Information Credibility Evaluation Research Articles

Competing for Trust: How Initiative and Repetition of Exposure Influence Information Credibility

ABSTRACTModern democracy often requires citizens to engage in policy debates demanding multidisciplinary knowledge. This can lead to a cognitive gap that urges citizens to seek information for decision‐making on unfamiliar policy issues. The credibility of this information is crucial, as it shapes individual perceptions and attitudes, which aggregate as public opinions. To deepen the understanding of information credibility, this study proposes an experiment leveraging theories of the continued influence effect and the illusory truth effect to answer how initiative and repetition of exposure influence the evaluation of information credibility. 1,400 participants will be recruited and requested to rate the credibility of opposing arguments regarding a fictitious referendum provided with varying orders and numbers. The result is expected to show a positive causality between exposure initiative, information repetition, and information credibility. The findings will aid in promoting well‐informed democratic deliberation in an era of political polarization.

Competency model development: The backbone of successful stealth assessments

AbstractBackgroundStealth assessment is a learning analytics method, which leverages the collection and analysis of learners' interaction data to make real‐time inferences about their learning. Employed in digital learning environments, stealth assessment helps researchers, educators, and teachers evaluate learners' competencies and customize the learning experience to their specific needs. This adaptability is closely intertwined with theories related to learning, engagement, and motivation. The foundation of stealth assessment rests on evidence‐cantered design (ECD), consisting of four core models: the Competency Model (CM), Evidence Model, Task Model, and Assembly Model.ObjectiveThe first step in designing a stealth assessment entails producing operational definitions of the constructs to be assessed. The CM establishes a framework of latent variables representing the target constructs, as well as their interrelations. When developing the CM, assessment designers must produce clear descriptions of the claims associated with the latent variables and their states, as well as sketch out how the competencies can be measured using assessment tasks. As the designers elaborate on the assessment model, the CM definitions need to be revisited to make sure they work with the scope and constraints of the assessment. Although this is the first step, problems at this stage may result in an assessment that does not meet the intended purpose. The objective of this paper is to elucidate the necessary steps for CM development and to highlight potential challenges in the process, along with strategies for addressing them, particularly for designers without much formal assessment experience.MethodThis paper is a methodological exposition, showcasing five examples of CM development. Specifically, we conducted a qualitative retrospective analysis of the CM development procedure, wherein participants unfamiliar with ECD applied the framework and showcased their work. In a stealth assessment course, four groups of students (novice stealth assessment designers) engaged in developing stealth assessments for challenging‐to‐measure constructs across four distinct projects. During their CM development process, we observed various activities to pinpoint areas of difficulty.ResultsThis paper presents five illustrative examples, including one for assessing physics understanding and four for the development of CMs for four complex competencies: (1) systems thinking, (2) online information credibility evaluation, (3) computational thinking, and (4) collaborative creativity. Each example represents a case in CM development, offering valuable insights.ConclusionThe paper concludes by discussing several guidelines derived from the examples discussed. Emphasizing the importance of dedicating ample time to fine‐tune CMs can significantly enhance the accuracy of assessments related to learners' knowledge and skills. It underscores the significance of qualitative phases in crafting comprehensive stealth assessments, such as CMs, alongside the quantitative statistical modeling and technical aspects of these assessments.

Developing a Tool for Assessing the Process of Seeking Health Information: Online Think-Aloud Method.

Nursing students can access massive amounts of online health data to drive cutting-edge evidence-based practice in clinical placement, to bridge the theory-practice gap. This activity requires investigation to identify the strategies nursing students apply to evaluate online health information. Online Think-Aloud sessions enabled 14 participants to express their cognitive processes in navigating various educational resources, including online journals and databases, and determining the reliability of sources, indicating their strategies for information-seeking, which helped to create this scoring system. Easy access and user convenience were clearly the instrumental factors in this behavior, which has troubling implications for the lack of use of higher-quality resources (e.g., from peer-reviewed academic journals). The identified challenges encountered during resource access included limited skills in the critical evaluation of information credibility and reliability, signaling a requirement for improved information literacy skills. Participants acknowledged the importance of evidence-based, high-quality information, but faced numerous barriers, such as restricted access to professional and specialty databases, and a lack of academic skills training. This paper develops and critiques a Performative Tool for assessing the process of seeking health information using an online Think-Aloud method, and explores factors and strategies contributing to evidence-based health information access and utilization in clinical practice, aiming to provide insight into individuals' information-seeking behaviors in online health contexts.

AHBO-DMN: Autoregressive Honey Badger Optimization Deep Maxout Network for Credibility Assessment of Web-Based Health Information

The cure and treatment suggested by non-medical experts on the Internet can lead to more damage than expected since spreading such misinformation may adversely affect public health. Different interventions have been modeled to help users better evaluate web-based health information. However, retrieving information credibility still results in a complex task. This paper aims to design a novel credibility assessment method using the proposed Autoregressive Honey Badger Optimization-based Deep Maxout Network (AHBO-based DMN) in web-based healthcare data. The AHBO is devised by inheriting Conditional Autoregressive Value At Risk (CAViaR) features with Honey Badger Algorithm (HBA). Finally, DMN, which is trained by the developed AHBO algorithm, accomplishes credibility information. Two different datasets on health misinformation were used in this study, namely News and Twitter. The effectiveness of the developed method is measured with a Deep learning-based text classifier, Credibility perception method, Hybrid ML approach, Consumer Health Information Credibility Evaluation Technique (CHICET), and Fuzzy Ant Colony Improvement (ACI) system. The developed model attains higher performance in terms of accuracy, True Positive Rate (TPR), and False Positive Rate (FPR) as 0.965, 0.955, and 0.101 with the News dataset. While evaluating the Twitter dataset, testing accuracy, TPR, and FPR measured by the proposed model are 0.961, 0.954, and 0.207, respectively. The results indicate an improvement in terms of credible assessment when compared to existing state-of-the-art models.

Information Credibility Evaluation in Presence of Users’ Safety in New Retailing

Understanding users’ safety perception of the credibility of web-based information has become increasingly important in the context of new retailing. This study extends the existing literature by exploring the factors influencing information credibility in the context of new retailing. Based on the technology acceptance model and the rational behavior theory, a theoretical model for the assessment of information credibility in new retailing was developed. We analyzed the factors influencing users’ safety preference toward information communication procedures and information credibility in new retailing based on two aspects: perceived information quality and user judgment motivation. The reliability and validity of the model measure were analyzed, and structural equation modeling was used to test the model hypotheses. The following results were obtained: (1) Authenticity, accuracy, and practicability positively affected the perceived information quality of new retailing information; (2) User judgment motivation had a positive impact on information users’ safety preference and information credibility; (3) Users’ safety preference positively affected information credibility; (4) Information acquisition, social interaction, and self-identity positively affected the perceived credibility of new retailing information.

Research on the Credibility of Social Media Information Based on User Perception

In order to accurately obtain the credibility of social media information, improve the efficiency of credibility evaluation, and enhance the security of social media, this paper proposes a method for evaluating the credibility of social media information based on user perception. Starting from the three dimensions of subject credibility, source credibility, and content credibility, the information credibility evaluation dimensions are analyzed. According to the information credibility evaluation dimension, establish a social media information database and deal with spam in the database. Perform credit evaluation based on the results of various data analyses in the database, and extract meaningful keywords from social media information through feature selection algorithms to form keyword clusters. Finally, based on user perception theory, the credibility evaluation of social media information is realized. The experimental results show that the quantitative results of the method for evaluating the credibility of social media information are close to the actual situation, the evaluation results obtained are more accurate, and the evaluation time is short, which can provide a theoretical basis for supervision and management.

The effects of information source and eHealth literacy on consumer health information credibility evaluation behavior

Health consumers can easily access information on the Internet. The quality of online health information varies and overall is concerning. The definition of eHealth literacy implies that consumers' ability to evaluate online health information varies with their eHealth literacy. A lab-based experiment was conducted to investigate how information source and consumers’ eHealth literacy affect their use of different types of indicators and criteria in evaluating the credibility of online health information. Credibility evaluation was viewed as a process of utilizing criteria to interpret webpage indicators. Twenty-five participants each viewed a total of 15 webpages from government, commercial, and online forum sources (five from each). Gaze-cued retrospective think-aloud was employed to elicit the indicators and criteria that participants used to evaluate the webpages. The findings showed that content-related indicators and criteria were used the most in credibility evaluation across different information sources. Participants used source indicators more often than design indicators in evaluating government websites and used design criteria more often than individual factors in evaluating commercial websites. eHealth literacy did not significantly affect credibility evaluation but showed a marginally significant interaction effect with information sources on the number of indicators used. The results enhance our understanding of factors influencing consumer online health information evaluation behavior.

Knowledge and information credibility evaluation strategies regarding COVID-19: A cross-sectional study

BackgroundThe novel coronavirus disease (COVID-19) pandemic has not only caused significant challenges for health systems worldwide, but also fueled a surge in misinformation. Nurses as frontline health care providers should be equipped with the most accurate information on COVID-19. PurposeThis study examines nurses’ knowledge and strategies of information credibility sourcing. MethodA cross-sectional survey among nurses and laypersons with no health care background. The questionnaire dealt with knowledge and ability assess credibility of COVID-19 information. FindingsNurses' knowledge of COVID-19 preventative behaviors was significantly higher than that of laypersons; however, there was no difference in science-based knowledge of COVID-19. In contrast to laypersons, nurses in this study were better able to discern the credibility of health-related information about COVID-19 than laypersons. Yet they rarely used scientific criteria in evaluating conflicting information. DiscussionGiven the importance of assessing the credibility of information, both information literacy skills and science-based knowledge about COVID-19 should be offered.

Incorporating message format into user evaluation of microblog information credibility: A nonlinear perspective

The spreading of misinformation and disinformation is a great problem on microblogs, leading user evaluation of information credibility a critical issue. This study incorporates two message format factors related to multimedia usage on microblogs (vividness and multimedia diagnosticity) with two well-discussed factors for information credibility (i.e., argument quality and source credibility) as a holistic framework to investigate user evaluation of microblog information credibility. Further, the study draws on two-factor theory and its variant three-factor lens to explain the nonlinear effects of the above factors on microblog information credibility. An online survey was conducted to test the proposed framework by collecting data from microblog users. The research findings reveal that for the effects on microblog information credibility: (1) argument quality (a hygiene factor) exerts a decreasing incremental effect; (2) source credibility (a bivalent factor) exerts only a linear effect; and (3) multimedia diagnosticity (a motivating factor) exerts an increasing incremental effect. This study adds to current knowledge about information credibility by proposing an insightful framework to understand the key predictors of microblog information credibility and further examining the nonlinear effects of these predictors.

Discovering differential features: Adversarial learning for information credibility evaluation

A series of deep learning approaches extract a large number of credibility features to detect fake news on the Internet. However, these extracted features still suffer from many irrelevant and noisy features that restrict severely the performance of the approaches. In this paper, we propose a novel model based on Adversarial Networks and inspirited by the Shared-Private model (ANSP), which aims at reducing common, irrelevant features from the extracted features for information credibility evaluation. Specifically, ANSP involves two tasks: one is to prevent the binary classification of true and false information for capturing common features relying on adversarial networks guided by reinforcement learning. Another extracts credibility features (henceforth, private features) from multiple types of credibility information and compares with the common features through two strategies, i.e., orthogonality constraints and KL-divergence for making the private features more differential. Experiments first on two six-label LIAR and Weibo datasets demonstrate that ANSP achieves state-of-the-art performance, boosting the accuracy by 2.1%, 3.1%, respectively and then on four-label Twitter16 validate the robustness of the model with 1.8% performance improvements.

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.

Analysis and Evaluation of Information Credibility Based on Big Data

Analysis and Evaluation of Information Credibility Based on Big Data

Exploring the dual-role of cognitive heuristics and the moderating effect of gender in microblog information credibility evaluation

Purpose Although microblogs have become an important information source, the credibility of their postings is still a critical concern due to the open and unregulated nature. To understand the antecedents of microblog information credibility, the purpose of this paper is to investigate the dual-role of cognitive heuristics (i.e. the additivity and bias roles) and the effect of gender differences. Design/methodology/approach This study collected data via an online field survey of active microblog users, and a total of 204 valid responses was received. Findings This study demonstrates the dual-role of source credibility and vividness, the additivity role of microblog platform credibility, and the bias role of social endorsement. Furthermore, this study also found out gender difference that the additivity role of cognitive heuristics was stronger for men while bias role was stronger for women. Research limitations/implications This research enriches the microblog literature by examining the cognitive heuristic determinants as key predictors of microblog information credibility, and contributes to the information credibility literature by identifying and analyzing the dual-role effect of cognitive heuristics and corresponding gender differences. Practical implications This study can help organizations better manage their reputation, especially during the reputation crises, and also serves as a reminder to microblog platform operators of the importance of their microblog platform credibility. Social implications This study can help organizations better manage their reputation, especially during the reputation crises, and serves as a reminder to the microblog platform operators of the importance of their microblog platform credibility. Originality/value This study investigates the dual-role effect of cognitive heuristics (i.e. the additivity role and bias role) and corresponding gender differences that are less touched on before, and thus provides a more nuanced understanding of the more complex effects of cognitive heuristics.

Information Credibility Evaluation on Social Media

With the growing online social media, rumors are spread fast and viewed by more and more people on the Internet. Rumors bring significant harm to daily life and public security. It is crucial to evaluate the credibility of information and detect the rumors on social media automatically. In this work, we establish a Network Information Credibility Evaluation (NICE) platform, which collects a database of rumors that have been verified on Sina Weibo and automatically evaluates the information generated by users on social media but has not been verified. Users can use a query to search related information. If the according information appears in our database, users can identify it is a rumor immediately. Otherwise, NICE will show users with real-time results crawled automatically from social media and can calculate credibility of a specific result with our algorithm. Our algorithm learns dynamic representations for information on social media based on behavior information, dynamic information, user information and comment information. Then, we use an ordinary logistic regression to classify information into rumors and non-rumors. Based on our algorithm, NICE system achieves satisfactory performance on evaluating information credibility and detecting rumors on social media.

Utilization and Content Evaluation of Mobile Applications for Pregnancy, Birth, and Child Care.

ObjectivesThe purpose of this study was to explore the use of mobile applications about pregnancy, birth, and child care among pregnant women and to review the characteristics, contents, and credibility of the applications used by these women.MethodsThis study was cross-sectional and was conducted using a survey method. One hundred and ninety-three pregnant women participated in this study. The questionnaire was developed to examine the pattern and reasons for pregnancy-related application usage. The 47 mobile apps used by participants were reviewed and categorized based on functions and developers. The credibility of the information provided by the mobile applications was evaluated using a structured measurement.ResultsFifty-five percent of the participants were using mobile apps related to pregnancy, birth, and/or child care. First-time mothers used the apps significantly more often than women who were pregnant for the second time. Women who had used a smartphone for a longer period of time were more likely to use apps related to pregnancy, birth, and/or child care. The most frequently-used information concerned signs of risk and disease during pregnancy. Experts' quick opinions and Q&A formats related to diet and medication administration during pregnancy were the women's most cited need for content in applications. Information was the most common function of the apps. In the evaluation of information credibility, the 'information source' category had the lowest score.ConclusionsThe results showed that applications related to pregnancy, birth, and child care have become an important information source for pregnant women. To fulfill the needs of users, credible applications related to pregnancy, birth, and child care should be developed and managed by qualified healthcare professionals.

Evaluation of information credibility on the website and printed source search

Evaluation of information credibility on the website and printed source search