by using techniques of core Natural Language Processing (NLP) and key terms of news article. Key terms are retrieved from lengthy news article by using various approaches of keyword extraction. The keyphr

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Gherheș, Vasile, Marcela Alina Fărcașiu, and Mariana Cernicova-Buca. "Are ChatGPT-Generated Headlines Better Attention Grabbers than Human-Authored Ones? An Assessment of Salient Features Driving Engagement with Online Media." Journalism and Media 5, no. 4 (2024): 1817–35. https://doi.org/10.3390/journalmedia5040110.

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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

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Neupane, Amul, Kapalik Khanal, Nitesh Nepal, and Naseeb Dangi. "Advanced News Aggregation and Content Generation Using LLMs and NLP Algorithms." European Journal of Applied Science, Engineering and Technology 3, no. 2 (2025): 295–303. https://doi.org/10.59324/ejaset.2025.3(2).24.

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The exponential growth of digital information has created a unique opportunity in the media industry to leverage Large Language Models (LLMs) for news aggregation and content generation. This paper explores the application of LLMs and Natural Language Processing (NLP) for the automation of news aggregation and content creation. This study presents a system designed to automatically fetch news headlines and articles from various news portals, with an initial implementation involving four distinct sources. Utilizing a combination of advanced natural language processing techniques, including Open

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Gherheș, Vasile, Marcela Alina Fărcașiu, Mariana Cernicova-Buca, and Claudiu Coman. "AI vs. Human-Authored Headlines: Evaluating the Effectiveness, Trust, and Linguistic Features of ChatGPT-Generated Clickbait and Informative Headlines in Digital News." Information 16, no. 2 (2025): 150. https://doi.org/10.3390/info16020150.

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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, Rom

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Maine, Barry. "Late Nineteenth-Century Trompe L'Oeil and Other Performances of the Real." Prospects 16 (October 1991): 281–95. http://dx.doi.org/10.1017/s0361233300004555.

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“We live in an age of wonders!” exclaims a character in Henry James's The Bostonians (1886). And so it must have seemed to any American who could read the newspapers, which thrived, in the 1880s, on the business of proclaiming marvels. In The Bostonians one of the “wonders” is Miss Verena Tarrant, whose precocious and hypnotic speaking powers on the subject of women's rights — together with a pretty face and trim figure — succeeded in selling out the Boston Music Hall. Other wonders of the decade were less comely but more enduring: the lightbulb, the electric generator (which so awed Henry Ada

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Zhou, Zhibo, Yang Yang, and Zhoujun Li. "APSN: Adversarial Pseudo-Siamese Network for Fake News Stance Detection." Electronics 12, no. 4 (2023): 1043. http://dx.doi.org/10.3390/electronics12041043.

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Fake news is a longstanding issue that has existed on the social network, whose negative impact has been increasingly recognized since the US presidential election. During the election, numerous fake news about the candidates distributes vastly in the online social networks. Identifying inauthentic news quickly is an essential purpose for this research to enhance the trustworthiness of news in online social networks, which will be the task studied in this paper. The fake news stance detection can contribute to detect a startling amount of fake news, which aims at evaluating the relevance betwe

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Laksman-Huntley, Myrna, and Fastira Dwiputri. "MEDIA FRAMING ON ONLINE NEWS HEADLINES IN REGARDS TO THE GILETS JAUNES PHENOMENON." JURNAL ILMU BUDAYA 8, no. 2 (2020): 367–78. http://dx.doi.org/10.34050/jib.v8i2.11260.

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Gilets Jaunes massive protest in France was considered as the most serious crisis in the Fifth Republic since the protest of May 1968. The importance of the event was such that it generated serious controversy, which was widely reported and many French media competed to highlight the development of the action group. This study examines the headlines of the Gilets Jaunes phenomenon in two online news sites (LeFigaro.fr and Libération.fr) with a different ideology. The analysis was carried out using theories of Le Querler's syntactic structure, Nida's meaning componential analysis, Dor & Gat

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Gotmare, Abhay, Gandharva Thite, and Laxmi Bewoor. "A multimodal machine learning approach to generate news articles from geo-tagged images." International Journal of Electrical and Computer Engineering (IJECE) 14, no. 3 (2024): 3434. http://dx.doi.org/10.11591/ijece.v14i3.pp3434-3442.

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Classical machine learning algorithms typically operate on unimodal data and hence it can analyze and make predictions based on data from a single source (modality). Whereas multimodal machine learning algorithm, learns from information across multiple modalities, such as text, images, audio, and sensor data. The paper leverages the functionalities of multimodal machine learning (ML) application for generating text from images. The proposed work presents an innovative multimodal algorithm that automates the creation of news articles from geo-tagged images by leveraging cutting-edge development

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Gotmare, Abhay, Gandharva Thite, and Laxmi Bewoor. "A multimodal machine learning approach to generate news articles from geo-tagged images." A multimodal machine learning approach to generate news articles from geo-tagged images 14, no. 3 (2024): 3434–42. https://doi.org/10.11591/ijece.v14i3.pp3434-3442.

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Classical machine learning algorithms typically operate on unimodal data and hence it can analyze and make predictions based on data from a single source (modality). Whereas multimodal machine learning algorithm, learns from information across multiple modalities, such as text, images, audio, and sensor data. The paper leverages the functionalities of multimodal machine learning (ML) application for generating text from images. The proposed work presents an innovative multimodal algorithm that automates the creation of news articles from geo-tagged images by

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Li, Hao. "Analysis of Misleading and Authenticity of News Headlines." Communications in Humanities Research 24, no. 1 (2024): 8–14. http://dx.doi.org/10.54254/2753-7064/24/20231203.

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The era of information has brought about a new "reading era" for news communication. Regardless of the type of news report, it is crucial to ensure the truthfulness of the news. The authenticity of news is the foundation for its existence and development. However, due to the entertainment-oriented nature of online media, some companies that profit from internet traffic take advantage of the general phenomenon of most users' lack of attention and irrationality on the internet platform. This phenomenon has been observed for many years and has repeatedly misled the masses, affecting their daily l

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Su, Xin, and Shengwen Wang. "The impact and management of internet-based public opinion dissemination during emergencies: A case study of Baidu News during the first wave of coronavirus disease 2019 (COVID-19)." PLOS ONE 19, no. 4 (2024): e0299374. http://dx.doi.org/10.1371/journal.pone.0299374.

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Background and aims The coronavirus disease 2019 (COVID-19) public health emergency has had a huge impact worldwide. We analyzed news headlines and keywords from the initial period of COVID-19, and explored the dissemination timeline of news related to the epidemic, and the impact of Internet-based media on the public using lifecycle theory and agenda-setting theory. We aimed to explore the impact of Baidu news headlines on public attention during the first wave of COVID-19, as well as the management mechanism of regulatory departments for social public opinion. Methods We searched Baidu News

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Gao, Guanyuan. "The Authenticity Crisis of AIGC News Content Ontology: A Media Culture Critique Based on Baudrillards Simulacra Theory." Communications in Humanities Research 72, no. 1 (2025): 117–23. https://doi.org/10.54254/2753-7064/2025.lc24857.

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With the increasing application of automated news writing and digital anchors in journalism, AIGC (Artificial Intelligence-Generated Content) has become highly realistic in form and increasingly conforms to the linguistic norms of traditional news. However, AIGC news often suffers from ambiguous sources and insufficient factual support, leading to a crisis of authenticity. Drawing on Baudrillards theory of simulacra, this paper systematically analyzes the manifestations of the authenticity crisis in AIGC news at the levels of content generation, expressive form, and audience perception, reveal

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Sidek, Zuleaizal, Sharifah Sakinah Syed Ahmad, and Noor Hasimah Ibrahim Teo. "Associating deep learning and the news headlines sentiment for Bursa stock price prediction." Indonesian Journal of Electrical Engineering and Computer Science 31, no. 2 (2023): 1041. http://dx.doi.org/10.11591/ijeecs.v31.i2.pp1041-1049.

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Accurate stock price prediction is appealing to academics, economists, and financial analysts for its potential to increase profits. Although remarkable progress has been made in stock prediction accuracy, studies to explore the relationship between public sentiments and the prediction of stock price movement based on online news portals in Malaysia context are limited. Therefore, this study aims to determine whether news sentiments influence the movement of the Bursa stock price. The stock prediction model was implemented using long short-term memory (LSTM), with stock data from Bursa Malaysi

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Sandhya, Aneja, Aneja Nagender, and Kumaraguru Ponnurangam. "Predictive linguistic cues for fake news: a societal artificial intelligence problem." International Journal of Artificial Intelligence (IJ-AI) 11, no. 4 (2022): 1252–60. https://doi.org/10.11591/ijai.v11.i4.pp1252-1260.

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Media news are making a large part of public opinion and, therefore, must not be fake. News on web sites, blogs, and social media must be analyzed before being published. In this paper, we present linguistic characteristics of media news items to differentiate between fake news and real news using machine learning algorithms. Neural fake news generation, headlines created by machines, semantic incongruities in text and image captions generated by machine are other types of fake news problems. These problems use neural networks which mainly control distributional features rather than evidence.

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Aneja, Sandhya, Nagender Aneja, and Ponnurangam Kumaraguru. "Predictive linguistic cues for fake news: a societal artificial intelligence problem." IAES International Journal of Artificial Intelligence (IJ-AI) 11, no. 4 (2022): 1252. http://dx.doi.org/10.11591/ijai.v11.i4.pp1252-1260.

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<span>Media news are making a large part of public opinion and, therefore, must not be fake. News on web sites, blogs, and social media must be analyzed before being published. In this paper, we present linguistic characteristics of media news items to differentiate between fake news and real news using machine learning algorithms. Neural fake news generation, headlines created by machines, semantic incongruities in text and image captions generated by machine are other types of fake news problems. These problems use neural networks which mainly control distributional features rather tha

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Bari, Omar A., and Arvin Agah. "Ensembles of Text and Time-Series Models for Automatic Generation of Financial Trading Signals from Social Media Content." Journal of Intelligent Systems 29, no. 1 (2018): 753–72. http://dx.doi.org/10.1515/jisys-2017-0567.

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Abstract Event studies in finance have focused on traditional news headlines to assess the impact an event has on a traded company. The increased proliferation of news and information produced by social media content has disrupted this trend. Although researchers have begun to identify trading opportunities from social media platforms, such as Twitter, almost all techniques use a general sentiment from large collections of tweets. Though useful, general sentiment does not provide an opportunity to indicate specific events worthy of affecting stock prices. This work presents an event clustering

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Zulhakim, Muthiah Khairunisa, and Akhmad Haqiqi Ma’mun. "A SYNTACTICAL ANALYSIS OF HEADLINE EDUCATION TOPIC NEWS ON THE CNA WEBSITE." NIVEDANA : Jurnal Komunikasi dan Bahasa 4, no. 1 (2023): 9–19. http://dx.doi.org/10.53565/nivedana.v4i1.746.

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Syntax aims to the rules and procedures that are used in certain languages to put together sentences. Learning syntax is essential because it enables us to illustrate the structures of sentences more clearly and efficiently and to look into the structure of English sentences. In reality, many readers and writers have poor comprehension of sentence structure, particularly the structure of sentences written in English. This problem is also encountered by students of English education who are enrolled in classes that cover information related to syntax, as well as by other readers who are not fam

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Xu, Yingjie. "Critical Discourse Analysis of People’s Daily and the New York Times’ Headlines on the Beijing Winter Olympics." New Horizons in English Studies 9 (December 30, 2024): 16–42. https://doi.org/10.17951/nh.2024.9.16-42.

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This paper examined the headlines of the Chinese newspaper, People’s Daily, and the American newspaper, the New York Times, about the Beijing Winter Olympics in 2022. To investigate the possible ideological biases of news headlines, this paper performs corpus-based critical discourse analysis. It hypothesizes that Chinese media will use positive strategies to report the event, whereas American media will maintain a neutral stance. Fairclough’s three-dimensional analysis model and sentiment analysis tools via Google apps were applied to analyze news headlines of the two newspapers. With “Beijin

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Armentia Vizuete, José Ignacio, Flora Marín Murillo, María del Mar Rodríguez González, and Iñigo Marauri Castillo. "What does the digital press talk about when it talks about nutrition? An analysis of elpais.com and lavanguardia.com during 2017." Doxa Comunicación. Revista interdisciplinar de estudios de comunicación y ciencias sociales, no. 29 (December 2019): 19–41. http://dx.doi.org/10.31921/doxacom.n29a1.

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News on nutrition has become a regular feature on the media agenda. This paper analyses characteristics of texts on this topic published during 2017 in the digital editions of El País and La Vanguardia, the two newspapers with the widest circulation in Spain. The study looked in greater depth at aspects such as the main topics being addressed, authorship, genres and headlines of the texts, framing, sources and the number of comments from readers. The two chosen newspapers reveal considerable differences in matters such as quantity of sources used or the number of comments that are generated by

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Vaideghy, A., and C. Thiyagarajan. "An Ensemble Classification and Hybrid Feature Selection Approach for Fake News Stance Detection." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 4s (2023): 28–39. http://dx.doi.org/10.17762/ijritcc.v11i4s.6304.

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The developments in Internet and notions of social media have revolutionised representations and disseminations of news. News spreads quickly while costing less in social media. Amidst these quick distributions, dangerous or seductive information like user generated false news also spread equally. on social media. Distinguishing true incidents from false news strips create key challenges. Prior to sending the feature vectors to the classifier, it was suggested in this study effort to use dimensionality reduction approaches to do so. These methods would not significantly affect the result, thou

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Wold, Thomas. "Ordinary People and Social Media as Sources in Norwegian Newspapers." Journalism and Media 3, no. 4 (2022): 750–70. http://dx.doi.org/10.3390/journalmedia3040050.

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Ordinary people have always been of interest to journalists, and social media has become a common place to find material for new stories. This paper presents a quantitative content analysis of Norwegian news articles that are based on social media posts published by ordinary people. The analysis focuses on the topics of the news stories, sources, headlines, lead paragraphs, use of amateur photos, and what news criteria they fulfill. The news articles generated covered a wide variety of topics but were mostly soft news. Social issues, culture, and politics were the largest categories. They were

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Negryshev, Andrey A. "The credibility of news media text: The experience of macrostructural analysis." Media Linguistics 8, no. 1 (2021): 4–22. http://dx.doi.org/10.21638/spbu22.2021.101.

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The problem of textual credibility is considered in line with the theory of reference from the standpoint of a text’s grammar. The referential semantics of a text is determined by the type of discourse in which the text is generated. Subsequently, the criteria for credibility are discur sively conditioned. The article reveals the referential aspects of news media text as a “product” of news discourse with its accuracy and objectivity requirements to factually ascertain a message. To describe these characteristics, the concept of referential density of the text is introduced, which is revealed

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Priyanka, V. Thakre* Ashish S. Sambare Namrata S. Khade. "ENHANCE APPROACH FOR AUTO CAPTION GENERATION ON DIFFERENT NEWS IMAGES DATASET USING FUZZY LOGIC." INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES & RESEARCH TECHNOLOGY 5, no. 7 (2016): 597–602. https://doi.org/10.5281/zenodo.57047.

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These times, whenever retrieving images from the search Engines that retrieves images without analysing their include restrain, simply by matching user inquires against the image’s file name and format, user comment the tags, captions, and, generally, text surrounding the image. Also the retrieved image contains any textual data along with the images. Our announced the task of automatic caption generation for news images. The task fuses insights from computer vision and natural language processing and holds promise for various multimedia applications, such as image retrieval, development

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Tandiano, Andreas Halim, and Deny Jollyta. "CLASSIFICATION OF FAKE NEWS IN INDONESIAN LANGUAGE USING SUPPORT VECTOR MACHINE METHOD." JURTEKSI (Jurnal Teknologi dan Sistem Informasi) 10, no. 2 (2024): 315–22. http://dx.doi.org/10.33330/jurteksi.v10i2.2895.

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Abstract: Since information and communication technology has become ingrained in our daily lives, it has become easier to access information. However, there are some concerns. One of them is about fake news. The aim of this study is to develop an Indonesian system for detecting false news by utilizing news headlines. The methods used are linear kernel support vector ma- chine and n-gram. According to the findings of the performance test that was carried out, the linear kernel support vector machine model employing the term frequency inverse document frequency unigram feature performs better th

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Perez, Jose Mikhail. "Greed and grievances: A Discursive Study on the Evolution of the Lumad Struggle in Mindanao, 2010-2019." Journal of Ethnic and Cultural Studies 6, no. 3 (2019): 41. http://dx.doi.org/10.29333/ejecs/254.

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Are vertical or horizontal inequalities causing the Lumad struggle in Mindanao? This study attempts to answer whether ethnic or economic causes, or a combination of both, are motivating the key conflict actors in the Lumad struggle to wage long-term wars. Employing the greed and grievances model in analysing conflicts, we hypothesize that the causes of the Lumad struggle stems from ethnic grievances in the onset of conflict but eventually become an issue on economic greed in the duration of conflict over time. Using critical discourse framework, the study generated a dataset of online headline

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Omotosho, Babatunde S. "Oil Price Shocks, Fuel Subsidies and Macroeconomic (In)stability in Nigeria." Central Bank of Nigeria Journal of Applied Statistics, Vol. 10 No. 2 (February 21, 2020): 1–38. http://dx.doi.org/10.33429/cjas.10219.1/6.

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This paper studies the macroeconomic implications of oil price shocks and the extant fuel subsidy regime for Nigeria. To do this, we develop and estimate a New-Keynesian DSGE model that accounts for pass-through effect of international oil price into the retail price of fuel. Our results show that oil price shocks generate significant and persistent impacts on output, accounting for about 22 percent of its variations up to the fourth year. Under our benchmark model (i.e. with fuel subsidies), we show that a negative oil price shock contracts aggregate GDP, boosts non-oil GDP, increases headlin

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Campo, Elena, and Cano Antonia Moreno. "Authorship in the coverage of the war in Ukraine: Newsroom work takes precedence over correspondents' dispatches." Revista de Comunicación 23, no. 1 (2024): 1–21. https://doi.org/10.26441/RC23.1-2024-3319.

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The war in Ukraine has grabbed headlines around the world. This analysis asks whois covering it and what this says about war reporting. The advent of the Web and the subsequentpolarization of the media pits the constant demand for information against thejournalistic expectation ofcalm analysis. While traditional media seem to guarantee credibility, more and more people are turningto alternative information providers. To explore these changes’ repercussions on war correspondents,this study analyzes 11,268 bylines of the news on Ukraine published digitally by the most widely readnewspapers

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Nickel, Brooke, Ray Moynihan, Alexandra Barratt, Juan P. Brito, and Kirsten McCaffery. "Media coverage of calls to rename low-risk cancers: a content analysis." BMJ Open 10, no. 7 (2020): e038087. http://dx.doi.org/10.1136/bmjopen-2020-038087.

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ObjectivesThe use of more medicalised labels can increase both concern about illness and the desire for more invasive treatment. This study analyses the media’s coverage of an Analysis article in The BMJ which generated a large amount of high-profile international media coverage. It aims to understand how to better communicate messages about low-risk cancers and overdiagnosis to the public.DesignContent analysis of media coverage.SettingMedia was identified by Isentia Media Portal, searched in Google News and cross-checked in Factiva and Proquest databases from August 2018.MethodsMedia headlin

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Bader, Simona, and Corina Sîrb. "Post-Sensationalism: Catastrophism and Fight Paradigm in Romanian On-Line Media." Postmodern Openings 12, no. 4 (2021): 01–17. http://dx.doi.org/10.18662/po/12.4/358.

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In traditional journalism, sensationalism was a characteristic of tabloid press. The main instruments used in sensationalistic headlines were bombastic epithets (awesome, amazing, greatest etc), and exaggerations used to increase the impact by curiosity. In the last decade, transformation with society and online media consumption behaviour have triggered a change of paradigm: we believe that we are facing a post-sensationalism media narrative, defined by catastrophism and the fight paradigm. In the context of a huge news feed overloaded with information, in the purpose of increasing the number

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Wrigley, Charlotte. "It's a bird! It's a plane! An aerial biopolitics for a multispecies sky." Environment and Planning E: Nature and Space 1, no. 4 (2018): 712–34. http://dx.doi.org/10.1177/2514848618816991.

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Bird strikes were catapulted into headline news in 2009 when US Airlines flight 1549's engines ingested a flock of Canada geese and lost all power, leaving the pilot no option but to ditch into the freezing cold Hudson River. Although everyone on board survived, thousands of birds were killed in the years that followed in attempt to redress aviation safety concerns. This article follows the story of Flight 1549 and considers the different stages of bird strike prevention at a variety of sites: the factory, the airfield, the sky and the accident aftermath. Drawn from empirical research and grey

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Samson Olufemi Olanipekun and Oluwasegun Olakoyenikan. "Ethical implications of generative AI in journalism: Balancing innovation, truth, and public communication trust." World Journal of Advanced Research and Reviews 16, no. 3 (2022): 1293–311. https://doi.org/10.30574/wjarr.2022.16.3.1159.

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The integration of generative artificial intelligence (AI) into journalism marks a transformative era in content creation, newsroom automation, and audience engagement. Tools such as large language models and generative image and video systems now enable rapid production of headlines, articles, infographics, and multimedia packages, dramatically improving editorial efficiency. However, this innovation introduces profound ethical questions that challenge long-standing journalistic values of truth, transparency, accountability, and public trust. This paper explores the ethical implications of ge

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Aiyesimoju, Ayodeji Boluwatife, Felix Olajide Talabi, Joshua Kayode Okunade, et al. "Content Analysis of Japaism Themes in Newspaper Reports on Youth Migration in Nigeria." International Research Journal of Multidisciplinary Scope 06, no. 01 (2025): 1083–93. https://doi.org/10.47857/irjms.2025.v06i01.02261.

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This study focused on newspaper coverage of “Japaism”: youth migration in Nigeria by contextually analysing the various themes used in Vanguard and The Nation newspapers’ reports. Thematic content analysis was adopted to investigate this phenomenon. A total of 162 online articles were sourced from Vanguard and The Nation newspapers within 2023 out of which 149 were selected as the study sample. A code guide was used as a research instrument, and themes and subthemes were generated for proper analysis based on the three research questions raised for the study. The study however revealed that Ni

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Qirom, Rizqi Latiful, Arif Ardy Wibowo, and Roziani Nasir. "Framing Gibran Rakabuming Raka: A Media Analysis of CNN Indonesia's Coverage of His Vice-Presidential Nomination." CHANNEL: Jurnal Komunikasi 12, no. 2 (2024): 185–93. https://doi.org/10.12928/channel.v12i2.924.

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Indonesia will celebrate democracy in 2024. In order to nominate candidates for president and vice president of Indonesia between 2024 and 2029, a coalition of several political parties will be formed. Prabowo Subianto and his running mate, Gibran Rakabuming Raka, are among the presidential candidate pairings being promoted. However, because of his youth and PDI Perjuangan party connection, Gibrans's nomination as a vice presidential candidate generated a range of public reactions. The PDI Perjuangan has formally announced Ganjar Pranowo and Mahfud MD as their candidates for the next Indonesia

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Farhan, Saima, Rubiya Shoukat, and Aqsa Aslam. "Automatic Sarcasm Detection on Cross-Platform Social Media Datasets: A GLoVe and Bi-LSTM Based Approach." JUCS - Journal of Universal Computer Science 30, no. (5) (2024): 674–93. https://doi.org/10.3897/jucs.104790.

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Sarcastic remarks on social media platforms have become commonplace, with people expressing their bad feelings in a quite positive manner or in a mocking way. This contradictive nature of sarcasm makes its detection a very challenging task. Many researchers have provided their solutions to perform automatic sarcasm detection from a single domain dataset. Most of them have considered only the content of the text and has ignored the context of the text. Understanding that the context of a text is the most important factor in determining either it is sarcastic or not. This study aims to detect sa

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Farhan, Saima, Rubiya Shoukat, and Aqsa Aslam. "Automatic Sarcasm Detection on Cross-Platform Social Media Datasets: A GLoVe and Bi-LSTM Based Approach ." JUCS - Journal of Universal Computer Science 30, no. 5 (2024): 674–93. http://dx.doi.org/10.3897/jucs.104790.

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Sarcastic remarks on social media platforms have become commonplace, with people expressing their bad feelings in a quite positive manner or in a mocking way. This contradictive nature of sarcasm makes its detection a very challenging task. Many researchers have provided their solutions to perform automatic sarcasm detection from a single domain dataset. Most of them have considered only the content of the text and has ignored the context of the text. Understanding that the context of a text is the most important factor in determining either it is sarcastic or not. This study aims to detect sa

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Marion, Russ. "The Top 10 Trends in P-12 and Higher Education, 2024." Culture, Education, and Future 2, no. 1 (2024): 1–8. http://dx.doi.org/10.70116/2980274126.

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In the editorial for this edition of Culture, Education, and Future, the editor-in-chief, the editors, and the editorial board present our choices for the top 10 trends in education for 2024. These trends are side effects of technological advances, political changes, and shifting cultural preferences; we project that they will affect educational systems in the foreseeable future. We have generated two Top-10 lists, one for P-12 education and one for higher education. We generate these lists to accomplish two goals. First, the intent is to focus readers’ attention on emerging futures germane to

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Klein, Ari Z., Arjun Magge, Karen O'Connor, Jesus Ivan Flores Amaro, Davy Weissenbacher, and Graciela Gonzalez Hernandez. "Toward Using Twitter for Tracking COVID-19: A Natural Language Processing Pipeline and Exploratory Data Set." Journal of Medical Internet Research 23, no. 1 (2021): e25314. http://dx.doi.org/10.2196/25314.

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Background In the United States, the rapidly evolving COVID-19 outbreak, the shortage of available testing, and the delay of test results present challenges for actively monitoring its spread based on testing alone. Objective The objective of this study was to develop, evaluate, and deploy an automatic natural language processing pipeline to collect user-generated Twitter data as a complementary resource for identifying potential cases of COVID-19 in the United States that are not based on testing and, thus, may not have been reported to the Centers for Disease Control and Prevention. Methods

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Marion, Russ. "The Top 10 Trends in P-12 and Higher Education, 2024." Culture, Education, and Future 2, no. 1 (2024): 1–8. https://doi.org/10.5281/zenodo.12400069.

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In the editorial for this edition of <em>Culture, Education, and Future</em>, the editor-in-chief, the editors, and the editorial board present our choices for the<em> top 10 trends in education for 2024.</em> These trends are side effects of technological advances, political changes, and shifting cultural preferences; we project that they will affect educational systems in the foreseeable future. We have generated two Top-<em>10</em> lists, one for P-12 education and one for higher education. We generate these lists to accomplish two goals. First, the intent is to focus readers’ attenti

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Hasanuzzaman, Muhammad, Miraz Hossain, Md Majedur Rahman, et al. "Understanding Social Media Behavior in the USA: AI-Driven Insights for Predicting Digital Trends and User Engagement." Journal of Ecohumanism 4, no. 4 (2025): 119–41. https://doi.org/10.62754/joe.v4i4.6717.

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The swift advancement in social networking platforms has radically shifted the patterns and nature of how people connect with services and brands in America. The utmost objective of this research project was to implement artificial intelligence together with machine learning approaches for creating predictive models that forecast digital pattern development as well as user commitment through social media interactions within the United States. The data used in this analysis are posts aggregated from two leading online platforms, X-Twitter and Reddit, and consist of user-generated material cover

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Ao, Xiang, Ling Luo, Xiting Wang, et al. "Put Your Voice on Stage: Personalized Headline Generation for News Articles." ACM Transactions on Knowledge Discovery from Data, November 3, 2023. http://dx.doi.org/10.1145/3629168.

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In this paper, we study the problem of personalized news headline generation, which aims to produce not only concise and fact-consistent titles for news articles but also decorate these titles as personalized irresistible reading invitations by incorporating readers’ preferences. We propose an approach named PNG ( P ersonalized N ews headline G enerator) by utilizing distant supervision in readers’ past click behaviors to resolve. First, user preference representations are learned through a knowledge-aware user encoder that comprehensively captures the genuine, sequential and flash interests o

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Li, Hongqiao. "Application of Artificial Intelligence-based Content Generation Technology in News Publishing." Applied Mathematics and Nonlinear Sciences 10, no. 1 (2025). https://doi.org/10.2478/amns-2025-0642.

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Abstract Artificial Intelligence (AI) content generation technology has had a profound impact on many aspects of news publishing. In this paper, a personalized news headline generation model that incorporates user characteristics achieves innovation in news headline generation. Firstly, an additive attention-based variant of Transformer, i.e., Fastformer encoder, is constructed through Transformer, into which preprocessed news text is fed for contextual modeling. A pointer generation network model decoder is used to copy words from the source text to improve the accuracy of the copied informat

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An, Ruopeng, Quinlan Batcheller, Junjie Wang, and Yuyi Yang. "Build neural network models to identify and correct news headlines exaggerating obesity-related scientific findings." Journal of Data and Information Science, June 7, 2023. http://dx.doi.org/10.2478/jdis-2023-0014.

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Abstract Purpose Media exaggerations of health research may confuse readers’ understanding, erode public trust in science and medicine, and cause disease mismanagement. This study built artificial intelligence (AI) models to automatically identify and correct news headlines exaggerating obesity-related research findings. Design/methodology/approach We searched popular digital media outlets to collect 523 headlines exaggerating obesity-related research findings. The reasons for exaggerations include: inferring causality from observational studies, inferring human outcomes from animal research,

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DeVerna, Matthew R., Harry Yaojun Yan, Kai-Cheng Yang, and Filippo Menczer. "Fact-checking information from large language models can decrease headline discernment." Proceedings of the National Academy of Sciences 121, no. 50 (2024). https://doi.org/10.1073/pnas.2322823121.

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Fact checking can be an effective strategy against misinformation, but its implementation at scale is impeded by the overwhelming volume of information online. Recent AI language models have shown impressive ability in fact-checking tasks, but how humans interact with fact-checking information provided by these models is unclear. Here, we investigate the impact of fact-checking information generated by a popular large language model (LLM) on belief in, and sharing intent of, political news headlines in a preregistered randomized control experiment. Although the LLM accurately identifies most f

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Robertson, Claire E., Nicolas Pröllochs, Kaoru Schwarzenegger, Philip Pärnamets, Jay J. Van Bavel, and Stefan Feuerriegel. "Negativity drives online news consumption." Nature Human Behaviour, March 16, 2023. http://dx.doi.org/10.1038/s41562-023-01538-4.

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AbstractOnline media is important for society in informing and shaping opinions, hence raising the question of what drives online news consumption. Here we analyse the causal effect of negative and emotional words on news consumption using a large online dataset of viral news stories. Specifically, we conducted our analyses using a series of randomized controlled trials (N = 22,743). Our dataset comprises ~105,000 different variations of news stories from Upworthy.com that generated ∼5.7 million clicks across more than 370 million overall impressions. Although positive words were slightly more

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van’t Riet, Jonathan, and Mariska Kleemans. "What drives selection of online children’s news articles?" Journalism, April 16, 2021, 146488492110101. http://dx.doi.org/10.1177/14648849211010157.

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Individual news consumers’ decisions to select specific news articles online are an important part of the present journalistic landscape, spurring scholars’ interest in ‘selective exposure’ and the factors that influence news selection. In the present study, we investigated predictors of young news consumers’ self-selection of individual news articles. We used a dataset containing information on upwards of 18,000 news articles that were published in the smartphone and tablet application of the Dutch children’s news television program. The dataset contained the headline of each published news a

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Celadin, Tatiana, Valerio Capraro, Gordon Pennycook, and David G. Rand. "Displaying News Source Trustworthiness Ratings Reduces Sharing Intentions for False News Posts." Journal of Online Trust and Safety 1, no. 5 (2023). http://dx.doi.org/10.54501/jots.v1i5.100.

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Professional fact-checking of individual news headlines is an effective way to fight misinformation, but it is not easily scalable, because it cannot keep pace with the massive speed at which news content gets posted on social media. Here we provide evidence for the effectiveness of ratings of news sources, instead of individual news articles. In a large pre-registered experiment with quota-sampled Americans, we find that participants are less likely to share false headlines (and more discerning of true versus false headlines) when 1-to-5 star trustworthiness ratings were applied to news headl

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