Mainstream Online Research Articles

Who’s fuelling Twitter disinformation on the COVID-19 vaccination campaign? Evidence from a computational analysis of the green pass debate

ABSTRACT The COVID-19 health emergency increased disinformation’s role and fostered a growing fragmentation between conflicting opinions on COVID-19 causes, vaccination policies, and government measures to deal with the pandemic. Studies have found that disinformation sources included private citizens, independent organizations, mainstream online newspapers and even public figures such as politicians, commentators, bloggers etc. In Italy, the Twitter debate ignited a conflict between mainstream positions in favour of restrictions, and more libertarian opinions extremely critical of government measures. Our research investigates, through a computational approach based on digital methods and social network analysis (SNA), opinion leaders’ roles in the Italian green pass debate on Twitter that surfaced in the second half of 2021. Drawing on the classic two-step model of communication, our essay identifies the Italian opinion leaders on Twitter and their content dissemination strategies. Our analysis reveals a limited number of dominant voices interacting in segregated networks of users. These networks can be considered echo chambers given the verbose and self-referential tweeting activity of their opinion leaders. Moreover, such activity involves spreading disinformation and conspiracy theories through a dissemination strategy aimed at diverting the audience from Twitter, towards ‘below-the-radar’ environments (e.g. Rumble), where political views are more radical.

Journal of Scientific Temper

Background: Climate change has emerged as the most pressing concern for humankind, given the rapidly depleting natural resources and burgeoning issues of pollution and waste posing grave threat to the planet’s survival. News media bear the onus of not only reporting on climate change to spread awareness, but they’re in fact educators for a large part of the population. Photographs embedded in news stories have a profound effect on the audiences in that they immediately reduce the distance between the readers and ground zero.Objective: The present study has been designed to conduct a visual content analysis of photographs incorporated in climate change news stories in mainstream online newspapers in India. Online newspapers have been incorporated owing to the growing propensity of a large populace to receive their news through digital devices like smartphones, laptops and tablets. SIt would help in examining the climate change imagery being employed by the online newspapers, which goes a long way in shaping readers’ perceptions about the burning issue.Methodology: The newspapers chosen are the online editions of The Hindu, The Hindustan Times, and The Times of India, keeping in view their wide reach and popularity. The newspapers are scanned for climate change stories over a period of two months (December 2020 and January 2021) with the accompanying photograph each the news story as the unit of analysis. The stories with key words ‘climate’, ‘climate change’ or ‘global warming’ in the headline or lead paragraph were selected for data collection.The corpus of images thus obtained is quantified and analyzed to ascertain the depiction of gender in terms of activity. Further, the data is scrutinized from several perspectives, ranging from analysing gender representation in the stories from various sources (internal staff, or national/international news agencies) to the spectrum of topics covered.Implications: The study follows a unique approach as it inspects gender portrayal along several dimensions not undertaken hitherto by any other study. It would be pertinent to understand the visual representation of gender in people’s daily dose of online news, which has strong agenda-setting influences on the society.

The hidden depths of suicidal discourse: Network analysis and natural language processing unmask uncensored expression.

The socially unattractive and stigmatizing nature of suicidal thought and behavior (STB) makes it especially susceptible to censorship across most modern digital communication platforms. The ubiquitous integration of technology with day-to-day life has presented an invaluable opportunity to leverage unprecedented amounts of data to study STB, yet the complex etiologies and consequences of censorship for research within mainstream online communities render an incomplete picture of STB manifestation. Analyses targeting online written content of suicidal users in environments where fear of reproach is mitigated may provide novel insight into modern trends and signals of STB expression. Complete written content of N = 192 users, including n = 48 identified as potential suicide completers/highest-risk users (HRUs), on the pro-choice suicide forum, Sanctioned Suicide, was modeled using a combination of lexicon-based topic modeling (EMPATH) and exploratory network analysis techniques to characterize and highlight prominent aspects of censorship-free suicidal discourse. Modeling of over 2 million tokens across 37,136 forum posts found higher frequency of positive emotion and optimism among HRUs, emphasis on methods seeking and sharing behaviors, prominence of previously undocumented jargon, and semantics related to loneliness and life adversity. This natural language processing (NLP)- and network-driven exposé of online STB subculture uncovered trends that deserve further attention within suicidology as they may be able to bolster detection, intervention, and prevention of suicidal outcomes and exposures.

A Framing Analysis of Online Mainstream News Coverage on Cancer Before the Novel Coronavirus 2019 (COVID-19)

A Framing Analysis of Online Mainstream News Coverage on Cancer Before the Novel Coronavirus 2019 (COVID-19)

A semantic similarity analysis of multiple English translations of The Analects: Based on a natural language processing algorithm.

Working from the readers' perspective, this study first investigates the online acceptance of the complete English translations of The Analects by investigating the number of online comments, downloads, academic citations, and other factors, and it ranks the different English versions according to how well they are received. The complete English translations of The Analects by D. C. Lau, James Legge, and 15 other translators are found to be well received by readers on mainstream online platforms. Then, based on five natural language processing (NLP) algorithms (TF-IDF, Word2Vec, GloVe, BERT, and SimHash), the 15 well-received English versions of The Analects are taken as samples to calculate semantic similarity. By comparing the semantic differences among the texts, this study analyzes the factors that affect the diversification of translated texts. (1) The influence of Chinese annotation on the translation semantics is great, even the greatest among many influential factors; and (2) different translators' identities, the translation era, the translation purpose, and the translation background do not significantly affect the semantic influence of the translation. On the one hand, the readers can understand the differences between the different translations and choose an appropriate translation for their reading and learning more effectively. On the other hand, using the algorithms of NLP, we focus on the semantic similarity of different English translations of The Analects and analyze them to show the semantic differences quantitatively, which makes the comparison more intuitive and efficiently. Such a quantitative presentation of the results draws scholars' attention to the differences in the translations.

Open Archive of Hate: Terrorism and Violent Extremism on Internet Archive

The recognized correlation between the growing spread of violent extremist and terrorist content on the Internet and online platforms and the significant increase of attacks inspired by these postings caused governments and security agencies to launch various countermeasures. These measures included removal of terrorist and violent extremist online content (or “deplatforming”), suspension of their social media accounts, hacking websites and pressuring social media companies to remove terrorist propaganda. Struggling to adjust to these countermeasures and sustain their online presence, terrorist and violent extremists moved from mainstream online platforms to alternative online channels including anonymous sharing portals and cloud services. The most popular archiving service used by various extremist and terrorist group is the Internet Archive. While the few studies on hate speech and violent contents on Internet Archive focused only on a certain type of content or a specific group, this study attempts to reveal a wider range of contents, groups and organizations who use this archive for sharing and promoting such contents. To examine the use of Internet Archive by various groups and organizations involved in violence, terrorism, hate speech, racism and neo-Nazism we applied several stages of data collection and analysis. The findings highlight an alarming volume of terrorist, extremist, and racist material on the Internet Archive. These findings are discussed in terms of ethical and practical implications.

The public information needs of COVID-19 vaccine: A study based on online Q&A communities and portals in China.

PurposeThis study analyzes the topic and distribution features of public information needs for the COVID-19 vaccine from Chinese online Q&A communities and portals. It aims to identify the features and differences in public COVID-19 vaccine information needs at different periods.Design/MethodologyA total of 14,296 questions about the COVID-19 vaccine from four Chinese mainstream online communities and portals were studied following five procedures: data collection, data processing, K-means clustering, LDA topic model analysis, and needs identification.FindingsThe study identified the topical features of public information needs for the COVID-19 vaccine during the first pandemic outbreak, pre-listing period, and post-listing period. It constructed a framework of public vaccine information needs. The information needs can be classified into 8 main categories and 16 subcategories. The eight main categories are vaccination (53.72%), evaluation and impact of other social events (17.90%), vaccine R&D and listing (9.49%), vaccine side effects and countermeasures (5.63%), vaccination necessity (4.98%), vaccine patent exemption (3.26%), vaccination effectiveness (2.94%), and essential knowledge of vaccine (2.08%), where percentage refers to the distribution of information needs data under various categories.ImplicationsOnline communities and portals should provide dynamic and tailored information services according to changing public vaccine information needs. The public information needs regarding vaccination is prominent and should be addressed first. In the follow-up booster vaccination efforts, government health departments should prioritize susceptible groups, such as overseas students, airport workers, and healthcare workers.Originality/ValueWe built a conceptual framework using data mining techniques and analyzed the COVID-19 vaccine information needs distribution at different time points and among different social groups, focusing on the theme of public information needs for the COVID-19 vaccine. It makes recommendations for government health departments and online platforms to improve the quality of COVID-19 vaccine information services for the public and provide a reference for the vaccination of COVID-19 booster shots.

Quantifying Changes in Vaccine Coverage in Mainstream Media as a Result of the COVID-19 Outbreak: Text Mining Study.

Achieving herd immunity through vaccination depends upon the public's acceptance, which in turn relies on their understanding of its risks and benefits. The fundamental objective of public health messaging on vaccines is therefore the clear communication of often complex information and, increasingly, the countering of misinformation. The primary outlet shaping public understanding is mainstream online news media, where coverage of COVID-19 vaccines was widespread. We used text-mining analysis on the front pages of mainstream online news to quantify the volume and sentiment polarization of vaccine coverage. We analyzed 28 million articles from 172 major news sources across 11 countries between July 2015 and April 2021. We employed keyword-based frequency analysis to estimate the proportion of overall articles devoted to vaccines. We performed topic detection using BERTopic and named entity recognition to identify the leading subjects and actors mentioned in the context of vaccines. We used the Vader Python module to perform sentiment polarization quantification of all collated English-language articles. The proportion of front-page articles mentioning vaccines increased from 0.1% to 4% with the outbreak of COVID-19. The number of negatively polarized articles increased from 6698 in 2015-2019 to 28,552 in 2020-2021. However, overall vaccine coverage before the COVID-19 pandemic was slightly negatively polarized (57% negative), whereas coverage during the pandemic was positively polarized (38% negative). Throughout the pandemic, vaccines have risen from a marginal to a widely discussed topic on the front pages of major news outlets. Mainstream online media has been positively polarized toward vaccines, compared with mainly negative prepandemic vaccine news. However, the pandemic was accompanied by an order-of-magnitude increase in vaccine news that, due to low prepandemic frequency, may contribute to a perceived negative sentiment. These results highlight important interactions between the volume of news and overall polarization. To the best of our knowledge, our work is the first systematic text mining study of front-page vaccine news headlines in the context of COVID-19.

Public reception of young K-pop fans in Vietnam, 2011–19

"Water buffalo youth," "mixed-race," and "out of control" are notable mainstream terms publicly used to refer to Korean popular music (hereafter "K-pop") fans in Vietnam. In a de-Westernization of fandom studies via a Vietnamese case study of the complicated relationship between nationalistic discourses and transcultural fandom, a cultural frame surrounding mainstream online news representations and public reception of young K-pop fans may be constructed, specifically the dominant shift in the mainstream narrative about Vietnamese K-pop fans between 2011 and 2019 in relation to politicized notions of shame and pride. These findings reveal and complicate various cultural, political, and ideological subjectivities of contemporary Vietnam that are similar to China's ambiguous reception of the Korean Wave. Studying a global and transcultural phenomenon like K-pop in different national contexts remains relevant and important.

Sustainability Trade-Offs in Media Coverage of Poverty Alleviation: A Content-Based Spatiotemporal Analysis in China’s Provinces

Poverty alleviation has always been fundamental for China to achieve the goal of creating a moderately prosperous society. This study conducted a content-based spatiotemporal analysis of media coverage, regression analysis of panel data, and text mining to examine how China’s Targeted Poverty Alleviation (TPA) Strategy is characterised by online mainstream media platform. A total of 10,857 articles related to TPA in 31 specific provinces of mainland China were collected and analysed by Natural Language Processing (NLP) analysis. The results of this study indicated that spatiotemporal distribution of TPA coverage was consistent with the typical logic of the Chinese government in policy implementation based on spatial and social marginalisation. Media attention on TPA is influenced by economic, environmental, and community sustainability indicators, reflecting the sustainability trade-offs in TPA-related media coverage. The keywords embedded in media coverage indicated that agricultural product promotion in extremely impoverished areas and the experiences of economically developed agricultural areas were essential for poverty eradication. Keywords emphasise top-down administrative-led poverty governance for extremely impoverished areas and local autonomy for relatively impoverished areas. This study provides perspectives for antipoverty governance and media empowerment in the postpoverty era in China.

Deep state phobia: Narrative convergence in coronavirus conspiracism on Instagram

Recent scholarship has established that conspiracist narratives proliferated in mainstream online discourse during the coronavirus pandemic. This proliferation has been provocatively characterized as a ‘conspiracy singularity’ in which previously divergent conspiracy narratives converged into a single, overarching narrative. Yet while the idea of narrative convergence has long figured in conspiracy theory research, empirical evidence has been scarce. The present article aims to address this gap by means of an investigation of an archive containing over 470,000 conspiracy-related Instagram posts from 2020. Given the size and conceptual complexity of the dataset, the paper introduces a ‘digital hermeneutics’ approach, which combines data science methods with qualitative interpretation and theorization. Operating across three levels of observation (hashtag analysis, text analysis, and image analysis) we identify patterns of convergence among different conspiracy narratives (including anti-vax, QAnon, anti-5G, and ‘The Great Reset’) over the year 2020 as well as the apparent role of protagonists and antagonists (notably Donald Trump and Bill Gates) in creating connections. In interpreting these findings we focus on the concept of ‘the Deep State’ as a bridge between various conspiracist narratives, which seems to cut diagonally across political ideologies.

Online Shopping Interest in Makassar City

Mainstream online shopping through various shopping sites continues to increase in the variety of products offered. This study aims to determine the determinants of the level of demand for products offered online. This research is Quantitative Research with an explanatory approach. This research was conducted in Makassar City by involving 50 respondents randomly but with the criteria of having an online shopping experience. Data collection techniques using a questionnaire. Giving a score with a Likert Scale technique. The data analysis technique used is multiple linear regression with the help of E-Views version 9.0 software. The results showed that product quality), price, substitution price, and taste had a positive and significant effect on the demand for online products in Makassar City. Meanwhile, the price of substitute goods has no significant effect on the demand for online products in Makassar City. Indicators of Quality, Price, and Taste can be valuable inputs for online sellers to maintain and increase consumer confidence to be able to boost sales of their products.

Investigating Audience Empowerment Through User-Generated Content Practice in Online Media Platforms

In the contemporary media sphere, newsrooms are urged to transform the exclusive editorial culture into an inclusive working environment to enable collaboration between journalists and audiences. Thus, UGC has become common practice to build a participatory space where the audiences function as active participants. However, the current UGC practice has strengthened the dilemma of audiences in journalism. On the one hand, media organisations are obliged to serve the ‘citizens’ as a part of the press's significant role in the realm of democracy. Nevertheless, at the same time, media organisations also have to operate as profit-based entities in order to survive financially. In this context, the audiences are treated as ‘consumers’ from whom the profit is derived. By utilising the concept of ‘participatory journalism’ and the audience empowerment in journalism, this article aims to capture the UGC practice in the media in Indonesia and how it affects the audiences; whether it merely serves the media as a part of business strategy or the practice also aspires to empower the audiences. This qualitative study case focuses on three mainstream online media in Indonesia, namely Kumparan, IDN Times, and Detikcom. This study finds that while the empowering UGC practice begins to appear, the general practice of UGC indicates that it is a mere business strategy. In other words, audiences are deemed as ‘consumers’ rather than ‘citizens.’ Thus, this current situation creates a ‘false sense of participation’ in participatory journalism practice.

The Correlation Analysis for New Media Internet Celebrity Economy in College Students' Entrepreneurial Values and Entrepreneurial Behavior.

Driven by the development of new media, the Internet celebrity economic marketing model has gradually become one of the mainstream online marketing models. It has aroused warm attention on the network platform and provided a breakthrough for entrepreneurship for college students. This thesis aims to explore the influence of the Internet celebrity economy on college students’ entrepreneurial values and entrepreneurial behavior. A questionnaire is conducted among students in two colleges in Xi’an. Moreover, a theoretical model is constructed according to the influence principle of entrepreneurial values on entrepreneurial behavior. The reliability and validity of the questionnaire data are analyzed, and the mediating and moderating effects are tested. The results of the questionnaire show that contemporary college students generally pay attention to Internet celebrity mainly through live broadcast platforms and shopping platforms, among which entertainment and shopping account for the largest proportion. More than 40% of college students are optimistic about the impact of Internet celebrity economy and remain rational and objective on the whole. The results of model analysis show that the standardized path coefficient of entrepreneurial values on entrepreneurial behavior reaches a significant level of 0.85, and entrepreneurial values have a positive and significant impact on entrepreneurial behavior. The influence coefficient of the Internet celebrity economy on entrepreneurial intention is 0.79, and the influence coefficient of entrepreneurial intention on entrepreneurial behavior is 0.84, both reaching a significant level. The entrepreneurial intention has an incomplete intermediary effect in the influence mechanism of the Internet celebrity economy on entrepreneurial behavior. The chain double intermediary composed of entrepreneurial motivation and entrepreneurial intention has an incomplete intermediary effect in the indirect impact path of the Internet celebrity economy on entrepreneurial behavior. The influence coefficient of the product of entrepreneurial intention and entrepreneurial policy satisfaction on entrepreneurial behavior is 0.17, which is always greater than −12.28, indicating that entrepreneurial policy satisfaction has a regulatory effect in the impact path of entrepreneurial intention on entrepreneurial behavior. The research results can guide college students to view the Internet celebrity economy rationally and objectively, and provide some guidance for them to have correct entrepreneurial values.

Slipping to the Extreme: A Mixed Method to Explain How Extreme Opinions Infiltrate Online Discussions

Qualitative research provides methodological guidelines for observing and studying communities and cultures on online social media platforms. However, such methods demand considerable manual effort from researchers and can be overly focused and narrowed to certain online groups. This work proposes a complete solution to accelerate the qualitative analysis of problematic online speech, focusing on opinions emerging from online communities by leveraging machine learning algorithms. First, we employ qualitative methods of deep observation for understanding problematic online speech. This initial qualitative study constructs an ontology of problematic speech, which contains social media postings annotated with their underlying opinions. The qualitative study dynamically constructs the set of opinions, simultaneous with labeling the postings. Next, we use keywords to collect a large dataset from three online social media platforms (Facebook, Twitter, and Youtube). Finally, we introduce an iterative data exploration procedure to augment the dataset. It alternates between a data sampler --- which balances exploration and exploitation of unlabeled data --- the automatic labeling of the sampled data, the manual inspection by the qualitative mapping team, and, finally, the retraining of the automatic opinion classifiers. We present both qualitative and quantitative results. First, we show that our human-in-the-loop method successfully augments the initial qualitatively labeled and narrowly focused dataset and constructs a more encompassing dataset. Next, we present detailed case studies of the dynamics of problematic speech in a far-right Facebook group, exemplifying its mutation from conservative to extreme. Finally, we examine the dynamics of opinion emergence and co-occurrence, and we hint at some pathways through which extreme opinions creep into the mainstream online discourse.

Prospects of E-Commerce Innovation in Increasing Students Consumer Trust

Various innovations have been made to solve problems in increasing consumer confidence to shop safely on e-commerce portals. This study aims to determine the level of consumer confidence in the e-commerce innovation model, as well as to explore the academic point of view. The study used a descriptive approach. The number of respondents in this study was 61 respondents for Students consumers, and 3 explanations from the perspective of academics. The results of the study show various problems that are often experienced by consumers and make consumer confidence relatively low, including; the first is the issue of personal information security, and the second is the issue of conformity with consumer expectations. Consumers believe that buying and selling online increases their shopping effectiveness. In addition, consumers perceive that the presence of an e-commerce portal provides great benefits for the ease of finding goods and services that consumers need. The tendency of consumers to shop online in the future will continue to increase, but consumers have not fully demonstrated the belief that mainstream online buying and selling in the future is more desirable than shopping in shopping centers

Evaluating the Psychometric Properties of the eHealth Literacy Scale (eHEALS) among Polish Social Media Users.

Social media have become mainstream online tools that allow individuals to connect and share information. Such platforms also influence people’s health behavior in the way they communicate about personal health, treatment, or physicians. Individuals’ ability to find and apply online health information on specific health problems can be measured using a valid and reliable instrument, the eHealth Literacy Scale (eHEALS). The objective of this study was to evaluate the psychometric aspects of the Polish version of this instrument (eHEALS-Pl) among social media users, which has not been explored so far. We examined the translated version of the eHEALS in a representative sample of Polish social media users (n = 1527). CAWI (computer-assisted web interviews) was a method to collect data. The reliability of the eHEALS-Pl was measured by calculating the Cronbach alpha coefficients and analyzing the principal components. Exploratory factor analysis and hypothesis testing was used to assess the construct validity of the instrument. The internal consistency of the eHEALS-Pl was sufficient: Cronbach alpha = 0.84. The item-to-total correlations ranged from r = 0.514 to 0.666. EFA revealed a single structure explaining 47.42% of the variance, with high factor loadings of the item ranging from 0.623 to 0.769. Hypothesis testing also supported the validity of eHEALS-Pl. The eHEALS-Pl evaluation supported by social media users reviled its equivalence to the original instrument developed by Norman and Skinner in 2006 and it can be used to measure e-health literacy. Since there is no prior validation of the eHEALS among social media users, these findings may indicate important directions in evaluating digital skills, especially in relation to the current challenges related to the COVID-19 pandemic.

Towards an Interreligious Engagement: A Case Study of Paguyuban Eklasing Budi Murko (PEBM) in Kulon Progo, Yogyakarta

The feeling of being the majority often drives people to hegemony other minorities. The presence of religious minorities seems marginalized because they do not have much power to assert their existence. This study examines the dynamic relationship between Paguyuban Eklasing Budi Murko (PEBM) followers and the religious majority in the Salamrejo. Thus, this research uses two theories: the indigenous religion paradigm and the non-formal interfaith dialogue. The research data is collected from an in-depth interview, literature studies, and mainstream online data. The hegemony assumption reveals that the feeling of the majority continues dominating society. In contrast, the hypothesis is too general. The relationship of PEBM as the indigenous religion and world religions community, particularly Islam, does not reflect this hypothesis. The dynamic relationships between PEBM and the majority groups in the village of Salamrejo are engaging with one another, which is influenced by two factors: First, Mbah Mangun is the elder and the most respected person in PEBM. Second, by the teachings of PEBM per se, which is very fluid to all religions in the paradigm of inter-subjectivity and interreligious engagement as a new strategy for PEBM to encourage social acceptance.

Distorted dense analogs intelligent recognition in fisheye lenses by partially featured points calibrating and clustering.

Dense analogs intelligent recognition (DAIR) has many potential applications in various fields as a new cross-disciplinary frontier of artificial intelligence and optical technology. However, with extensive application of fisheye lenses, inherent distortions in fisheye images have brought new challenges to DAIR. To solve this problem, we propose and experimentally demonstrate a partially featured points calibrating method that needs only correction of central points of the bounding boxes output by a convolutional neural network (CNN). The key to our method is a central-coordinate calibrating and clustering algorithm (CCCCA) based on a hemispheric double longitude projection model. Experimental results show that the CCCCA reduces the classification error rate by 6.05%, enhancing the classification accuracy of distorted DAIR up to 99.31%. Such classification accuracy is about 2.74% higher than that achieved by the mainstream online hard example mining algorithm, effectively modifying recognition errors induced by the CNN.

Learning Beyond the Cloud: Optimization Strategies for Online Course

The rapid development of the mobile Internet has given birth to new changes in the field of education. Cloud-based teaching, which has been in an auxiliary role for a long time, has broken the equipment limitations of computer rooms, and cloud-based classrooms have gradually become an important supplement to on-site teaching. Starting from the current mainstream online classroom teaching mode, this article discusses and analyzes the advantages and disadvantages of the Internet teaching mode in the current practice, so as to provide a strategic reference for further optimization of practice.