Exploring Double Subjectivity through News Frames in Online News Sources: A Network Approach

Ubiquitous access to internet has resulted in more and more people going online to get their daily dose of news. In a 2010 survey conducted by the Pew Project for Excellence in Journalism, 41% of the respondents said they get most of their news online, 10% more than those who said they got most of their news from a newspaper. A lot of socio-technical factors have contributed to this phenomenal rise in adoption of online news in recent years. One of the biggest reasons why people are increasingly reading news online is because it facilitates discussion with peers (Nguyen 2010), offering different viewpoints which aid in forming a rounded personal opinion about the news story. The Pew survey found that 37% of online news users (and 51% of 18-29 year olds) think that commenting on news stories is an important feature to have. A lot of people tend to shape their opinion by reading discussion comments, reflective articles, blogs and even tweets about the news. Hence, an increasing number of people rely on online sources of news – be it news websites or news aggregator services like Digg, Reddit, Google Reader, Flipboard, Pulse etc. The problem with these news websites and aggregators is that the only way people can gather public opinion is by actively searching through the endless stream of comments and feeds, filtering out spam (which is a growing problem) and then reading the relevant posts. A top trending story on Twitter will typically see multiple tweets per second, and keeping up with the rapid flow of incoming tweets is quite cumbersome and cognitively taxing. Hence it becomes increasingly difficult and time consuming for someone who wants to get the pulse of the people affected by a news story. Furthermore, in certain scenarios people might want to look at more fine grained opinions. Currently, there is no elegant way to extract geographic and demographic impact of a news story. What is the public sentiment in Indonesia about the Arab Spring? How did the public opinion about the Wikileaks disclosures change as the story unfolded during the course of a year? It is very difficult and tedious to observe such patterns using the currently available news providers. This work attempts to solve these problems by proposing a news aggregator platform which pulls news stories from various sources and also aggregates public responses, reflections, opinions and sentiments associated with those stories. This data is presented in ways that are easily understandable so readers can make better sense of the stories unfolding across the globe. Such a news aggregator platform that gathers and display public opinion and sentiments about a story, must deal with various challenges – 1. Opinions are very subjective. Different people feel about a story in different ways. With such an enormous amount of diverse opinions and subjectivity, how can we possibly aggregate the responses into something that makes sense as a whole? ________________________________ Acknowledgements: Prof. Yardi, S. for the guidance Sethi, P. (2013). Public opinion aggregation by annotation and tagging of online news stories. iConference 2013 Proceedings (pp. 891-894). doi:10.9776/13453 Copyright is held by the author. iConference 2013 February 12-15, 2013 Fort Worth, TX, USA 892 2. There isn’t really a unified web standard for expressing opinion (in textual form). Some people tweet in 140 characters, while others write elaborate blog posts. Some websites employ tags which a reader can use to define and classify their public opinion, while others rely on threaded comments and comment ranking systems. How can a platform be flexible enough to adapt to all these varied standards so that it can extract valuable data from various sources? Perhaps the platform can create a new standard of expression on the web which is flexible and comprehensive enough to be used to express diverse views about every news story in the world. 3. How to filter out spam while extracting public opinion? 4. Once the platform has access to the data it needs, how should it be displayed to the reader in a way that makes sense? What forms of visualizations, illustrations and graphical representations can be employed to give the reader a holistic view of how people feel about a story? 5. How can the platform determine and convey effects of geographical, demographic and temporal variations as the story unfolds? These are just a few out of possibly many issues which must be dealt with. Previous research on similar public opinion aggregation services has greatly focused on natural language processing, data mining and text categorization and clustering. Xiaojun (2010) proposed a framework for crawling the web for comments and applying various data mining algorithms on the data to extract relevant information. Diakopoulos and Shamma (2010) used tweets posted in conjunction with the live presidential debate between Barack Obama and John McCain to gauge public opinion. Brody and Diakopoulos (2011) studied the use of word lengthening to detect sentiment in microblogs. This research proposes a solution – The Opinionated Reader, which relies on sentiment tags and annotations associated with a news story. The essential idea is to create a commenting, discussion and sharing plug-in which can be used by news websites and aggregators as a commenting solution for their news pages. Users wanting to share or comment on a news story through the plug-in are asked to tag the news story with sentiment tags and annotate the story with their reaction (happy/positive or sad/negative). These tags and annotations are stored, aggregated and linked to each news story. A mobile application provides the front-end interface for users to access the news stories and the aggregated sentiment associated with each story. The basic architecture is explained as follows: The Opinionated Reader – Mobile/Tablet App The app fetches news articles from various web sources, based on the interests and preferences configured by the user. In every news article, a portion of the screen real estate is reserved for Opinions which shows graphical visualizations and illustrations of the public opinion surrounding the news story. These visualizations include: A Sentiment Graph indicating the popular tags associated with the story (E.g.: “Shocking”, “Inspiring”, “Amusing” etc.). See Figure 1 for example visualization. A Positivity Graph which plots the level of positivity associated with the story on a time scale from when the news broke. See Figure 2 for example visualization. Figure 1. Example Sentiment Graph Figure 2. Example Positivity Graph iConference 2013 February 12-15, 2013 Fort Worth, TX, USA 893 The user can choose to see these visualizations for a particular time period in the evolution history of the news story, or for a specific country. The app also facilitates people to tag and annotate news articles from within its interface. The Opinionated Reader – Commenting and Sharing Web Plug-in These days, a common way of adding discussion and commenting functionality to news websites is by using 3 rd party services (like DISQUS). The Opinionated Reader is a similar service which can be embedded into the news articles of various news websites to enable commenting and sharing. When someone wishes to comment on an article, the comment is directed through this plug-in, which allows the users to annotate the article with the sentiment tags and reactions along with their comments. The Opinionated Reader saves this information along with the commenter’s location and date of comment (See Figure 3). Figure 3. The Opinionated Reader Web Plug-in 'Add Comment' dialog mockup The Opinionated Reader – Back-end The Back-end maintains a database of news items extracted from RSS feeds of various news websites. Each news article is linked with the sentiment tags and reaction/positivity annotations extracted from the comments and annotations gathered by the commenting plug-in. This data is used by the mobile/tablet app to generate visualizations (Sentiment Graph and Positivity Graph). The back-end also performs data mining on the tags and annotations for geographies and tracks the opinions across time. The back end system responds to queries received from the mobile app with the news story and associated tags and annotations, which are then rendered by the mobile app for the user. iConference 2013 February 12-15, 2013 Fort Worth, TX, USA 894 Discussion and Conclusion This design idea is still in a nascent state and has long hours of research, brainstorming, designing and development to go before it can be realized into something tangible. Twitter has grown exponentially in importance as a news source and it would be vastly valuable to integrate Twitter with The Opinionated Reader. Possibilities include use of special hash tags and natural language processing of tweets to extract public sentiment. The current design supports only two reaction annotations – positive and negative. Not every news story fits this annotation paradigm. Further research about human reactions to news stories might unveil interesting insights which would help zero in on a more robust annotation rubric. Lastly, since this service is envisioned to be non-curated and non-moderated, the value served by the app depends on the users themselves. Greater adoption will lead to more annotations and tags, which translates into a more accurate public opinion as presented to the user.

This study examines the impact of technology on public opinion through online news consumption, analyzing the relationship between online news sources and public opinion. Utilizing secondary data from existing academic datasets, this research investigates how demographic characteristics influence online news consumption and its subsequent effect on public opinion. The study, through content analysis, identifies trends and patterns in online news consumption and its influence on public opinion. Agenda-Setting Theory, Framing Theory, and Uses and Gratifications Theory provides theoretical grounds to the entire research work along with other theoretical concepts like Echo Chambers, Misinformation and Social Media Dynamics. Findings shed light on the significant role technology plays in shaping public opinion, highlighting the influence of online news sources on public perception. The study reveals variations in online news consumption patterns across demographics, impacting public opinion. Social media emerges as a critical factor in shaping public opinion, with online news sources demonstrating agendasetting capabilities. This research contributes to the understanding of technology's impact on public opinion, informing strategies for media outlets, policymakers, and communication practitioners. By elucidating the 2 | P a g e complex dynamics between online news consumption and public opinion, this study provides valuable insights for navigating the rapidly evolving media landscape.

During the Trump administration, from 2017 to 2020, and Donald Trump as a presidential candidate in 2016, the issue of immigration was central to his policies and rhetoric. Using topic modeling and network analysis, we studied over 30,064 online news articles from 13 online news sources in the U.S. between January 2017 and December 2020 to examine the news frame usage and depiction of immigration by these different publications. Our results present evidence of an ideological frame highly used by Liberal and Conservative online news outlets, which shows a tendency to discuss this critical topic considering ideology-based issues. However, we also offer proof of news coverage of immigration that can deliver nuanced and less ideologically charged reporting.

Online news providers struggle to pull audiences behind their paywalls in order to complement (insufficient) advertising revenue with sales income. But what do consumers think about paying for—once freely available—online news content and how does it affect their payment behavior? The present study complements the existing research by developing a typology of attitudes and behavior toward paying for online news content based on qualitative interviews (n = 64) conducted in Germany. Five types of mindsets were identified: paying subscribers, free riders, promisers (users who do not pay but announce to do so in the future), occasional buyers, and convinced deniers. The typology organizes the diversity in positions toward paid news content, thus enabling a more powerful explanatory research on consumer valuation and monetization in journalism. In addition, it can assist online news providers in developing targeted strategies for converting online readers into purchasing customers.

In our contemporary digital landscape, news consumption has transitioned largely to online platforms, shaping public opinion and influencing societal discourse. However, this shift has also led to the proliferation of misinformation and fake news, which can have profound consequences on public perception and decision-making processes. Moreover, the rise of social media has accelerated the dissemination of news, amplifying the impact of false information.In response to these challenges, this project focuses on developing a robust framework for evaluating news websites using advanced techniques in Artificial Intelligence (AI), Natural Language Processing (NLP), and Machine Learning (ML). The primary objective is to empower users with the ability to discern between credible and unreliable sources of information by performing binary classification of news articles.Through the utilization of sophisticated algorithms and datasets, our system aims to analyse the content of online news articles and assess their authenticity. By leveraging AI and ML models, users will be equipped with tools to identify potentially misleading or fabricated news stories, thereby promoting critical thinking and informed decision-making.Key features of the proposed system include the classification of news articles as either authentic or fake, as well as an evaluation of the credibility of the websites publishing the news. By harnessing the power of AI-driven analysis, this project endeavours to mitigate the spread of misinformation and enhance trust in online news sources.

Medical research news provided through the World Wide Web is easily accessible to the general public. Thus, it is necessary to understand how research findings released from online news sources are portrayed. The sample includes articles (n=205) published between January 1, 2010 and June 18, 2010 in top online news sites with competitive traffic rankings in the United States as determined by Alexa.com. Google Reader was used to find health-related news articles corresponding to the relevant news sources. Data analysis was performed using SPSS with two-tailed significance values of the χ(2) statistic. A content analysis (n=205 stories) revealed that the majority of complementary and alternative medicine (CAM) modalities for cancer discussed in top online news sources are classified as nutritional therapeutics, and the cancer topic that appeared most frequently was that of prevention. General oncology was the most frequently cited cancer type that was discussed with regard to CAM. Medical journals were the most frequently cited source in CAM/cancer news articles. The majority of news stories on CAM/cancer were neutral in tone, and the relationship between tone and evidence type reported was statistically significant. Observational studies rather than randomized controlled trials were the predominant form of evidence provided for research findings. Overall, the quality of how online news sources report research findings on complementary and alternative medicine therapies for cancer is fairly high. However, certain top online media sources are more reliable and informative than others when it comes to reporting about CAM remedies for cancer.

It is increasingly acknowledged that high income countries do not enjoy universal drinking water coverage, with pockets of water insecurity persisting close to areas that have had high-quality, centralized water provision for half a century or more. The reasons behind this are complex, and effective ways to improve water security in these areas defy simple prescriptions. Nuanced measures that disaggregate the different facets of water insecurity are especially important as we attempt to understand and, crucially, improve water security in high-income countries. In this paper, we describe a qualitative protocol for assessing water insecurity at the household level in a study conducted in rural and unincorporated settlements in the Southwestern United States. The study focuses on households connected to small, piped water systems; households that rely on hauled water, individual wells, and other sources; and those who are withdrawing from centralized systems. The goals of this study are: 1) to measure a baseline for individual households’ water security; 2) identify mechanisms for coping with poor water insecurity; 3) evaluate changes in water insecurity following co-designed interventions. We include in this protocol three codebooks for the coding and analysis of interview data, to make them available for use by other water insecurity researchers. The design embeds the interventions into the study without pre-supposing the nature of the interventions. The socio-technical interventions will be co-designed by the study team, project partners, and the communities themselves. By identifying and working with communities experiencing different water insecurity challenges the study aims to directly increase their water security and develop a deeper understanding of the drivers of and responses to water insecurity in the Southwestern United States.

The rapid advancement of technology and the widespread use of the internet have brought significant positive and transformative impacts across various aspects of human life, including finance, healthcare, education, and the media industry. One notable consequence of information transparency is the vast availability and large-scale exchange of data. However, this also presents new challenges, particularly in the spread of misleading content such as disguised advertorials that resemble genuine news. This threatens the objectivity of the information received by the public. To address this issue, an automated solution is needed to identify the distinguishing characteristics of advertorials in online news content. This study proposes a deep learning approach using the Convolutional Neural Network (CNN) model to detect sentiment as an indicator of advertorial content. CNN is a widely used deep learning model for processing sequential and spatial data, capable of automatically learning features from text. The dataset comprises news articles categorized by advertorial traits, such as positive or neutral sentiment, persuasive language, and promotional content highlighting specific entities. The data undergo several processing stages, including text preprocessing, tokenization, padding, and CNN model training. Model performance is evaluated using accuracy, precision, recall, and F1-score. The experimental results show a validation accuracy of 84%, although overfitting issues were observed. Despite ongoing limitations, such as restricted data and suboptimal parameter tuning, the findings suggest that the CNN model has potential for automatically detecting advertorial content and can serve as a basis for future research using more advanced models and refined parameter adjustments.

We propose a framework for understanding how the Internet has affected the U.S. political news market. The framework is driven by the lower cost of production for online news and consumers' tendency to seek out media that conform to their own beliefs. The framework predicts that consumers of Internet news sources should hold more extreme political views and be interested in more diverse political issues than those who solely consume mainstream television news. We test these predictions using two large datasets with questions about news exposure and political views. Generally speaking, we find that consumers of generally left‐of‐center (right‐of‐center) cable news sources who combine their cable news viewing with online sources are more liberal (conservative) than those who do not. We also find that those who use online news content are more likely than those who consume only television news content to be interested in niche political issues.

This study is concerned with the news values and working practices that inform the creation of interactive infographics in UK online news. The author draws upon organisational theory in journalism studies, and considers how conventional journalistic news values compare with best practice as espoused in different literatures within this field. A series of open-ended, depth interviews with visual news journalists from the UK national media were undertaken, along with a short-term observation case study at a national online news publisher. Journalistic and organisational norms are found to shape the selection, production, and treatment of interactive graphics, and a degree of variation is found to exist amongst practitioners as to definitions of quality in this field. Some news stories are considered to be better suited to rendering in interactive form than others. The availability of “big data” does not drive decision-making in itself, but some numbers are considered more newsworthy than others. Budgetary constraint drives practice and limits potential in this field. Risk aversion, embodied in various forms; from the use of templates, to a perceived need to avoid audience complaint, is found to dampen experimentation. Detailed audience research was found to inform the choice of methods used in data visualisation at one national news producer. This warrants further investigation as to how audiences engage with news interactives, and what the framing of news in certain (preferred) data visualisation formats means in terms of how news is understood.

Wars and conflicts have constituted major events throughout history. Despite their importance, the general public typically learns about such events only indirectly, through the lens of news media, which necessarily select and distort events before relaying them to readers. Quantifying these processes is important, as they are fundamental to how we see the world, but the task is difficult, as it requires working with large and representative datasets of unstructured news text in many languages. To address these issues, we propose a set of unsupervised methods for compiling and analyzing a multilingual corpus of millions of online news documents about armed conflicts. We then apply our methods to answer a number of research questions: First, how widely are armed conflicts covered by online news media in various languages, and how does this change as conflicts progress? Second, what role does the level of violence of a conflict play? And third, how well informed is a reader when following a limited number of online news sources? We find that coverage levels are different across conflicts, but similar across languages for a given conflict; that Middle Eastern conflicts receive more attention than African conflicts, even when controlling for the level of violence; and that for most languages and conflicts, following very few sources is enough to stay continuously informed. Finally, given the prominence of conflicts in the Middle East, we further analyze them in a detailed case study.

We present FAST (\url{http://fast.qcri.org/}), a platform for real-time traffic predictions in online news sources. FAST accurately forecasts the future number of page views of an article based on user traffic and social media engagement signals. To our knowledge, this is the first industrial scale, real-time system for predictive web analytics.

Public opinion is important for decision-making on numerous occasions for national growth in democratic countries like Bangladesh, the USA, and India. Sentiment analysis is a technique used to determine the polarity of opinions expressed in a text. The more complex stage of sentiment analysis is known as Aspect-Based Sentiment Analysis (ABSA), where it is possible to ascertain both the actual topics being discussed by the speakers as well as the polarity of each opinion. Nowadays, people leave comments on a variety of websites, including social networking sites, online news sources, and even YouTube video comment sections, on a wide range of topics. ABSA can play a significant role in utilizing these comments for a variety of objectives, including academic, commercial, and socioeconomic development. In English and many other popular European languages, there are many datasets for ABSA, but the Bengali language has very few of them. As a result, ABSA research on Bengali is relatively rare. In this paper, we present a Bengali dataset that has been manually annotated with five aspects and their corresponding sentiment. A baseline evaluation was also carried out using the Bidirectional Encoder Representations from Transformers (BERT) model, with 97% aspect detection accuracy and 77% sentiment classification accuracy. For aspect detection, the F1-score was 0.97 and for sentiment classification, it was 0.77.

The emergence of digital media has played a major role in changing how war is being told, understood and shared among people in the world today. This paper examines the way in which contemporary conflicts are mediated in digital platforms and the effect of such mediations on the use of language, mass communication, and the education system. In the age when the perception is created through social media and online news sources, as well as through algorithms and various other methods of content generation, the war stories no longer remain within the frames of conventional journalism. Rather, they are built up of fragmented, real-time, and frequently emotionally charged digital utterances. The paper methodologically uses a qualitative discourse analysis approach based on the critical discourse analysis (CDA) to analyze the chosen digital texts on the topic of war, such as news reports, posts in social media, and educational commentaries. The paper examines the framing of language, constructions of metaphors and ideological placement to influence the way the society comprehends conflicts. The results indicate that digital war stories are more likely to polarize the discourse, ideological binaries, and pedagogical interpretations in the academic environment. The paper finds that the digital war stories are essential to the formation of collective memory and the educational discourse, and also cause the concern of misinformation, narrative bias, and ethical presentation. It emphasizes the necessity of critical media literacy in schools to enable students to decode and critically use digital content about war.

In this article, we develop and test an economic theory of Supreme Court news. We hypothesize that information about the Third Branch is newsworthy when it has lower production costs and qualities attractive to the audiences and advertisers desired by news organizations. We examine Supreme Court news in elite newspapers, television news broadcasts, and online news sources during the October 2008 and 2010 terms. The results of our quantitative analyses indicate that all three types of news outlets are more likely to provide content about Supreme Court decisions with substantive importance but vary in their responses to costs and qualities appealing to the lay audience. We conclude by discussing the similarities and differences among news outlets with regard to their selection of Supreme Court information as news content.