Perceptions of the information quality of internet and traditional text sources among Chinese users of the internet

Klein, Barbara; Guo, Yi; Zhou, Chunyue

doi:10.1504/ijiq.2011.040670

Cited by 7 publications

(10 citation statements)

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“…Drawing from examples in the literature (Reavley et al, 2012;Sciberras et al, 2010;Weitzel et al, 2012), a source scoring system was created to account for the variation of quality of sources-assigning the numbers one through five for each individual source. This metric was based upon the accuracy, objectivity, reputation, ease of understanding of the source and ease of accessing it (Klein et al, 2011). As an example, if participants consulted an Internet chat forum and received information from anonymous respondents whose qualifications are unable to be vetted, they would receive a score of two.…”

Section: Discussionmentioning

confidence: 99%

Health information-seeking behavior in the time of COVID-19: information horizons methodology to decipher source path during a global pandemic

Zimmerman

2021

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PurposeTo determine the differences, as represented by information horizons mapping, in the health information-seeking behavior from a group of participants between March 2019 and April 2020 of the novel coronavirus pandemic.Design/methodology/approachIn March 2019, the author conducted a study on health information-seeking behavior in which 149 participants drew information horizons maps in a health-related context. They also took health and information literacy assessments. This exact study was replicated using the same population in April 2020 to determine the differences in what the participants drew on their maps and how these changes interacted with their health and information literacy, their age and their education.FindingsThere is a statistically significant difference in the increased number of sources and the ranked quality of the sources that people used during the pandemic. Participants were much more likely to use credible sources and news sources, especially if they were older, more educated and had higher literacy levels – both health and information. They also relied heavily on social media. The participant group in the pandemic had a much heavier reliance on sources that are often used in a passive encountering way but engaging with them in an active information-seeking manner. The health information-seeking behavior in this study did not adhere to other research that found issue with information overload, avoidance and cyberchondria in response to crisis situations.Originality/valueThis article utilizes information horizons methodology to explore pre- and post-pandemic information-seeking. It is completely unique in this approach.

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Section: Discussionmentioning

confidence: 99%

Health information-seeking behavior in the time of COVID-19: information horizons methodology to decipher source path during a global pandemic

Zimmerman

2021

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“…If the data is of high quality, it can be perfect for the intended use. However, data quality is affected by many factors such as accuracy, completeness, consistency, timeliness, credibility, and interpretability of data (Klein, 2001). An important and di cult issue in processing experimental data is how to eliminate the in uence of human subjective factors and the interference of non-stationary data factors in data preprocessing, accurately nd the representative value data at each measurement point and reduce the measurement error of the system.…”

Section: Data Preprocessingmentioning

confidence: 99%

Predicting fertilizer concentration for precision irrigation under mixed variable-rate fertigation using machine learning: a case study of combined fertilization with dipotassium hydrogen phosphate and potassium chloride

Xiong

et al. 2023

Preprint

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Variable precise fertigation is critical to precision irrigation. The question of how to monitor the combination of fertilizer concentration and variable irrigation components as accurately as possible is challenging. The primary goal of this study is to develop accurate prediction models integrated with machine learning (ML) to predict the concentration of each type of fertilizers in mixed variable-rate fertigation used for precision irrigation. First, the feasibility of predicting of fertilizer concentration by monitoring physical parameters such as electrical conductivity (EC), acidity (pH) and temperature in mixed variable-rate fertigation was confirmed. 11 selected ML algorithms were applied to develop regression models that can accurately predict each fertilizer concentration of the mixed fertilizer compared to the classical multivariate linear regression (MLR). In addition, cubic spline interpolation (CSI) was used to densify the data sets, and K-fold cross-validation was employed to fairly evaluate the generalization ability (GA) of these models. The statistical and diagnostic analyzes revealed the superiority of ML especially SVM, KNN, ETs, and MLP over MLR in predicting each type of fertilizer concentration in mixed variable-rate fertigation with an R2 range of 0.9499 ~ 0.9970 and an RMSE range of 0.0852 ~ 0.4434 g/L, better than MLR with an R2 range of 0.8544 ~ 0.9425 and an RMSE range of 0.3752 ~ 0.7559 g/L. Moreover, the contribution of CSI to the modeling accuracy was confirmed, but the sensitivity of the models to EC and pH increased with the data from CSI and the tuning of the model hyper-parameter. Overall, the feasibility and performance of the ML models for predicting mixed fertilizer concentration by monitoring temperature, EC, and pH indicate that the presented ML models have significant application potential for irrigation and fertilization monitoring management of mixed variable-rate fertigation in precision irrigation with high-precision sensor technology.

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“…Drawing from these examples in the literature, a source scoring system was created assigning the numbers one through five for each individual source. This metric was based upon the accuracy, objectivity, reputation and ease of understanding of the source (Klein et al, 2011). Additionally, the level of expertise in accessing the source was factored into the score of each resource.…”

Section: Mapping Literaciesmentioning

confidence: 99%

Mapping literacies

Zimmerman

2019

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Purpose Information literacy and health literacy skills are positively correlated with indicators of quality of life. Assessing these literacies, however, can be daunting – particularly with people that may not respond well to prose-based tools. The purpose of this paper is to use information horizons methodology as a metric that may be reflective of literacies. Design/methodology/approach Following a power analysis to insure statistical significance, a sample of 161 participants was recruited from a university population and given formal, vetted measures of information literacy and health literacy and then was asked to create an information horizons map within a health-related context. The information horizons maps were evaluated in two different ways. First, the number of sources was counted. Then, the quality of sources was factored in. Multiple regression analysis was applied to both metrics as independent variables with the other assessments as dependent variables. Anker, Reinhart, and Feeley’s model provided the conceptual framework for the study. Findings Information horizons mapping was not found to have a significant relationship with measures of information literacy. However, there were strong, statistically significant relationships with the measures of health literacy employed in this study. Originality/value Employing information horizons methodology as a means of providing a metric to assess literacies may be helpful in providing a more complete picture of a person’s abilities. While the current assessment tools have value, this method has the potential to provide important information about the health literacy of people who are not traditionally well represented by prose-based measures.

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Perceptions of the information quality of internet and traditional text sources among Chinese users of the internet

Cited by 7 publications

References 0 publications

Health information-seeking behavior in the time of COVID-19: information horizons methodology to decipher source path during a global pandemic

Health information-seeking behavior in the time of COVID-19: information horizons methodology to decipher source path during a global pandemic

Predicting fertilizer concentration for precision irrigation under mixed variable-rate fertigation using machine learning: a case study of combined fertilization with dipotassium hydrogen phosphate and potassium chloride

Mapping literacies

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