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Record W3157764960 · doi:10.18438/eblip29854

Incoming Undergraduate Students Struggle to Accurately Evaluate Legitimacy of Online News

2021· article· en· W3157764960 on OpenAlexvenueno aff
Sarah Schroeder

Bibliographic record

VenueEvidence Based Library and Information Practice · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicMisinformation and Its Impacts
Canadian institutionsnot available
Fundersnot available
KeywordsMisinformationCredibilitySocial mediaLikert scalePsychologyComputer sciencePolitical scienceWorld Wide Web

Abstract

fetched live from OpenAlex

A Review of: Evanson, C., & Sponsel, J. (2019). From syndication to misinformation: How undergraduate students engage with and evaluate digital news. Communications in Information Literacy, 13(2), 228-250. https://doi.org/10.15760/comminfolit.2019.13.2.6 Abstract Objective – To determine how new undergraduate students access, share, and evaluate the credibility of digital news. Design – Asynchronous online survey and activity. Setting – A small private, liberal arts college in the southeastern United States of America. Subjects – Participants included 511 incoming first-year college students. Methods – Using the Moodle Learning Management System, incoming first-year students completed a mandatory questionnaire that included multiple choice, Likert scale, open-ended, and true/false questions related to news consumption. Two questions asked students to identify which news sources and social networking sites they have used recently, and the next two questions asked students to define fake news and rate the degree to which fake news impacts them personally and the degree to which it impacts society. The end of the survey presented students with screenshots of three news stories and asked them to reflect on how they would evaluate the claim in the story, their confidence level in the claim, and whether or not they would share this news item on social media. The three items chosen represent certain situations that commonly cause confusion for news consumers: (a) a heading that does not match the text of the article, (b) a syndicated news story, and (c) an impostor URL and fake news story. Researchers coded the student responses using both preset and emergent codes. Main Results – Eighty-two percent of students reported using at least one social media site to access political news in the previous seven days. Students reported believing that fake news is a worrying trend for society, with 86% labelling it either a “moderate” or “extreme” barrier to society’s ability to recognize accurate information. However, they expressed less concern about their own ability to navigate an information environment in which fake news is prevalent, with 51% agreeing that it has only somewhat of an effect on their own ability to effectively navigate digital information. Of the three news items presented to them, students expressed the least confidence (an average of 1.55/4) and least interest in sharing (12%) the first news item, in which the heading does not match the text. However, only 14% of respondents noted this mismatch. In evaluations of the second item, an AP news item on the Breitbart website, 35% of students noted the website on which the article was found, but fewer noted that the original source is the Associated Press. Student responses to the third article, a fake news item from a website masquerading as an NBC website, show that 37% of students believed the source to come from a legitimate NBC source. Only 7% of students recognized the unusual URL, and 24% of respondents indicated that they might share this news item on social media. Conclusion – The study finds that impostor URLs and syndicated news items might confuse students into misevaluating the information before them, and that librarians and other instructors should raise awareness of these tactics.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.008
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.875
Threshold uncertainty score0.905

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.188
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.063
GPT teacher head0.400
Teacher spread0.337 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations1
Published2021
Admission routes1
Has abstractyes

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