News Stories Research Articles

Who Wrote It? News Readers’ Sensemaking of AI/Human Bylines

This exploratory mixed methods study analyzes readers’ sensemaking of artificial intelligence’s role in journalistic writing. In this study, participants were randomly assigned to a byline condition that varied the stated role of AI in a news story. Regardless of condition, participants detailed various explanatory stories—or “folk theories”—to explain AI’s involvement in the writing process, ranging from AI primarily conducting the writing to AI assisting in specific tasks to optimize the writing. Overall, most participants did not view AI as an independent actor. The findings suggest newsrooms should be transparent about their AI use, given inconsistencies in readers’ interpretation.

The Role of Artificial Intelligence in Shaping News Narratives: A Review of Global Case Studies

The use of artificial intelligence (AI) in journalism is changing how news is created, disseminated, and consumed in the quickly changing media landscape. Reviewing international case studies, this study examines how artificial intelligence (AI) influences news narratives. The study looks at how AI-powered algorithms affect the way news stories are framed, chosen, and distributed. Through examining case studies from various regions such as Europe, Asia, Africa, and North America, the research delves into how AI impacts journalism practices and the associated ethical considerations. Key findings show that although AI improves personalization and efficiency, it also raises questions about audience perceptions, media diversity, and bias. The results of this study also emphasize the necessity of ethical concerns and legal frameworks by highlighting the opportunities and difficulties that AI presents to the media landscape. The study ends with suggestions for additional research and possible approaches to reducing the ethical difficulties.

Misattribution stigma and contagion: How did the art auction market react to Australian “Black art scandals”?

AbstractIn 1999, two major stories hit the media concerning the authenticity of artworks by two of Australia’s leading Indigenous artists. The incidents were quickly tagged as ‘Black art scandals’, with the artists disavowing production of the artworks in question. We theorize the market impacts using the lens of stigma, given that, in the art market, uncovering fakes and forgeries may create a panic among buyers of an artist. Our proposed mechanism is that negative media attention about works misattributed to an artist stigmatizes the artist’s name and their artworks. We also note the potential stigma transfer to other artists in a style or genre (category-level contagion). Our results show a decline in the clearance rate and some weak evidence of a fall in the value of sales for auctions occurring after the news stories broke. We suggest stigma as a lens for future work in cultural economics.

What Comes After the Algorithm? An Investigation of Journalists’ Post-editing of Automated News Text

ABSTRACT Journalists often perceive that automated journalism produces news texts that lack narrative and editorial quality. Therefore, they sometimes manually edit automated output before publication, creating so-called “post-edited” variants. Using an exploratory sequential mixed-methods design, this study aims to advance research on automated journalism by investigating the steps journalists say they take when post-editing and testing whether—and if so, how—these steps are reflected systematically on a larger scale in post-edited news texts. First, journalists’ statements about the post-editing process were gathered in semi-structured interviews. Then, qualitative content analysis of automated news stories and their post-edited offspring examined whether post-edited stories contain evidence of forms of editing not mentioned by the journalists. Second, the qualitative findings were investigated quantitatively using comparative content analysis (N = 282). The qualitative findings suggest that a range of editorial steps may be taken during post-editing. The quantitative analysis shows that some of these steps are reflected systematically in post-editing, resulting in significant differences between automated news stories and their post-edited offspring. However, findings also show differences between journalists’ reports and post-editing practice on a larger scale, including evidence that forms of editing were taking place that were not mentioned in the interviews.

The politicization of education policies: The case of Ghana’s Free Senior High School Policy

While many scholars recognize and criticize the politicization of education policies, scholarly attention to the strategies politicians utilize to politicize educational policy discourses remains limited. Focusing on the officials of Ghana’s two major political parties, the New Patriotic Party (NPP) and the National Democratic Congress (NDC), as policy actors, this paper investigates how officials of these two parties operationalized discussions regarding Ghana’s Free Senior High School Policy (FSHSP) to garner support for their parties while stoking resentment for their political opponents. The study employs a critical discourse analysis (CDA) framework and a dataset of 175 documents, including news stories, press releases, party manifestos, and government publications. The analysis reveals that both parties resorted to strategies of positive self-presentation and negative other presentation. The NPP contrasted its regime with the NDC’s tenure and framed the NDC as a threat to FSHSP and the education of the poor and vulnerable. The NDC problematized and highlighted implementation bottlenecks while framing the NPP and FSHSP as threats to quality education. I argue that through these strategies, NPP officials aimed to maintain incumbency while NDC officials advocated a regime change. The paper concludes by emphasizing the potential risks associated with politicizing education policies.

Media frames, AI romantic relationships, and the perspectives of people in relationships; mapping and comparing news media themes with user perspectives

ABSTRACT Human romantic relationships with artificially intelligent (AI) conversational agents and anthropomorphic computers have long been the realm of science fiction. As companies like Luka, Inc. are developing embodied conversational agents called Replika, people’s ability to engage in romantic relationships has garnered news coverage around the phenomenon. Because news is often a place where emerging technologies are first introduced, it is essential to understand the choice of topics and themes these stories focus on (e.g., the ethics of technological development, human desires and vulnerabilities, and what constitutes meaningful and authentic connection). Similarly, the extent to which these articles include user perspectives and how users respond to these frames is also less well understood. This article reports on a mixed-method study that combines content analysis of news articles in 2023 about Replika (1008 search results, n = 104 unique articles) and qualitative semi-structured interviews with users in romantic relationships with their Replika (n = 16), recruited from the Reddit forum ‘ILoveMyReplika.’ Through a frame analysis of these news stories and coding for the types of futures they present, this study first examines some of the key events, themes, and hopes/fears reported in mass media. A thematic analysis of the interview data offers a comparative study to see where there is some overlap between these hopes/fears and user experiences and places where user reports diverge, downplay, and dispute the media characterizations. These studies help us better understand the interrelationship between media narratives, technological frames, and moral panics around a unique subset of and relationship with AI technologies.

THE JOURNALISTIC APPROACH IN THE CASES OF DANIEL ALVES AND SANDRA MARA FERNANDES: AN ANALYSIS OF GENERATIONAL AND GENDER ISSUES

This study aims to denounce typical elements of structural machismo in Brazilian society, as evidenced by the media coverage of the case of Daniel Alves, a soccer player accused of raping a young woman at the Sutton nightclub in Barcelona in December 2022. It also addresses the case of Sandra Mara Fernandes, a businesswoman who, during a psychotic episode, had sexual relations in her car with homeless man Givaldo Alves in Planaltina (DF) in March 2022. The qualitative methodology is based on theoretical categories capable of explaining the relationships between the media, hegemony, and society. The aim is to highlight that even in news stories where the main subject is a man's mistake, machista culture exalts the male figure while distorting the image of the women involved.

Framing feminist protest: a content analysis of the glitter revolution

ABSTRACT Feminist groups in Mexico City started a mobilization in August 2019 that would later be called the Glitter Revolution. The protest was a reaction to the alleged rape of a 17-year-old girl by four policemen. The protest earned the name after one of the protestors showered Mexico City’s chief of police with pink glitter while he was trying to address activists and reporters. A second protest turned violent, and one of Mexico’s best-known landmarks was defaced by protesters who covered it in graffiti with signs reading “Mexico feminicide,” and “rapists.” This paper engages with literature on the protest paradigm and feminist theories to conduct a qualitative content analysis of news coverage of the two protests. This research shows how: 1) news stories by mainstream media adhere to the protest paradigm deploying a marginalizing framing device to cover the feminist protests; 2) heavy reliance on official sources and information is still prevalent in mainstream news, reinforcing official narratives and discourses; 3) the emergence of new frames that assign more value to historical monuments than to women’s access to justice; 4) and alternative news provided different frames such as feminist movement/activism and gender violence to contextualize the protests.

The influence of spider news on online information-seeking.

Fear of spiders is a widespread condition often disproportionate to the actual danger spiders pose to humans. Likely rooted in evolutionary history, fear of spiders might also have a cultural component. Recent studies have shown that a significant fraction of spider-related media reports are misleading and sensationalistic. Information-seeking behaviours serve as common coping mechanisms for our fears and anxieties, yet the link between spider-related news stories and such behaviors remains unexplored.We hypothesize that media reports foster concern about spiders, resulting in an increased awareness of spiders and health issues associated with them. We extracted 1486 reports in English from a public database providing a content-analysis of spider-related online traditional media reports published between 2010-2020. We examined whether the volume of spider-related queries in Google Trends, Wikipedia, and iNaturalist increased in the week following the publication of each news story.Sensationalistic news stories were associated with a small, significant increase in search volumes, compared to non-sensationalistic ones. The search volume for "brown recluse" (Loxosceles reclusa), which are potentially dangerous spiders, was higher after the publication date of news related to human-spider encounters. There was a significant positive relationship between the number of spider-related news stories published in a given month and the traffic on target spider-related Wikipedia pages, especially so for the page on brown recluse spiders.Our results suggest that traditional media have a detectable impact on the behaviour of the general public towards spiders, supporting the hypothesis that the fear of spiders is perpetuated by culture. Additionally, our findings indicate that information-seeking behaviour is a common response to learn about spiders and potentially fact-check spurious claims found in sensationalised news. By recognizing the role of media in shaping attitudes towards spiders and acknowledging the benefits of accurate representation, we can lay the foundation for a more informed and harmonious relationship between humans and spiders.

Modality deep-learning frameworks for fake news detection on social networks: a systematic literature review

Fake news on social networks is a challenging problem due to the rapid dissemination and volume of information, as well as the ease of creating and sharing content anonymously. Fake news stories are problematic not only for the credibility of online journalism, but also due to their detrimental real-world consequences. The primary research objective of this study is: What are the recent state-of-the-art modalities based on deep learning to detect fake news in social networks. This paper presents a systematic literature review of deep learning-based fake news detection models in social networks. The methodology followed a rigorous approach, including predefined criteria for study selection of deep learning modalities. This study focuses on the types of deep learning modalities; unimodal (refers to the use of a single model for analysis or modeling purposes) and multimodal models (refers to the integration of multiple models). The results of this review reveal the strengths and weaknesses of modalities approaches, as well as the limitations of low-resource languages datasets. Furthermore, it provides insights into future directions for deep learning models and different fact checking techniques. At the end of this study, we discuss the problem of fake news detection in the era of large language models in terms of advantages, drawbacks, and challenges.

Too many numbers and worse word choice: Why readers find data-driven news articles produced with automation harder to understand

The production of data-driven journalism is becoming increasingly automated, impacting its composition and comprehensibility. Given the importance of data-driven reporting for democratic participation, this study investigates, firstly, how readers evaluate the composition of data-driven articles produced with and without the help of automation and, secondly, how these evaluations affect readers’ perceptions of the articles’ comprehensibility. In an online survey experiment, 3135 online news consumers evaluated 24 articles produced with or without automation using criteria developed in prior research. Our factor analysis reduced those criteria to five categories that matter in readers’ evaluations of the articles’ composition: numeric features, writing style, sentence and paragraph length, descriptive language, and word choice. Our results show, firstly, that although the perception of news stories produced with automation did not differ significantly from that of news stories produced without automation regarding sentence and paragraph length and writing style, the stories produced with automation were evaluated as significantly less comprehensible; and, secondly, that this can be explained partly by readers’ perceptions of some of the other article composition categories, which were rated significantly worse in automated articles. Our findings suggest that using automation to produce data-driven news articles changes their perceived composition in ways that negatively impact comprehensibility. However, this study also suggests how such articles could be made more comprehensible. Specifically, when ‘post-editing’ automated articles, journalists should aim to further reduce the quantity of numbers, better explain words that readers are unlikely to understand and change inappropriate wording.

Seeing Beyond Crisis: Analyzing Photographs and Photographer Bylines in Solutions-Oriented Environmental Journalism Stories

ABSTRACT Environmental journalism helps readers better understand the relationship between humanity and humanity’s home, but visual aspects of this type of reporting often focus more on conflict and distant human suffering. Though this is somewhat changing in print media, there remains a lack of focus on the action-forward type of reporting that might mobilize and educate readers toward what can be done about environmental problems. This study looks at environmental journalism visuals through a solutions journalism perspective, which compels journalists to add to their coverage of problems by asking and showing what people are doing about it. Through a content analysis, we explore how the environment is visually represented in solutions-oriented news stories. We found key differences based on story topic, as well as how people were included and depicted within the images. We also discuss the significant reliance on outside sources for images used in these stories.

Internet Search Activity for Intentional Self-Harm Forums After a High-Profile News Publication: Interrupted Time Series Analysis.

Searches for "pro-suicide" websites in the United States peaked during the week a high-profile news story was published and remained elevated for 6 months afterward, highlighting the need to avoid mentioning specific sources of explicit suicide instructions in media publications.

The Making of Good News: Discursive Construction of Good News Through News Values

ABSTRACT Good news aims to redress the balance from negative bias and contributes to a more comprehensive form of journalism. This article explores the largely ignored discursive construction of good news through news values. It first investigates the features of good news stories as against top news stories in the mainstream media, and then identifies differences in the discursive practices by the mainstream media and the good news outlets in covering the same events. The findings show that in the mainstream media, top news stories tend to highlight Eliteness and Negativity, whereas good news stories give emphasis to Personalization and Positivity. When covering the same events, reports by the good news outlets show an overemphasis on Positivity, and those by the mainstream media demonstrate a more balanced distribution of the major news values. It is proposed that good news can serve as a valuable complement to top news in the mainstream media. Reports in the mainstream media tend to be more comprehensive and informative, and the relatively shorter reports in the good news outlets pay more attention to the positive aspects of the event that are effective in uplifting the readers’ mood.

“We don’t believe in [the] Coronavirus; we believe in Allah. Whatever happens, it happens from Allah”: Pakistani media’s response to countering conspiracy theories during the COVID-19 pandemic

This study examines the coverage of the COVID-19 pandemic by the four leading newspapers of Pakistan—Dawn, The News, Daily Times and The Nation—when they were responsible for informing and educating the public during a health crisis hit by conspiracy theories. The researchers utilized content analysis to analyze 1,124 news stories. The findings reveal less emphasis was placed on scientifically investigating the causes, precautions and care of coronavirus and dispelling public misconceptions.

A Proposed Ensemble Approach for Searching Hacking News Semantically

Efficient search of hacking information has been a topic of great discussion in recent years. Many challenges are encountered when searching for this information. In particular, researchers may encounter unfamiliar and potentially challenging terms, ideas, tools, and other items that are unique to hacking. Effective comprehension of synonyms and polysemy is necessary. These reasons serve as the driving force behind our efforts to develop a productive method for semantic hacking information searches. Semantic search, using advanced NLP techniques, has transformed information retrieval by improving search result accuracy and relevance. Unlike traditional lexical methods, neural models like sentence-transformers handle synonyms and polysemy efficiently. However, processing time increases with model size. This paper proposes a novel ensemble semantic search (NESS) approach that aggregates mini or small neural embedding models, leveraging their distinct advantages. Evaluated on a dataset with over 300,000 Hacker News stories, our proposed method significantly enhances ranking quality and retrieval accuracy compared to existing techniques, while requiring half the processing time of the best-performing large model. The findings underscore the trade-offs between model complexity, retrieval accuracy, and processing efficiency, offering insights for optimizing semantic search systems.

When politics trumps truth: Political concordance versus veracity as a determinant of believing, sharing, and recalling the news.

Resistance to truth and susceptibility to falsehood threaten democracies around the globe. The present research assesses the magnitude, manifestations, and predictors of these phenomena, while addressing methodological concerns in past research. We conducted a preregistered study with a split-sample design (discovery sample N = 630, validation sample N = 1,100) of U.S. Census-matched online adults. Proponents and opponents of 2020 U.S. presidential candidate Donald Trump were presented with fake and real political headlines ahead of the election. The political concordance of the headlines determined participants' belief in and intention to share news more than the truth of the headlines. This "concordance-over-truth" bias persisted across education levels, analytic reasoning ability, and partisan groups, with some evidence of a stronger effect among Trump supporters. Resistance to true news was stronger than susceptibility to fake news. The most robust predictors of the bias were participants' belief in the relative objectivity of their political side, extreme views about Trump, and the extent of their one-sided media consumption. Interestingly, participants stronger in analytic reasoning, measured with the Cognitive Reflection Task, were more accurate in discerning real from fake headlines when accurate conclusions aligned with their ideology. Finally, participants remembered fake headlines more than real ones regardless of the political concordance of the news story. Discussion explores why the concordance-over-truth bias observed in our study is more pronounced than previous research suggests, and examines its causes, consequences, and potential remedies. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Reported speech and gender in the news: Who is quoted, how are they quoted, and why it matters

News stories have a well-defined generic structure, consisting of components such as headline, lede, and body, with reported speech a prominent feature, especially in hard news stories. Reported speech serves multiple purposes, from providing evidentiality and intertextuality to contributing to the construction of newsworthiness and to the context creation of news. It is also a site of potential bias in who is cited and how, including with respect to the gender of sources. Using a large corpus of English-language news stories for all of 2023 from the main five mainstream news outlets in Canada (over 370,000 articles from news websites), I examine the gender distribution of those quoted, the syntactic variation in the structure of quotes, and the types of reporting verbs. The study provides a comprehensive overview of the extend of gender bias in contemporary Canadian news, at the same time offering insights into the nature of reported speech in modern news and how it endures and evolves, including in news meant for digital-only publication.

Exemplification and stigmatization: How news stories affect stigma-related attitudes, emotional reactions, and behavioral intentions towards students with a disability

Exemplification and stigmatization: How news stories affect stigma-related attitudes, emotional reactions, and behavioral intentions towards students with a disability

Inflation of crisis coverage? Tracking and explaining the changes in crisis labeling and crisis news wave salience 1785–2020

Abstract Has there been an inflation in crisis coverage in newspapers over the last centuries, and if so, what structural factors drive this change? We utilize computational text analyses along with our own signal detection algorithm to measure the presence of crisis keywords and the emergence of crisis news waves. An analysis of crisis coverage in The Times (U.K., 1785–2020, 183,239 news stories) shows that the share of coverage that uses crisis keywords has increased, though not steadily. The number and salience of crisis news waves tied to discernible events has increased at a slower pace. The hypothesized driving forces—government expansion, mediatization of politics, and the activity of crisis frame sponsors—explain the development well and allow accurate predictions even when ignoring time in the forecasting model. Crisis coverage seems to reflect not so much the problems society faces, but society’s identity, priorities, and outlook on the world.