Cable News Network Research Articles

Hybrid model for extractive single document summarization: utilizing BERTopic and BERT model

Extractive text summarization has been a popular research area for many years. The goal of this task is to generate a compact and coherent summary of a given document, preserving the most important information. However, current extractive summarization methods still face several challenges such as semantic drift, repetition, redundancy, and lack of coherence. A novel approach is presented in this paper to improve the performance of an extractive summarization model based on bidirectional encoder representations from transformers (BERT) by incorporating topic modeling using the BERTopic model. Our method first utilizes BERTopic to identify the dominant topics in a document and then employs a BERT-based deep neural network to extract the most salient sentences related to those topics. Our experiments on the cable news network (CNN)/daily mail dataset demonstrate that our proposed method outperforms state-of-the-art BERT-based extractive summarization models in terms of recall-oriented understudy for gisting evaluation (ROUGE) scores, which resulted in an increase of 32.53% of ROUGE-1, 47.55% of ROUGE-2, and 16.63% of ROUGE-L when compared to baseline BERT-based extractive summarization models. This paper contributes to the field of extractive text summarization, highlights the potential of topic modeling in improving summarization results, and provides a new direction for future research.

Hybrid model: IndoBERT and long short-term memory for detecting Indonesian hoax news

The world has entered an era that technology has developed far. Due to rapid technological development, information is easily spread. However, not all information spread through social media is factual information. Responding to this social phenomenon, we initiated to create a hoax detection system using the combined method of Indo bidirectional encoder representations from transformers (IndoBERT) and long short-term memory (LSTM). The dataset used in this study are obtained through the process scraping on the site turnbackhoax.id and cable news network (CNN) Indonesia. We decided to use the IndoBERT-LSTM method to detect hoaxes, using IndoBERT as the feature extractor and LSTM as the classification layer can be an effective method because of its advantages in managing and understanding Indonesian language. The results show that the IndoBERT-LSTM model achieved an accuracy of 93.2%, precision of 92%, recall of 89.7%, and F1-score of 90,8%. From a total of 5876 data composed of a total of 1998 factual news and 3878 hoax data. The hoax detection system using IndoBERT-LSTM is a promising approach for detecting hoaxes accurately and efficiently. This model has the potential to make a significant impact in the fight against the spread of Hoaxes.

Cable news advertising: Applying formal analysis to uncover current trends in self-promotional marketing

The emergence of 24/7 cable news channels within the US revolutionized news consumption, offering viewers accessibility beyond fixed broadcast schedules. While the phenomenon began with CNN, competitors quickly emerged, targeting different political ideologies and mirroring the heightened polarization that has altered American society. Cable news channels must market themselves effectively to their audiences through distinctive product differentiation strategies. Understanding these approaches provides insights into information dissemination, political communication, and audience engagement dynamics. This paper aims to conduct a formal media analysis of current self-promotional advertisements across the big three cable news networks within the US to derive insights into contemporary differentiation strategies crucial for monitoring the industry’s technological transition. As the rest of the world has begun to wrestle with increased polarization, this analysis will be relevant to anticipating how non-US news sources use ideology and values to position themselves within highly competitive markets.

Media Framing of African Refugee Migration to Europe: An Analysis of Narrative Constructions From Selected Global Media Houses in 2023

The framing of migrants from Africa to Europe on the global media is complex and quite obscure. The manner in which media tells a story is quite critical as it has the potential to influence the discernment of the public from different parts of the world. The main goal of the study was to examine how the global media houses portrayed African migrants to Europe in 2023.This was guided by two specific objectives:To examine the global media outlets' coverage of African refugee migration to Europe and to analyze the language used by the media to describe African refugees, assessing for bias, stereotypes, or stigmatization. The study was guided by the framing theory. The study employed a qualitative method design to conduct a detailed analysis of media framing surrounding African migration to Europe. Data was collected from all the global media houses with a sampling frame from three major media houses which included: The Cable News Network (CNN), The British Broadcasting Corporation (BBC), and the Eastern Africa. Data was analyzed using thematic content analysis. Findings indicated that, how global media houses cover the migration of people from Africa to Europe exposes the intricate narrative formed by a variety of both negative and positive views. While the CNN and BBC comprise varied perspectives to the issues, The Eastern Africa gives insight into the regional circumstances surrounding the migration problem as well as the humanitarian concerns. Across all the examined media houses (CNN, BBC, The Eastern Africa), there is an inclination towards portraying the African migrants in negative light, underscoring their impact on destination states. There were instances of Bias and stereotypes that could potentially lead to stigmatization. The study recommended that there is need for balanced reporting from media houses, there needs for some ethical considerations in reporting and that media houses should mainly focus on their role of creating awareness and advocacy.

The traction of moral virtues compared to risk-return fundamentals in fossil fuel-related investments

The fundamental view that investors require compensation aligned to the risk attributes they discern in their investments runs at odds with efforts to curb climate risks. Moral considerations should play an important role in climate-related investments (Hulme, 2014). However, in the case of fossil fuel investments, the moral virtues in decisions are blurred. This article aims to determine the extent of moral virtues in investment decisions involving fossil fuels amidst risk-return principles. Document analysis is used from a population of 60 banks identified as increasing or reducing fossil fuel financing from the Cable News Network website. In addition to a random sampling of banks, market prices, and ten-year government bonds data are obtained from Macrotrends and Yahoo.com websites to compute the cost of equity over a seven-year period using the capital asset pricing model (CAPM). The t-test proves that the required returns for increasing fossil fuel financing remain higher than those for decreasing, while regression reflects that the moral virtue gap remains an existential threat to climate mitigation. These results demonstrate that the curbing of climate risks remains elusive unless investors place moral considerations above monetary returns. In conclusion, the need for adequate monetary compensation for investing in fossil fuels far outweighs the moral obligation.

ENGAGEMENT STRATEGIES IN SOCIAL ACTORS' RESPONSES TO THE #ENDSARS PROTEST

Studies on the #EndSARS protest have examined the movement from a non-linguistic standpoint, providing perspectives on the human right violations leading up to the stifling and abrupt end of the protesters’ activities, the potential consequences of the government’s response in relation to foreign ties with global communities, and the economic implications of the protest on the nation. There exists a gap of knowledge in linguistic-related studies on the protest, especially on the techniques protesters employ in establishing meaning and building solidarity with the audience in the communication processes. The study, therefore, examined the engagement strategies in the responses of social actors to events following the #EndSARS protest, and James Martin and Peter White's Appraisal framework constituted the theoretical framework. For data, two newspaper reports on the Lagos State inquiry panel on the protest and the Nigerian government's response to Cable News Network (CNN) were purposively drawn from two Nigerian newspapers, and the analyses were carried out using a descriptive approach. The results showed that of the three selected social actors, only two efficiently engaged readers, providing lucid evidence to corroborate stance in the meaning-negotiation process. Negative connotations were evoked to disclaim, condemn, and disendorse other social actors in the entrenchment of propositional values, and authorial presence was extensively engaged to reveal authors’ evaluative stance.

The “Us vs. Them” Mentality: The Role of Affective Polarization in Deepening the Partisan Divide in Media Bias Perception

This study examines how affective polarization impacts the partisan divide in perceptions of cable news networks’ political biases. Results from a 2016 U.S. presidential election survey show that affective polarization deepens this divide, even after controlling for election involvement, partisanship strength, and cable news usage. Partisanship strength is more closely associated with in-party love than out-party hate, but out-party hate has a stronger association with media bias perception, indicating that out-group hate is more influential in shaping partisans’ media bias perception.

A Comparative Case Study of Media Coverage of the Opening and Closing Ceremonies of Winter Olympic Games Beijing 2022

Establishing a good national image and encouraging Chinese culture to spread out is a particularly important part of China's national cultural soft power construction. Winter Olympics Beijing 2022 is a global event, which is of immense value and significance for showing China's cultural charm and shaping the national image. Recently, research on the combination of framing theory and reports on the Olympic Games has significantly increased. However, there are few studies focusing on the cultural part of the analysis of reports. After making a summary of the coverage on China major events and Olympic Games of mainstream media, this study, based on the theory of cultural resistance and cultural-bound frame, chose mainstream media CNN (Cable News Network), BBC (British Broadcasting Corporation), and China Daily as samples, attempting to analyze their reports of the Opening and Closing ceremonies of the Winter Olympics Games Beijing 2022. Through the comparative analysis of Olympic coverage, this research shows that foreign media’s attitudes towards China have remarkably changed and investigates how Chinese culture is disseminated in some parts of the world to identify the factors affecting the dissemination of cultural elements. The research findings also revealed that coverage of BBC and CNN concerns more about China’s alleged abused human rights, while the part about Chinese cultural translation is limited. Only by having a comprehensive understanding of the process of transmitting culture can cultural translation be well-achieved. and it can help promote mutual communication and cooperation of the world.

News of Noam: Unpacking Media Coverage of Chomsky

Media inform and obfuscate. Corporate motives influence how news is neglected, reported, and contextualized. It is informative to examine the extent to which news content varies based on source and period. As a longstanding critic of news media, and a leading public intellectual, Noam Chomsky is a worthy case study. Three author-created, corpora of news articles referencing Noam Chomsky were created. The corpora contained articles from Agence France-Presse (AFP; n = 54), The Associated Press (AP; n = 48), and the Cable News Network Wire (CNNW; n = 6), from the years 2012 to 2022. Number and length analyses, along with hypothesis tests, established the degree of similarity existing among the articles of the three news wire services and periods. Analysis of variance showed that news source was significant in terms of both the number of articles (H2), F(2,6) = 5.916, p = 0.038., and article length (H4), F(2,105) = 23.936, p = 0.000 ( = 0.05). Little commonality in content or framing was established among the three news sources or periods in terms of top words or bigrams of merit. Whereas there were differences in the absolute values and degree of change, each of the three news sources contained a slightly negative average sentiment score when using the AFINN lexicon. The results of this study, while limited to a single case, are illustrative of broader concerns and are potentially useful for those engaged in media studies, politics, rhetoric, organizational management, and the social sciences.

A Pragmatic Study of Euphemisms in Cable News Network (2003- Present)

A Pragmatic Study of Euphemisms in Cable News Network (2003- Present)

The Stability of Cable and Broadcast News Intermedia Agenda Setting Across the COVID-19 Issue Attention Cycle

ABSTRACT In today’s fragmented media environment, it is unclear whether the correspondence between media agendas that characterizes intermedia agenda setting persists. Through a combination of manual and computerized content analysis of 486,068 paragraphs of COVID−19 coverage across 4,589 cable and broadcast news transcripts, we analyze second and third-level attribute agenda setting, both in terms of central themes and aspects. Through the lens of the issue attention cycle, we assess whether relationships among media agendas change over time. The results show that even in a fragmented media environment, there is considerable evidence of intermedia agenda setting. The attribute agendas were largely similar across outlets despite the similarity slightly decreasing over time. The findings suggest that there was only modest evidence for the prominent perception of fragmented coverage for cable and broadcast news networks’ attribute agendas concerning the COVID−19 pandemic.

CABLE NEWS NETWORK (CNN) ARTICLES CLASSIFICATION USING RANDOM FOREST ALGORITHM WITH HYPERPARAMETER OPTIMIZATION

The growth of news articles on the internet occurs in a short period with large amounts so necessary to be grouped into several categories for easy access. There is a method for grouping news articles, namely classification. One of the classification methods is random forest which is built on decision tree. This research discusses the application of random forest as a method of classifying news articles into six categories, these are business, entertainment, health, politics, sport, and news. The data used is Cable News Network (CNN) articles from 2011 to 2022. The data is in form of text and has large amounts so good handling is needed to avoid overfitting and underfitting. Random forest is proper to apply to the data because the algorithm works very well on large amounts of data. However, random forest has a difficult interpretation if the combination of parameters is not appropriate in the data processing. Therefore, hyperparameter optimization is needed to discover the best combination of parameters in the random forest. This research uses search cross-validation (SearchCV) method to optimize hyperparameters in the random forest by testing the combinations one by one and validating those. Then we obtain the classification of news articles into six categories with an accuracy value of 0.81 on training and 0.76 on testing.

TV News and the Military: Exploring Media Frames of an American Institution

This exploratory study analyzes television news transcripts ( N = 300) to examine how broadcast news networks (ABC, CBS, and NBC) and cable news networks (CNN, Fox, and MSNBC) cover military veterans and service members in news programming. Findings show that the stories and sources of broadcast news networks focused on veterans and service members or their families, while cable news networks relied on legislative issues, politicians and other elite sources. Other findings and recommendations are discussed.

The Gulf War as Simulacra: An Analysis of Vishwa Ghatana [World Affairs] on Nepal Television

Between early August 1990 and the end of February 1991, the coverage of the Persian Gulf War on Cable News Network (CNN) television showcases a comprehensive example of simulacra. Based on Jean Baudrillard's ideas of postmodern simulacra and simulation, this paper attempts to demonstrate how the simulacra of the Gulf War appeared before the Nepali audience through a domestic nationwide channel. The major source of news and perspectives of the contents of Nepal Television's world affairs program were the reports and images of CNN. The paper, with an approach to looking into the event retrospectively, demonstrates a case where CNN used a comparatively small television channel in a developing country to disseminate its cultural product to the local audience in their vernacular language. Taking Nepal Television (NTV) and its Vishwa Ghatana [World Affairs], a program dedicated to the world affairs as a case in point, this paper examines relationships between the rhetoric and reality in the theoretical and conceptual frame of simulacra and simulation based on Baudrillard’s ideas.

Ext-ICAS: A Novel Self-Normalized Extractive Intra Cosine Attention Similarity Summarization

With the continuous growth of online news articles, there arises the necessity for an efficient abstractive summarization technique for the problem of information overloading. Abstractive summarization is highly complex and requires a deeper understanding and proper reasoning to come up with its own summary outline. Abstractive summarization task is framed as seq2seq modeling. Existing seq2seq methods perform better on short sequences; however, for long sequences, the performance degrades due to high computation and hence a two-phase self-normalized deep neural document summarization model consisting of improvised extractive cosine normalization and seq2seq abstractive phases has been proposed in this paper. The novelty is to parallelize the sequence computation training by incorporating feed-forward, the self-normalized neural network in the Extractive phase using Intra Cosine Attention Similarity (Ext-ICAS) with sentence dependency position. Also, it does not require any normalization technique explicitly. Our proposed abstractive Bidirectional Long Short Term Memory (Bi-LSTM) encoder sequence model performs better than the Bidirectional Gated Recurrent Unit (Bi-GRU) encoder with minimum training loss and with fast convergence. The proposed model was evaluated on the Cable News Network (CNN)/Daily Mail dataset and an average rouge score of 0.435 was achieved also computational training in the extractive phase was reduced by 59% with an average number of similarity computations.

Selection, Trust, and the Effects of Cable News Consumption

Using two experimental studies, we examine how the selection and consumption of cable news influences news consumers’ cognitive processing, attitudes, and policy preferences. As expected, participants overwhelmingly self-selected into an ideologically aligned cable news network. Then, ideologically congruent messaging from Fox News and MSNBC was likely to prompt higher levels of agreement and lower levels of disagreement for those with mid- and high levels of trust in their selected cable news network. Our findings indicate a reinforcing spiral effect among both MSNBC and Fox News consumers who have high levels of trust in the cable news network they select.

Video news framing: the contribution of audiovisual translation

ABSTRACT In recent years, studies on media accessibility in general, and audiovisual translation in particular, have mostly focused on the reception of various audiovisual translation modes among target viewers. This study embraces wider social and ideological concerns to integrate audiovisual translation into the video news framing process. It aims to examine the role of audiovisual translation methods in mediating the framed representation that news broadcasters set out to achieve. It mainly focuses on the controversial operation of China’s China Global Television Network (CGTN) through the complex lenses of audiovisual translation in comparison with the parallel output of British Broadcasting Corporation (BBC) and Cable News Network (CNN). Three recent mini case studies are devised to describe the modes of audiovisual translation involved and explicate how they accentuate or modify the contested narratives of video news framing. The findings reveal that there is barely any value-free, random deployment of audiovisual translation. All the audiovisual translation methods under study are part of the selection process for the framing purposes of each news organisation. Moreover, this study found that CGTN has most effectively employed nuanced audiovisual translation techniques to highlight the societal significance of its framing perspectives despite the prevailing critiques of its executive operation.

News Sources and Climate Change Attitude among Students: Implications for Libraries

ABSTRACT This study aimed to investigate the relationship between news sources and climate change attitudes among students at Adeleke University and its implications on libraries. The study utilized a descriptive survey design with a questionnaire to collect data from 688 respondents. The majority of the participants showed an unconcerned attitude towards climate change. The sources from which students obtained climate change news were NTA, AIT, British Broadcasting Corporation (BBC), Cable News Network (CNN), TVC News, and Punch. It was found that sourcing climate change news from specific local and international news outlets such as Fox News, Al Jazeera, Euro News, Sky News, CNBC Africa, Channels, AIT, Vanguard, Nigeria Tribune, Guardian Nigeria, Silverbird Television, TVC News, Arise News, and NTA was strongly associated with an unconcerned attitude towards climate change. The study concludes that libraries should provide more news sources that can raise awareness about climate change in Nigeria to address the prevalent unconcerned attitude towards climate change.

IP Media Infrastructure—Opportunities, Observations, and Challenges

As guest editor, I specifically reached out to Gordon Castle for a contribution to this issue of the journal because it is important to hear from different voices as we work our way through the transition from the serial digital interface (SDI) to Internet Protocol (IP) systems. Rarely, if ever do we get to hear directly from the business/technology people who are making key decisions as we transition to IP. He has recent, direct relevant experience as one of the key decision-makers for a major refresh at a large live sports facility in Europe. He is a senior technical strategist with years of experience working both on the user side at Cable News Network (CNN) and later Eurosport and also on the vendor side at Ericsson. The views and opinions in this article are based on his years of experience, and the points he raises are ones that we, as media engineers and experts, should ponder, both with our employers and in society. Castle asks a number of questions in this article. He answers some of them but leaves others as open challenges. Perhaps, someone would like to tackle one or more of these in an upcoming issue of the journal?—Brad Gilmer, Guest Editor

A novel classification and clustering algorithms for intrusion detection system on convolutional neural network

At present data transmission widely uses wireless network framework for transmitting large volume of data. It generates numerous security problems and privacy issues which laid a way for developing IDS. IDS act as preventive technique in securing computer networks. Previously there are numerous metaheuristic and deep learning algorithms used in IDS for detecting threats. Some are affected by dynamic growth of feature spaces and others are degraded in performance during detection of threats. One fine-grained model for intrusion detection can be developed by selecting accurate features and testing them with the intelligent algorithms. Based on these explorations, in this research IDS is implemented with intelligence from preprocessing to feature classification. At first stage, data preprocessing is done using binning concept to reduce noise. Secondly feature selection is done dynamically using dynamic tree growth algorithm with fire fly optimization techniques. Finally, these features are processed using DTB-FFNN for detecting anomalies perfectly. This DTB-FFNN is evaluated with popular KDD dataset. Our proposed model cable news network (CNN)-classification is compared with existing intelligent techniques: feed forward deep neural network, support vectors machines, decision tree, and CNN-clustering is compared with k-means, density-based spatial clustering of applications with noise (DBSCAN). The experimental outcome proves that dynamic tree based FFNN and CNN-clustering produce higher accuracy than the existing models.