News Posts Research Articles

Coverage of the research documents with top altmetric attention scores in online news

PurposeThis paper investigates the news coverage of research documents with the top Altmetric attention score (AAS) using a descriptive and explorative approach.Design/methodology/approachA dataset comprising 1,000 articles with high AAS was retrieved from Altmetric.com. The DOIs of these articles were then used to retrieve citation data from Dimensions. Data collection was finalized on February 11, 2024, and separate Excel files were merged using SPSS. The research included subject analysis and conducted correlation and Mann–Whitney U statistical tests.FindingsThe study reveals that the highest number of articles with high AAS were published in 2020. News post correlation with citation count is stronger than with AAS score. From the top AAS documents, titles of those with the highest news post counts include public concern keywords, and their subjects were primarily biomedical and clinical sciences. While no significant difference exists between multidisciplinary and specialty journal news posts, there is an increase in the variety of news sources in 2020 and 2021. Documents on biomedical and clinical sciences receive the most attention primarily from US-based news outlets, and many of the news outlets covering top AAS documents do not have specific subject orientations.Practical implicationsResearchers and journal editors should prioritize factors influencing research news coverage, including subjects, writing styles, and publication sources. Science writers should expand their coverage to include academic publications from subject areas beyond biomedical and clinical sciences. They can also consider diverse publication sources, such as preprints.Originality/valueThis study offers insights into the news coverage of top social-attention academic articles and is among the first to analyze the news coverage of academic publications.Peer reviewThe peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-12-2022-0666

Research on Fake News Detection Based on Dual Evidence Perception

With the development of online social networks, the prevalence of fake news on social media is increasing, leading to a heightened focus on detecting fake news. To combat this, researchers have developed various strategies, including text-based and fact-checking approaches. The text-based approach analyses linguistic features within fake news posts, while the fact-checking method verifies statements against an external knowledge base, disregarding the linguistic patterns between posts. Despite their differences, both approaches offer complementary benefits in the fight against fake news, highlighting the importance of integrating these methods. However, there has been relatively limited investigation into merging these two strategies into a unified framework. In this paper, we proposed the FakE News Detection Based on Dual Evidence Perception (DEP-FEND) method. This novel approach merges text-based and fact-checking techniques into a unified framework to enhance fake news detection. DEP-FEND consists of three sub-modules: evidence perception based on the historical news environment, evidence perception from an external knowledge base, and the fake news detector. Initially, two independent modules were developed to gather evidence from the historical news environment and an external knowledge base using the Gaussian kernel soft-counting method, generating two distinct evidence vectors. Subsequently, these two independent perception modules were combined using domain gates, and the posts, along with the combined evidence perception signals, were inputted into the fake news detector. The experimental results on the two Chinese datasets demonstrate that our proposed solution outperformed other fake news detection methods with an average accuracy improvement of 3.6% and a macro-f1 improvement of 2.1%.

Empowering social media users: nudge toward self-engaged verification for improved truth and sharing discernment

Abstract How can we empower social media users to better discern the veracity of news and share less false news? This survey experiment (N = 636) assessed the effectiveness of two interventions—signing a Pro-Truth Pledge and utilizing a Fact-Checking Guide. Results showed that utilizing the Fact-Checking Guide increased skepticism of news posts, likelihood to verify news posts, verification engagement, and reduced intention to share news without regard to news veracity. Before and after comparisons indicated that after verification engagement activities, truth and sharing discernment improved with higher factual accuracy ratings for true news, lower accuracy ratings for false news, and a greater likelihood to share true news compared to false news. Individual’s engagement in verification was identified as a crucial mechanism through which the Fact-Checking Guide intervention led to better truth and sharing discernment. The study could inform social media designs that promote a truthful news environment.

Social Media Metrics as Predictors of Publishers’ Website Traffic

The relationship between legacy media and social media has become a crucial topic in the discussions about new media. The debate intensified after Facebook announced a reduction in news posts in user timelines in 2018. In the era of the “Like economy”, social media holds significant economic value, prompting media outlets to adopt a “let’s try and see” approach to reach new audiences and increase their online advertising share. The present study, based on a large-scale survey of 50 publishers’ websites, Facebook pages, and Twitter accounts, deepens our understanding of the relationship between legacy and social media as indicators of audience feedback. Through the lens of network gatekeeping and reciprocal journalism theories, it contributes to the development of new evaluation tools that predict publishers’ website traffic based on social media metrics. Results show that Facebook and Twitter metrics can predict publishers’ website traffic indicators at a rate exceeding 60%. This study underscores the importance of social media metrics in evaluating media practices and the need to shift research toward specific indicators to understand the relationship between legacy and social media.

In Twitter we trust(ed): How perceptions of Twitter's helpfulness influence news post credibility perceptions and news engagement

This study examines the impacts of news veracity, source credibility, collective social endorsement, and perceptions of Twitter's utility on perceived credibility of and engagement with social media news posts. We conducted a 2 (Veracity: real vs. fake) × 2 (Source Credibility: credible vs. non-credible) x 2 (Collective Social Endorsement: high vs. low) online experiment (N = 271). In processing news posts, news veracity and source credibility influenced users' perceptions of post credibility, in that real news posts from credible sources were perceived as more credible than fake news posts from non-credible sources. Contrary to previous findings, social endorsement cues did not affect users' perceptions of post credibility or news engagement intent. More importantly, users' perceptions of Twitter's helpfulness in staying informed were associated with post credibility and intent to ‘like’ or share. Users with higher perceptions of Twitter's helpfulness were more likely to perceive posts as credible and more likely to engage with posts on social media. The results of this study have important implications for future research on news assessment and spread on social media.

Social corrections act as a double-edged sword by reducing the perceived accuracy of false and real news in the UK, Germany, and Italy

Corrective or refutational posts from ordinary users on social media have the potential to improve the online information ecosystem. While initial evidence of these social corrections is promising, a better understanding of the effects across different topics, formats, and audiences is needed. In three pre-registered experiments (N = 1944 UK, N = 2467 Italy, N = 2210 Germany) where respondents completed a social media post assessment task with false and true news posts on various topics (e.g., health, climate change, technology), we find that social corrections reduce perceived accuracy of and engagement with false news posts. We also find that social corrections that flag true news as false decrease perceived accuracy of and engagement with true news posts. We did not find evidence to support moderation of these effects by correction strength, anti-expert sentiments, cognitive reflection capacities, or susceptibility to social influence. While social corrections can be effective for false news, they may also undermine belief in true news.

Implementation of Artificial Intelligence Applications in Australian Healthcare Organisations: Environmental Scan Findings.

Artificial Intelligence (AI) has great potential to improve healthcare, but implementation into routine practice remains a challenge. This study scoped the extent to which AI and Natural Language Processing (NLP) is being implemented into routine practice in Australian healthcare organisations. An environmental scan of publicly available data was undertaken to identify AI applications. Publicly available data consisted of news posts from Australian public healthcare organisations and conference proceedings from key research organisations. Two researchers reviewed and analysed posts related to AI applications to create a list of potential implementation case studies. The final list of AI applications was reviewed by a governance committee in order to identify any missing applications. One application was identified by the governance committee and subsequently added. The environmental scan identified eighteen AI applications, of which eleven met all eligibility criteria. Only one application included NLP. Twelve applications were included when the application identified by the governance committee was added to the list. Implementation of AI applications is spread across four broad categories of use: 1) Decision Support, 2) Monitoring Treatment Effectiveness, 3) Personalised Care and 4) Risk Prediction.

Respons Fans Janiwota terhadap Berita Jhonny's Jimusho dalam Media Sosial Twitter

This research examines "Janiwota's Response to Johnny's Jimusho News on Twitter Social Media: Netnographic Study". The aim of this research is to find out the topics uploaded, as well as find out the responses and interactions given by fans to the news uploaded by the official Jhonnys & Associates Twitter account. The method used is a qualitative method with a netnographic approach. Data collection was carried out by accessing the discussion archives of the idol groups Travis Japan and Naniwa Danshi, which were then analyzed by categorizing the data obtained according to theory. The research results show that more news posts about Travis Japan were uploaded in March, and Naniwa Danshi was more uploaded in February. Uploads on various Twitter social media accounts have generated many different responses and reactions from fans from various countries.

Молодежь в пространстве социальных сетей Интернета: итоги социологического опроса

Many scientists in Russia pin their hopes on positive social transformations in the country on the youth, which is most often understood as the most active social group, including citizens from 18 to 35 years old. From a scien-tific point of view, the study of the position of representatives of this age category in the space of social net-works of the Internet is also of interest. The two most common stereotypes of using social networks of Internet were identified as: 1) continuous, constant usage of Internet networks; 2) regular news scrolling. The research confirmed the author’s assumption that in modern society, young people use online social networks to satisfy a wide range of needs: not only to communicate with friends, but also to obtain news and professional infor-mation. It was also found that representatives of the new generation in modern Russian society treat news posts, messages published in social network channels and blogs predominantly critically, double-checking the information received from the Internet or trusting only the channels and blogs chosen for themselves.

Making it Fit: How Science News Gets Remediated for Facebook and Instagram

ABSTRACT Today’s news users have a wide range of options when it comes to news consumption. Articles can be read on an app, via a news website, on social media or a printed newspaper. To provide these different platforms news media have become integrated newsrooms that shovel news as a standard practice. After posting a news article to the news website, the same news article can later appear on a social media platform like Facebook, Twitter, or Instagram. During the transportation from one platform to another the news inevitably undergoes changes. These remediations can be initiated by the platform: format-dependent constraints, e.g., the mandatory addition of a status message; based on preferences of the algorithm; or can be according to the personal preferences of the social media editor. Based on these insights we compiled a unique corpus of 109 news stories that map “a perfect chain” of science news in a one-to-one relationship between the news website, Facebook, and Instagram accounts of three traditional Flemish newspapers. We manually explored the circulation flow of these news posts and identified the corresponding types of news remediation. These results contribute theoretical and practical insights into the affordances, characteristics and constraints of social media news.

Exploring the Role of Emotions in Arabic Rumor Detection in Social Media

With the increasing reliance on social media as a primary source of news, the proliferation of rumors has become a pressing global concern that negatively impacts various domains, including politics, economics, and societal well-being. While significant efforts have been made to identify and debunk rumors in social media, progress in detecting and addressing such issues in the Arabic language has been limited compared to other languages, particularly English. This study introduces a context-aware approach to rumor detection in Arabic social media, leveraging recent advancements in Natural Language Processing (NLP). Our proposed method evaluates Arabic news posts by analyzing the emotions evoked by news content and recipients towards the news. Moreover, this research explores the impact of incorporating user and content features into emotion-based rumor detection models. To facilitate this investigation, we present a novel Arabic rumor dataset, comprising both news posts and associated comments, which represents a first-of-its-kind resource in the Arabic language. The findings from this study offer promising insights into the role of emotions in rumor detection and may serve as a catalyst for further research in this area, ultimately contributing to improved detection and the mitigation of misinformation in the digital landscape.

Facts are hard to come by: discerning and sharing factual information on social media

Abstract How credulous are we when engaging information on social media? Addressing this question, this article aims to understand how individuals’ epistemic vigilance, a set of cognitive mechanisms that comprise our system of precaution in social interactions, may operate and fall short. Reporting findings from two survey experiments (Study 1, N = 413; Study 2, N = 392), we show that participants tended to be skeptical toward social media news, were reasonably successful in identifying true news, and reported a tendency to share true rather than false news. In one study, social endorsement enticed a higher accuracy rating of news posts. In both studies, people judged attitudinally congruent news posts as being more accurate and reported a higher likelihood to share them. Individuals’ propensity to reflective thinking measured by cognitive reflection test potentially operated as a restraint on sharing inaccurate information and bolstered veracity anchoring in their information engagement.

Displaying News Source Trustworthiness Ratings Reduces Sharing Intentions for False News Posts

Professional fact-checking of individual news headlines is an effective way to fight misinformation, but it is not easily scalable, because it cannot keep pace with the massive speed at which news content gets posted on social media. Here we provide evidence for the effectiveness of ratings of news sources, instead of individual news articles. In a large pre-registered experiment with quota-sampled Americans, we find that participants are less likely to share false headlines (and more discerning of true versus false headlines) when 1-to-5 star trustworthiness ratings were applied to news headlines. This is true both when the ratings are generated by fact-checkers and by laypeople (although the effect is stronger using fact-checker ratings). We also observe a positive spillover effect: sharing discernment also increases for headlines whose source was not rated, likely because the presence of ratings on some headlines prompts users to reflect on source quality more generally. This study suggests that displaying information regarding the trustworthiness of news sources provides a scalable approach for reducing the spread of low-quality information.

The effects of message attributes and source characteristics of news posts on audience engagement on social media

ABSTRACT Informed by the theoretical framework of media effects and resonance theory, this study investigates how issue obtrusiveness and information richness as message attributes, and media hierarchy and orientation as source characteristics influence audience engagement with news posts on social media. The data of news posts (N = 943,793) from the top 99 Sina Weibo accounts of Chinese media with likes, comments, and reposts as indicators of audience engagement were retrieved. Through multilevel modeling, the study finds that source characteristics exert stronger effects on audience engagement than message attributes, and the effects on comments differ from those on likes and reposts. The association between issue obtrusiveness and comments is stronger than that between obtrusiveness and likes/reposts. Posts of high information richness draw more audience engagement than posts of low information richness. Through their news posts, central-level media attract more engagement than local media. The implications of the findings are discussed.

Collecting and analyzing news from newspaper posts in facebook using machine learning

Many people use social networks to spend their free time. News, especially at the time of great world changes, began to gain considerable popularity. Washington Post, New York Times, Time, Reuters, Forbes are among the most famous global newspaper publications. An average analyst can spend up to 40 hours a week collecting information about competitors and researching the most popular posts. According to the conducted research, an average of 40 new posts with news per day. The data processing process can be automated using modern information tools to facilitate the routine work of analysts. To analyze the target audience and reach, it is worth considering the text of the message, the number of likes, comments and links. This information was obtained using the Selenium automated web page testing tool using the Java programming language. The time spent on collecting data in the described way from four newspaper editions amounts to approximately 12 hours. The Tensorflow library using the JavaScript programming language is applied to the collected information. Based on information about the number of shares, comments, likes, frequency of news posts, an analysis was carried out using machine learning algorithms. Based on the clustering data, we can observe such a tendency that posts with a large number of likes receive a large number of comments and vice versa. An analysis of the most active hours of users in the network based on news posts is performed. As a result, the highest activity is observed at least three times a day, namely: in the morning hours from 9:00 to 11:00, in the lunch time of the day from 12:00 to 15:00 and in the evening time period from 18:00 to 20:00. This trend is due to the work schedule of most employees during the working week. The resulting statistical information in the work can be used for other content or user behavior in social networks.

Attitudes to fake news verification: Youth orientations to ‘right click’ authenticate

This article examines the phenomenon of fake news through a survey of university students in the United Kingdom. The survey, composed through a selection of factual and non-factual content/news and complemented through a validation tool, sought to assess the attitudes of these respondents to items of factual misinformation before and after these were verified with the tool. The findings from the survey present online misinformation as a very complex and unfolding phenomenon in terms of user behaviour, particularly when presented with an authentication tool. The majority of respondents failed in identifying factual from fake news posts. While respondents indicated mistrust in using third-party validation tools, the majority indicated a critical need for a verification tool that would support their quest and increase their trust in what they read online.

Examining Exposure to Messaging, Content, and Hate Speech from Partisan News Social Media Posts on Racial and Ethnic Health Disparities.

We investigated the content of liberal and conservative news media Facebook posts on race and ethnic health disparities. A total of 3,327,360 liberal and conservative news Facebook posts from the United States (US) from January 2015 to May 2022 were collected from the Crowd Tangle platform and filtered for race and health-related keywords. Qualitative content analysis was conducted on a random sample of 1750 liberal and 1750 conservative posts. Posts were analyzed for a continuum of hate speech using a newly developed method combining faceted Rasch item response theory with deep learning. Across posts referencing Asian, Black, Latinx, Middle Eastern, and immigrants/refugees, liberal news posts had lower hate scores compared to conservative posts. Liberal news posts were more likely to acknowledge and detail the existence of racial/ethnic health disparities, while conservative news posts were more likely to highlight the negative consequences of protests, immigration, and the disenfranchisement of Whites. Facebook posts from liberal and conservative news focus on different themes with fewer discussions of racial inequities in conservative news. Investigating the discourse on race and health in social media news posts may inform our understanding of the public's exposure to and knowledge of racial health disparities, and policy-level support for ameliorating these disparities.

"Attention! It’s the News.” Cognitive Load and News Posts on Facebook: An Eye-tracking Study

Individuals get much of their news and information through social media. Thus, it has become crucial to evaluate how they are paying attention to and processing news content in this saturated space. The present study investigates users’ visual engagement with news posts on Facebook using cognitive load theory of attention. An eye-tracking device was used to track visual attention of N = 152 participants. Following a 3 x 2 between-subjects factorial design, we manipulated intrinsic cognitive load by varying the story topic (either health or science) and the extraneous load by varying the presentation type (text only, video only, or video with text). Cognitive load does impact attention, such that those who viewed the video with text story (high cognitive load) exhibited higher visual attention to the content. We conclude that content higher in complexity elicits more attention compared to less complex content on Facebook.

ECS News - Best Posts of 2022

Highlights from 2022 ECS News posts

Posts Supporting Anti-Environmental Policy in Brazil are Shared More on Social Media.

Weakening environmental laws supported by disinformation are currently of concern in Brazil. An example of disinformation is the case of the "firefighter cattle". Supporters of this idea believe that by consuming organic mass, cattle decrease the risk of fire in natural ecosystems. This statement was cited by a member of the Bolsonaro government in response to the unprecedented 2020 fires in the Pantanal, as well as in support of a new law that enables extensive livestock in protected areas of this biome. By suggesting that grazing benefits the ecosystem, the "firefighter cattle" argument supports the interests of agribusiness. However, it ignores the real costs of livestock production on biodiversity. We analysed the social repercussion of the "firefighter cattle" by analysing public reactions to YouTube, Facebook, and Google News posts. These videos and articles and the responses to them either agreed or disagreed with the "firefighter cattle". Supportive posts were shared more on social media and triggered more interactions than critical posts. Even though many netizens disagreed with the idea of "firefighter cattle", it has gone viral, and was used as a tool to strengthen anti-environmental policies. We advocate that government institutions should use resources and guidelines provided by the scientific community to raise awareness. These materials include international reports produced by the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES) and the Intergovernmental Panel on Climate Change (IPCC). We need to curb pseudoscience and misinformation in political discourse, avoiding misconceptions that threaten natural resources and confuse global society.