Assess Information Credibility Research Articles

A System to Ensure Information Trustworthiness in Artificial Intelligence Enhanced Higher Education

Aim/Purpose: The purpose of this paper is to address the challenges posed by disinformation in an educational context. The paper aims to review existing information assessment techniques, highlight their limitations, and propose a conceptual design for a multimodal, explainable information assessment system for higher education. The ultimate goal is to provide a roadmap for researchers that meets current requirements of information assessment in education. Background: The background of this paper is rooted in the growing concern over disinformation, especially in higher education, where it can impact critical thinking and decision-making. The issue is exacerbated by the rise of AI-based analytics on social media and their use in educational settings. Existing information assessment techniques have limitations, requiring a more comprehensive AI-based approach that considers a wide range of data types and multiple dimensions of disinformation. Methodology: Our approach involves an extensive literature review of current methods for information assessment, along with their limitations. We then establish theoretical foundations and design concepts for EMIAS based on AI techniques and knowledge graph theory. Contribution: We introduce a comprehensive theoretical framework for an AI-based multimodal information assessment system specifically designed for the education sector. It not only provides a novel approach to assessing information credibility but also proposes the use of explainable AI and a three-pronged approach to information evaluation, addressing a critical gap in the current literature. This research also serves as a guide for educational institutions considering the deployment of advanced AI-based systems for information evaluation. Findings: We uncover a critical need for robust information assessment systems in higher education to tackle disinformation. We propose an AI-based EMIAS system designed to evaluate the trustworthiness and quality of content while providing explanatory justifications. We underscore the challenges of integrating this system into educational infrastructures and emphasize its potential benefits, such as improved teaching quality and fostering critical thinking. Recommendations for Practitioners: Implement the proposed EMIAS system to enhance the credibility of information in educational settings and foster critical thinking among students and teachers. Recommendation for Researchers: Explore domain-specific adaptations of EMIAS, research on user feedback mechanisms, and investigate seamless integration techniques within existing academic infrastructure. Impact on Society: This paper’s findings could strengthen academic integrity and foster a more informed society by improving the quality of information in education. Future Research: Further research should investigate the practical implementation, effectiveness, and adaptation of EMIAS across various educational contexts.

Strategies for Assessing Health Information Credibility Among Older Social Media Users in China: A Qualitative Study

ABSTRACT The fact that social media gives users easy access to online health information raises the question of what information evaluation strategies older adults use to distinguish trustworthy from unreliable health information. Identifying how older adults assess the credibility of health information that they acquire on social media is an important step toward understanding and reducing their susceptibility to health misinformation. In this study, we investigated the credibility assessment strategies used by older WeChat users in China. Following a qualitative approach, we conducted in-depth interviews with 40 WeChat users 65–85 years old (M = 71.75, SD = 6.65) in China who had acquired health information on WeChat. Results of theoretical thematic analysis revealed five source-based and content-based evaluative strategies: (1) determining the communicative orientation of the source, (2) assessing source reputation, (3) confirming content based on life experiences, (4) checking for exaggeration in claimed effects, and (5) assessing the consistency of content across sources. Older WeChat users’ reliance on certain heuristic cues and their self-reliant approach to assessing information credibility provide contextual explanations for the link between heuristic processing and susceptibility to health misinformation. The findings have implications for anti-misinformation interventions targeting the older population in China and potentially beyond.

What Influences Audience Susceptibility to Fake Health News: An Experimental Study Using a Dual Model of Information Processing in Credibility Assessment

ABSTRACT This experimental study investigates the effects of several heuristic cues and systematic factors on users’ misinformation susceptibility in the context of health news. Specifically, it examines whether author credentials, writing style, and verification check flagging influence participants’ intent to follow article behavioral recommendations provided by the article, perceived article credibility, and sharing intent. Findings suggest that users rely only on verification checks (passing/failing) in assessing information credibility. Of the two antecedents to systematic processing, social media self-efficacy moderates the links between verification and participants’ susceptibility. Theoretical and practical implications are discussed.

The design and evaluation of a nudge‐based interface to facilitate consumers' evaluation of online health information credibility

AbstractEvaluating the quality of online health information (OHI) is a major challenge facing consumers. We designed PageGraph, an interface that displays quality indicators and associated values for a webpage, based on credibility evaluation models, the nudge theory, and existing empirical research concerning professionals' and consumers' evaluation of OHI quality. A qualitative evaluation of the interface with 16 participants revealed that PageGraph rendered the information and presentation nudges as intended. It provided the participants with easier access to quality indicators, encouraged fresh angles to assess information credibility, provided an evaluation framework, and encouraged validation of initial judgments. We then conducted a quantitative evaluation of the interface involving 60 participants using a between‐subject experimental design. The control group used a regular web browser and evaluated the credibility of 12 preselected webpages, whereas the experimental group evaluated the same webpages with the assistance of PageGraph. PageGraph did not significantly influence participants' evaluation results. The results may be attributed to the insufficiency of the saliency and structure of the nudges implemented and the webpage stimuli's lack of sensitivity to the intervention. Future directions for applying nudges to support OHI evaluation were discussed.

Adapting the 3S-model for investigating trust in arc solutions in the Danish construction industry

Automated solutions to building compliance checking has been slow in its implementation in the Danish construction industry. Mainly socio-technical barriers, such as trust in automation challenge the spread of technological advances from research and development. This paper studies the trust relationship of Architectural, Engineering, and Construction (AEC) professionals towards Automated Rule Checking (ARC) systems in Denmark. The 3S-model from the field of cognitive psychology was employed to help understanding the varying ways people assess information credibility. Three hypotheses were tested through experimentation to investigate the significance of different information features, and pertaining user characteristics on human-automation trust behaviors in the domain of ARC. Although the highly specified research area targeted a relatively small demographic within the Danish construction industry, the findings present an interesting new perspective on the common characteristics and trust behaviors of the end user. With the refinement of the framework’s application, the 3S-model can elucidate the many factors that are believed to influence technology acceptance within the industry. This research aims to contribute to the development of ARC solutions with a front-end viewpoint on trustworthiness.

I don’t understand you but I trust you: using computer-aided text analysis to examine medical terminology use and engagement of vaccine online articles

ABSTRACT Background Public health departments recommend that medical terms should be replaced by plain language in order to make health information understandable to the general public. However, as a heuristic cue to assess information credibility, terminology makes an article appear more convincing and objective to its audience. Terminology can also make the information more engaging and persuasive. Using online articles about vaccine as an example, the present study examined whether practitioners should reduce medical terminology to lower the readability level or increase medical terminology to engage more audience. Method Computer-aided text analysis was used to analyze 541 pro-vaccine articles and 382 anti-vaccine articles. Results Anti-vaccine online articles contained significantly more medical terminology than pro-vaccine articles. Medical terminology was not significantly associated with readability. The use of medical terminology positively predicted reader engagement in both pro- and anti-vaccine articles, while the reading level of the articles negatively predicted the pro-vaccine articles’ reader engagement. Finally, pro-vaccine articles gradually reduced the use of medical terms and lowered readability from 2007 to 2017. Readability and the amount of medical terminology included in anti-vaccine articles did not significantly change over time. Conclusions Health practitioners are recommended not to simply reduce the use of medical terminology to lower readability levels when creating health education materials. Other recommendations for designing effective health messages and combating misinformation are provided.

Information credibility evaluation in online professional social network using tree augmented naïve Bayes classifier

In recent years, companies depend on the Internet for posting job advertisements and attracting qualified personnel. However, with the vast number of Internet users and the tremendous amount of information on the Internet, it is difficult to accurately evaluate the credibility of the information that candidates provide on the Internet. Therefore, we propose an approach to assess information credibility in terms of trustworthiness and authority to identify unreliable user profiles in online professional social networks. Our approach calculates the trustworthiness probabilities of user profile information using the Tree Augmented Naive Bayes (TAN) classifier. It also measures the authority of individual users by applying the PageRank algorithm for analyzing the user interactions in the professional social networks. Finally, a group of LinkedIn users’ profiles is selected for conducting experiments to validate the proposed approach. Experiments based on a real-world scenario show that our approach integrating the TAN Bayes and PageRank algorithm outperforms other existing approaches in classification accuracy. In addition, the approach has been applied to another social network, namely, Maimai in China to further demonstrate its usefulness.

“WHY WOULD SOMEONE INTENTIONALLY LIE?”: ASSESSING THE CREDIBILITY OF CANCER (MIS)INFORMATION ON FACEBOOK

As misinformation on social media continues to proliferate, scholars are increasingly calling for explorations of the negative ramifications of health-related misinformation on health outcomes. In 2018, 96% of the top 100 shared health articles were shared on Facebook; 51% of these had neutral to poor credibility. This exploratory study seeks to understand how U.S. Latinos assess the credibility of the cancer screening and prevention information (CPSI) they engage with on Facebook. Through semi-structured in-depth interviews, participants (n=20) accessed their Facebook account alongside the researcher, typed “cancer” in the search bar, and discussed cancer-related posts they engaged with during the past 6-12 months. If a participant engaged with CPSI, the researcher asked questions regarding if and how participants assessed the credibility of the information. Computer screen and audio were recorded for analysis. Interviews are being analyzed thematically, and CPSI via content analysis. Preliminary findings suggest most CPSI engagement comes from Facebook Friends and Groups that at times share unreliable information (e.g. foods claiming cancer prevention/curative properties). Participants with higher education levels were more likely to verify information via outside sources, while others looked for cues within the post to assess credibility (i.e. being shared by a reputable news agency). However, most individuals rely on heuristics (post virality, cultural associations, testimonies) to assess information credibility, rather than a verification process. These findings can assist in developing social media campaigns to counteract health misinformation. Findings also raise broader questions regarding Facebook’s role/responsibility in regulating and monitoring its platform’s health misinformation.

The evidence for motivated reasoning in climate change preference formation

Despite a scientific consensus, citizens are divided when it comes to climate change — often along political lines. Democrats or liberals tend to believe that human activity is a primary cause of climate change, whereas Republicans or conservatives are much less likely to hold this belief. A prominent explanation for this divide is that it stems from directional motivated reasoning: individuals reject new information that contradicts their standing beliefs. In this Review, we suggest that the empirical evidence is not so clear, and is equally consistent with a theory in which citizens strive to form accurate beliefs but vary in what they consider to be credible evidence. This suggests a new research agenda on climate change preference formation, and has implications for effective communication. In this Review, a Bayesian framework is used to explain climate change belief updating, and the evidence required to support claims of directional motivated reasoning versus a model in which people aim for accurate beliefs, but vary in how they assess information credibility.

A Credibility Analysis System for Assessing Information on Twitter

Information credibility on Twitter has been a topic of interest among researchers in the fields of both computer and social sciences, primarily because of the recent growth of this platform as a tool for information dissemination. Twitter has made it increasingly possible to offer near-real-time transfer of information in a very cost-effective manner. It is now being used as a source of news among a wide array of users around the globe. The beauty of this platform is that it delivers timely content in a tailored manner that makes it possible for users to obtain news regarding their topics of interest. Consequently, the development of techniques that can verify information obtained from Twitter has become a challenging and necessary task. In this paper, we propose a new credibility analysis system for assessing information credibility on Twitter to prevent the proliferation of fake or malicious information. The proposed system consists of four integrated components: a reputation-based component, a credibility classifier engine, a user experience component, and a feature-ranking algorithm. The components operate together in an algorithmic form to analyze and assess the credibility of Twitter tweets and users. We tested the performance of our system on two different datasets from 489,330 unique Twitter accounts. We applied 10-fold cross-validation over four machine learning algorithms. The results reveal that a significant balance between recall and precision was achieved for the tested dataset.

A MODEL FOR RECALIBRATING CREDIBILITY IN DIFFERENT CONTEXTS AND LANGUAGES - A TWITTER CASE STUDY

Due to the growing dependence on the WWW UserGenerated Content (UGC) as a primary source for information and news, the research on web credibility is becoming more important than ever before. In this paper we review previous efforts to evaluate information credibility, focusing specifically on microblogging. In particular, we provide a comparison of different systems for automatic assessment of information credibility based on the used techniques and features, and we classify the Twitter credibility surveys based on the features considered. We then propose a general model to assess information credibility on UGC different platforms, including Twitter, which employs a contextual credibility approach that examines the effect of culture, situation, topic variations, and languages on assessing credibility, using Arabic context as an example. We identify several factors that users may consider in determining credibility, and argue that the importance of each factor may vary with a context. Future work will include both a user study and machine learning techniques to evaluate the effectiveness of various factors for information credibility classification in different contexts.

Social and Heuristic Approaches to Credibility Evaluation Online

The tremendous amount of information available online has resulted in considerable research on information and source credibility. The vast majority of scholars, however, assume that individuals work in isolation to form credibility opinions and that people must assess information credibility in an effortful and time-consuming manner. Focus group data from 109 participants were used to examine these assumptions. Results show that most users rely on others to make credibility assessments, often through the use of group-based tools. Results also indicate that rather than systematically processing information, participants routinely invoked cognitive heuristics to evaluate the credibility of information and sources online. These findings are leveraged to suggest a number of avenues for further credibility theorizing, research, and practice.