U.S. immigration and media bias surrounding the reporting of the Deferred Action for Childhood Arrivals (DACA) and Deferred Action for Parents of Americans and Lawful Permanent Residents (DAPA) immigration policies

In 2012, President Barack Obama used his executive power to bypass Congress and unilaterally pass a controversial immigration policy called the Deferred Action for Childhood Arrivals (DACA) program and two years later its successor, the Deferred Action for Parents of Americans and Lawful Permanent Residents immigration policy. This MRP explores whether a media slant is salient in the editorial reporting surrounding these policies from two major U.S. political networks‐‐ The FOX News Channel (FOX) and the Cable News Network (CNN). Previous academic research (Iyengar & Hahn, 2009; Stroud, 2007) has indicated that CNN’s audience tends to be left-leaning favoring the Democratic Party, while rightleaning conservative Republicans tune into FOX for their political information (Gil de Zúñiga, Correa and Valenzuela, 2012). Keeping this in consideration, would the political networks tailor its digital editorial content to mimic its audiences’ political preference? Borrowing from Benson and Wood’s (2015) media frames surrounding undocumented immigration, a framing analysis and a textual content analysis were employed on the digital editorial content published by FOX and CNN from July 2014 and February 2015. The findings revealed that both networks published messaging aligned with its audiences’ political affiliation. The FOX News Channel emphasized how undocumented immigrants were a problem for society and authorities and published content which contained anti‐Democrat rhetoric and was acutely critical of President Obama. Conversely, the framing analysis revealed the Cable News Network was more likely to accentuate the problems for immigrants and defend President Obama and his unilateral exercises of constitutional powers.

U.S. immigration and media bias surrounding the reporting of the Deferred Action for Childhood Arrivals (DACA) and Deferred Action for Parents of Americans and Lawful Permanent Residents (DAPA) immigration policies

In 2012, President Barack Obama used his executive power to bypass Congress and unilaterally pass a controversial immigration policy called the Deferred Action for Childhood Arrivals (DACA) program and two years later its successor, the Deferred Action for Parents of Americans and Lawful Permanent Residents immigration policy. This MRP explores whether a media slant is salient in the editorial reporting surrounding these policies from two major U.S. political networks‐‐ The FOX News Channel (FOX) and the Cable News Network (CNN). Previous academic research (Iyengar & Hahn, 2009; Stroud, 2007) has indicated that CNN’s audience tends to be left-leaning favoring the Democratic Party, while rightleaning conservative Republicans tune into FOX for their political information (Gil de Zúñiga, Correa and Valenzuela, 2012). Keeping this in consideration, would the political networks tailor its digital editorial content to mimic its audiences’ political preference? Borrowing from Benson and Wood’s (2015) media frames surrounding undocumented immigration, a framing analysis and a textual content analysis were employed on the digital editorial content published by FOX and CNN from July 2014 and February 2015. The findings revealed that both networks published messaging aligned with its audiences’ political affiliation. The FOX News Channel emphasized how undocumented immigrants were a problem for society and authorities and published content which contained anti‐Democrat rhetoric and was acutely critical of President Obama. Conversely, the framing analysis revealed the Cable News Network was more likely to accentuate the problems for immigrants and defend President Obama and his unilateral exercises of constitutional powers.

Media Images of Islamophobia on Cable News Network (CNN) and Implications for International Relations

This study focuses on media images of islamophobia as portrayed by Cable News Network (CNN) and its implications for international relations. The study employed qualitative methodology. Data was collected using key informant interviews, while documentary search was done using CNN current affairs videos. The study findings indicated that the media has the power to influence human perceptions towards stereotyping Islam as a terrorist organisation and conflating the Islamic religion and the Muslim culture with terrorism. The study also found out that islamophobia really has a relationship with how Muslims are represented in the media. The study recommends that media houses should have media ethics, laws and policies which force journalists to be more accountable and objective when reporting issues of religion, race and culture as a way of eliminating offensive communication and religious intolerance.

Drowning Out the Message: How Online Comments on News Stories About Nike’s Ad Campaign Contributed to Polarization and Gatekeeping

This study examined audience commentary on Fox News, Cable News Network, and MSNBC’s YouTube and Facebook platforms associated with news stories on Nike’s selection of controversial former National Football League quarterback Colin Kaepernick as the spokesman for its 2018 campaign. The study, using the theory of gatekeeping as a starting point, sought evidence for a drowning effect, in which the audience strayed from the primary message of the journalism presented to it. Content analysis revealed a significant drowning effect across platforms and outlets.

Deep Learning Based Abstractive Text Summarization: Approaches, Datasets, Evaluation Measures, and Challenges

In recent years, the volume of textual data has rapidly increased, which has generated a valuable resource for extracting and analysing information. To retrieve useful knowledge within a reasonable time period, this information must be summarised. This paper reviews recent approaches for abstractive text summarisation using deep learning models. In addition, existing datasets for training and validating these approaches are reviewed, and their features and limitations are presented. The Gigaword dataset is commonly employed for single-sentence summary approaches, while the Cable News Network (CNN)/Daily Mail dataset is commonly employed for multisentence summary approaches. Furthermore, the measures that are utilised to evaluate the quality of summarisation are investigated, and Recall-Oriented Understudy for Gisting Evaluation 1 (ROUGE1), ROUGE2, and ROUGE-L are determined to be the most commonly applied metrics. The challenges that are encountered during the summarisation process and the solutions proposed in each approach are analysed. The analysis of the several approaches shows that recurrent neural networks with an attention mechanism and long short-term memory (LSTM) are the most prevalent techniques for abstractive text summarisation. The experimental results show that text summarisation with a pretrained encoder model achieved the highest values for ROUGE1, ROUGE2, and ROUGE-L (43.85, 20.34, and 39.9, respectively). Furthermore, it was determined that most abstractive text summarisation models faced challenges such as the unavailability of a golden token at testing time, out-of-vocabulary (OOV) words, summary sentence repetition, inaccurate sentences, and fake facts.

Strategic Functions in CNN’s Media Discourse An Ideological Strategy To Win People’s Support A Critical Discourse Analysis Study

The present study investigates the possibility of utilizing the four strategic functions of political discourse initiated by Chilton and Schaffner (1997) to analyze media discourse. The paper is concerned with how Cable News Network (CNN) employs the four strategic functions within its media discourse to convey its media message to its readers, reflecting the concept of ‘Self’ and ‘Other’. Hence, this research contributes to the realization of strategic functions notion in media discourse, in general, CNN’s news discourse, in particular, by analyzing presupposition and the hidden ideologies behind. It seeks to answer the following question: Can strategic functions be established and utilized within the media discourse to convey ideological media message to the recipients? van Dijk’s theory of Ideological Square (1998) will be utilized to clarify CNN’s presentation of positive ‘Self’ and negative ‘Other’ (in and out groups). Wodak’s historical discourse approach for CDA (2009) will be integrated to provide the readers with the needed background information to understand the text. Fairclough’s 2-dimentional approach for CDA (1995) will be employed to organize the process of analysis. The linguistic analysis of CNN’s news text that concerns with Arab-spring Yemen approves that the strategic functions concept can be detected within media discourse.

Pembentukan Opini Publik oleh Media: Cable News Network (CNN) Indonesia dalam 2018 North Korea–United States Singapore Summit

It is undeniable that media is mostly used as a propaganda tool of the political elite to achieve the national interest of its country. This research identifies and explains the establishment of CNN Indonesia in making public opinion as one of the extensions on CNN International regarding the Kim Jong Un and Donald Trump Summit in Singapore in 2018. This research utilized: agenda-setting, priming and framing theory to find out the detail process of forming public opinion by CNN Indonesia. This research also utilizes qualitative research methods based on Robert E. Stake’s explanation. The finding suggests that CNN Indonesia, through its agenda-setting, was able to make the summit has an urgency to be discussed in public. Its priming is able to increasing awareness of the importance of denuclearization and world peace which has been Indonesa’s foreign policy agenda. Through its framing, CNN Indonesia attempted to construct public opinion that the United States able to utilize its super power label in order to perquisite many parties, namely creating the world peace.

Globalization, the Media, and Challenges of Illegal Migration for Africa

There is a growing concern about African migrants who risk their lives to embark on hazardous journeys across dozens of borders and the treacherous waves of the Mediterranean Sea in search of a better life in Europe. Cable News Network footage of a live auction in Libya, where black youths were presented to north African buyers as potential farmhands and sold for as little as $400 confirm the fears and brought to the fore the ugly reality of the plight of illegal migrants. Aside, the narratives in the media about migration also give cause for concern. In the midst of the general invisibility of illegal migrants in the media, most portrayals refer to migrants in connection with themes of ‘trafficking', ‘prostitution', ‘slavery', and ‘death' because cases of enslavement, drowning, and killings of trafficked Africans in search of utopia greener pastures flood newspapers, magazines, and broadcast space. It is against this backdrop that this chapter proffers solutions and recommends ways to halt illegal migration and change media narratives about migration in Africa.

Anticipating Stock Market of the Renowned Companies: A Knowledge Graph Approach

An increasing number of the renowned company’s investors are turning attention to stock prediction in the search for new efficient ways of hypothesizing about markets through the application of behavioral finance. Accordingly, research on stock prediction is becoming a popular direction in academia and industry. In this study, the goal is to establish a model for predicting stock price movement through knowledge graph from the financial news of the renowned companies. In contrast to traditional methods of stock prediction, our approach considers the effects of event tuple characteristics on stocks on the basis of knowledge graph and deep learning. The proposed model and other feature selection models were used to perform feature extraction on the websites of Thomson Reuters and Cable News Network. Numerous experiments were conducted to derive evidence of the effectiveness of knowledge graph embedding for classification tasks in stock prediction. A comparison of the average accuracy with which the same feature combinations were extracted over six stocks indicated that the proposed method achieves better performance than that exhibited by an approach that uses only stock data, a bag-of-words method, and convolutional neural network. Our work highlights the usefulness of knowledge graph in implementing business activities and helping practitioners and managers make business decisions.

Media Images of Islamophobia on Cable News Network (CNN) and Implications for International Relations

This study focuses on media images of islamophobia as portrayed by Cable News Network (CNN) and its implications for international relations. The study employed qualitative methodology. Data was collected using key informant interviews, while documentary search was done using CNN current affairs videos. The study findings indicated that the media has the power to influence human perceptions towards stereotyping Islam as a terrorist organisation and conflating the Islamic religion and the Muslim culture with terrorism. The study also found out that islamophobia really has a relationship with how Muslims are represented in the media. The study recommends that media houses should have media ethics, laws and policies which force journalists to be more accountable and objective when reporting issues of religion, race and culture as a way of eliminating offensive communication and religious intolerance.