Online Information Review

Name: Online Information Review
Creator: vLex
License: https://vlex.com/terms-of-service/

Publisher:: Emerald Group Publishing Limited
Publication date:: 2021-02-01

ISBN:: 1468-4527

Issue Number

No. 48-7, May - June - July - September - November 2024

Latest documents

Bad robot? The benevolent use of automated software and social bots by influencers in the #antivaxx discourse on Twitter
Purpose: Automated social media messaging tactics can undermine trust in health institutions and public health advice. As such, we examine automated software programs (ASPs) and social bots in the Twitter anti-vaccine discourse before and after the release of COVID-19 vaccines. Design/methodology/approach: We compare two Twitter datasets comprising user accounts and associated English-language tweets featuring the keywords “#antivaxx” or “anti-vaxx.” The first dataset, from 2018 (pre-COVID vaccine), includes 3,154 user accounts and 6,380 tweets. The second comprises 327,067 accounts and 545,268 tweets published during the 12 months following December 1, 2020 (post-COVID vaccine). Using Information Laundering Theory (ILT), the datasets were examined manually and through user analytics and machine learning to identify activity, visibility, verification status, vaccine position, and ASP or bot technology use. Findings: The post-COVID vaccine dataset showed an increase in highly probable bot accounts (31.09%) and anti-vaccine accounts. However, both datasets were dominated by pro-vaccine accounts; most highly active (59%) and highly visible (50%) accounts classified as probable bots were pro-vaccine. Originality/value: This research is the first to compare bot behaviors in the “#antivaxx” discourse before and after the release of COVID-19 vaccines. The prevalence of mostly benevolent probable bot accounts suggests a potential overstatement of the threat posed by anti-vaccine accounts using ASPs or bot technologies. By highlighting bots as intermediaries that disseminate both pro- and anti-vaccine content, we extend ILT by identifying a benevolent variant and offering insights into bots as “pathways” to generating mainstream information.
Editorial: Generative artificial intelligence in the creator economy
Scientific evidence and specific context: leveraging large language models for health fact-checking
Purpose: This study aims to evaluate the performance of LLMs with various prompt engineering strategies in the context of health fact-checking. Design/methodology/approach: Inspired by Dual Process Theory, we introduce two kinds of prompts: Conclusion-first (System 1) and Explanation-first (System 2), and their respective retrieval-augmented variations. We evaluate the performance of these prompts across accuracy, argument elements, common errors and cost-effectiveness. Our study, conducted on two public health fact-checking datasets, categorized 10,212 claims as knowledge, anecdotes and news. To further analyze the reasoning process of LLM, we delve into the argument elements of health fact-checking generated by different prompts, revealing their tendencies in using evidence and contextual qualifiers. We conducted content analysis to identify and compare the common errors across various prompts. Findings: Results indicate that the Conclusion-first prompt performs well in knowledge (89.70%,66.09%), anecdote (79.49%,79.99%) and news (85.61%,85.95%) claims even without retrieval augmentation, proving to be cost-effective. In contrast, the Explanation-first prompt often classifies claims as unknown. However, it significantly boosts accuracy for news claims (87.53%,88.60%) and anecdote claims (87.28%,90.62%) with retrieval augmentation. The Explanation-first prompt is more focused on context specificity and user intent understanding during health fact-checking, showing high potential with retrieval augmentation. Additionally, retrieval-augmented LLMs concentrate more on evidence and context, highlighting the importance of the relevance and safety of retrieved content. Originality/value: This study offers insights into how a balanced integration could enhance the overall performance of LLMs in critical applications, paving the way for future research on optimizing LLMs for complex cognitive tasks. Peer review: The peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-02-2024-0111
Research on the motivation for viewer addiction to live streaming: a cross-cultural investigation of China and the USA
Purpose: With the increasing popularity of live streaming, the exploration of online behaviors on live streaming platforms has become a research hot spot in academia. However, the literature lacks cross-cultural investigations into live streaming addictive (LSA) behaviors among viewers. To address this gap, based on stimulus-organism-response (SOR) theory and motivation theory, this study investigates how information seeking, sensation seeking and community commitment (stimulus) affect hedonic and utilitarian motivation (organism), which in turn influence viewers’ LSA behaviors (response). This study also compares American and Chinese viewers to understand how LSA behaviors differ across national cultures. Design/methodology/approach: We surveyed live stream viewers in both nations, collecting 758 valid samples including 377 Chinese respondents and 381 American respondents for use with the research model. A partial least squares (PLS) approach is employed for data analysis. Findings: The results show that information seeking increases utilitarian motivation, sensation seeking increases hedonic motivation, and community commitment strengthens both hedonic and utilitarian motivation. Both hedonic motivation and utilitarian motivation positively affect LSA behaviors. In addition, based on cross-cultural analysis, we found that there was a significant mediating path common to the respondents in China and the USA (i.e. sensation seeking ? Hedonic motivation ? LSA). Originality/value: The findings of this study make theoretical contributions to live streaming research and offer practical guidance for live streaming firms to formulate proper marketing strategies. Last, this study outlines several limitations and possible directions for future research.
Multi-level emotion propagation in natural disaster events: diverse leadership of super-spreaders in different levels of hierarchy
Purpose: In social media, crisis information susceptible of generating different emotions could be spread at exponential pace via multilevel super-spreaders. This study aims to interpret the multi-level emotion propagation in natural disaster events by analyzing information diffusion capacity and emotional guiding ability of super-spreaders in different levels of hierarchy. Design/methodology/approach: We collected 47,042 original microblogs and 120,697 forwarding data on Weibo about the “7.20 Henan Rainstorm” event for empirical analysis. Emotion analysis and emotion network analysis were used to screen emotional information and identify super-spreaders. The number of followers is considered as the basis for classifying super-spreaders into five levels. Findings: Official media and ordinary users can become the super-spreaders with different advantages, creating a new emotion propagation environment. The number of followers becomes a valid basis for classifying the hierarchy levels of super-spreaders. The higher the level of users, the easier they are to become super-spreaders. And there is a strong correlation between the hierarchy level of super-spreaders and their role in emotion propagation. Originality/value: This study has important significance for understanding the mode of social emotion propagation and making decisions in maintaining social harmony. Peer review: The peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-03-2024-0192.
Privacy? What’s that? Differences in privacy boundaries
Purpose: While research on privacy concerns is rich in understanding and depth, there is still not a clear understanding of why people express having privacy concerns, but do not behave consistently with their concern. We propose that this misalignment derives from a diverse set of privacy boundaries, depending on the user. This research builds on prior Communication Privacy Management Theory research to further define individual privacy boundaries. Beyond that, we evaluate the relationship between the privacy boundaries people set, and their ability to protect themselves. Design/methodology/approach: A survey was conducted to assess how private individuals find twenty items. Along with measuring the sensitivity of information, we collected responses on the Online Privacy Information Literacy test to measure differences in sensitivity based on privacy knowledge. 285 participant’s responses were evaluated using exploratory factor analysis and K-means clustering. Findings: We identify five different groups of privacy indicators. Our findings also suggest that users have limited understanding of how to keep data private, even if they have high privacy concerns. Originality/value: We contribute to theory by offering guidance on how to better apply theoretical understanding, based on our results. More explicitly, we offer analysis that suggests boundary conditions might be absent from current theoretical understanding. Practically, we offer guidance for understanding privacy differences, which is important to understanding how to implement privacy protection laws.
Network relations among open government data stakeholders: a structural social capital and ERGM analysis
Purpose: The objective of this research is to methodically categorize the various types of Open Government Data (OGD) stakeholders, and to elucidate the intricate network relationships among OGD stakeholders, along with the underlying mechanisms that shape their formation. Design/methodology/approach: To comprehend the collaboration mechanism of stakeholders in the OGD ecosystem, the authors constructed an OGD multi-stakeholder relationship network by using data from the Shandong Province Data Application Innovation and Entrepreneurship Competition. Based on the structural social capital theory and exponential random graph model (ERGM), an analytical framework was established to explore the formation mechanism of the collaborative network of OGD multi-stakeholder. Findings: The results indicate that multi-stakeholder collaboration among government, enterprises and the public is crucial for achieving OGD goals. Organizing OGD competitions serves as an effective mechanism for solidifying and maintaining relationships among OGD stakeholder groups. Degree centrality and structural parameters reveal a Matthew effect within the connection process of the OGD ecosystem's collaborative network. Additionally, there is evidence of agglomeration and transferability within the network's structure. Originality/value: This study contributes to the understanding regarding the formation mechanism of OGD stakeholders. The findings have implications for developing multi-stakeholder relationship networks of OGD and driving OGD initiatives. Peer review: The peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-06-2023-0284
How are news portals integrated into daily news habits? A study on news trust, news repertoires and political participation in South Korea
Purpose: The modern high-choice news environment has changed the way people consume news. We examined the structure of news repertories, the role of news trust (and cynicism) in shaping news repertoires, and the effects of different news repertoires on political participation in the South Korean news environment, where news portals function as major news sources. Design/methodology/approach: We conducted a two-wave national survey of adults in January and February 2022 with a three-week time interval. Latent profile analyses and latent profile regression were employed. Findings: Four major news repertoires were identified: news portal-concentrated, portal and TV combination, all except print and SNS, and platform omnivores. Trust in mainstream news media was found to be high in the following order: platform omnivores, all except print and SNS, portal and TV combination, and portal-concentrated repertoires. Cynicism about mainstream media was found to be higher among the users of portal-concentrated repertoire than those using the other three repertoires. Those who rely mainly on news portals for news (i.e. portal-concentrated) were less likely to participate in politics. Originality/value: This study provides comprehensive insights into how audience perceptions of trust and cynicism toward mainstream news media shape news consumption patterns in today’s high-choice media environment. In particular, the findings of this study illuminate the function of news portals as a substitute platform for those who exhibit lower trust and higher cynicism toward mainstream news media.
The digitalization tendency of young adults: differences by living environment, gender and education
Purpose: This study investigates the differences in digitalization tendency levels among young adults (18–30) regarding their living environment, gender, and education. Design/methodology/approach: Digital transformation (DT) refers to digital technologies becoming more prevalent in society. This process transforms lifestyles and business models using information and communication technologies. While digital transformation is on everyone’s agenda worldwide, it is still in its infancy. The data collected from 1,067 young adults show that all three control variables, living environment (CV1), gender (CV2), and education level (CV3), have a significant impact on digitalization tendency. Samples are selected using stratified sampling. Frequency distributions of quantitative data, descriptive statistics, ANOVA analysis, independent sample t-tests, reliability and validity analysis are interpreted in SPSS. Findings: It is concluded from the study that the young adults living in Istanbul are more inclined to use digital technologies and to see the process from the standpoint of its positive results. It is observed that women have a higher digitalization tendency than men. Finally, the tendency to use digital technologies increases with education level, as does the likelihood of perceiving the outputs of these technologies as positive. Research limitations/implications: The study limited the sample to young adults aged 18–30. Originality/value: Digitalization tendencies are positively influenced by factors such as living in Istanbul, being a woman, and having a high education level. Peer review: The peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-01-2023-0020.
Artificial intelligence (AI) for supply chain collaboration: implications on information sharing and trust
Purpose: Managers and scholars alike claim that artificial intelligence (AI) represents a tool to enhance supply chain collaborations; however, existing research is limited in providing frameworks that categorise to what extent companies can apply AI capabilities and support existing collaborations. In response, this paper clarifies the various implications of AI applications on supply chain collaborations, focusing on the core elements of information sharing and trust. A five-stage AI collaboration framework for supply chains is presented, supporting managers to classify the supply chain collaboration stage in a company’s AI journey. Design/methodology/approach: Using existing literature on AI technology and collaboration and its effects of information sharing and trust, we present two frameworks to clarify (a) the interrelationships between information sharing, trust and AI capabilities and (b) develop a model illustrating five AI application stages how AI can be used for supply chain collaborations. Findings: We identify various levels of interdependency between trust and AI capabilities and subsequently divide AI collaboration into five stages, namely complementary AI applications, augmentative AI applications, collaborative AI applications, autonomous AI applications and AI applications replacing existing systems. Originality/value: Similar to the five stages of autonomous driving, the categorisation of AI collaboration along the supply chain into five consecutive stages provides insight into collaborations practices and represents a practical management tool to better understand the utilisation of AI capabilities in a supply chain environment.