The DOI matches its Crossref record, with a small difference in title or year; 107 of the 113 references it cites were found in a registry or an index.
Epistemology in the Era of Fake News: An Exploration of Information Verification Behaviors among Social Networking Site Users
Abstract
Fake news has recently garnered increased attention across the world. Digital collaboration technologies now enable individuals to share information at unprecedented rates to advance their own ideologies. Much of this sharing occurs via social networking sites (SNSs), whose members may choose to share information without consideration for its authenticity. This research advances our understanding of information verification behaviors among SNS users in the context of fake news. Grounded in literature on the epistemology of testimony and theoretical perspectives on trust, we develop a news verification behavior research model and test six hypotheses with a survey of active SNS users. The empirical results confirm the significance of all proposed hypotheses. Perceptions of news sharers' network (perceived cognitive homogeneity, social tie variety, and trust), perceptions of news authors (fake news awareness and perceived media credibility), and innate intentions to share all influence information verification behaviors among SNS members. Theoretical implications, as well as implications for SNS users and designers, are presented in the light of these findings.
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“At an individual level, a variety of social ties-defined as the diversity of offline groups and contexts represented in one's online social networks (Torres et al., 2018)-can help increase awareness of fake content.”
From Deepfakes: Deceptions, mitigations, and opportunities (2023) · cites this paper as Torres et al., 2018
In the citing paper
“Additionally, the study indicates that increasing consumer awareness of fake content, such as deepfakes, has a beneficial effect on verification behavior and network trust (Torres et al., 2018).”
From Deepfakes: Deceptions, mitigations, and opportunities (2023) · cites this paper as Torres et al., 2018
In the citing paper
“Addressing the surge of algorithm-generated misinformation opens up a new field of business for developing AI-based solutions and services that detect synthetic content from human-generated content and provide consumers with warnings when confronted with marketplace deception or suspicious content (Maksutov et al., 2020; Torres et al., 2018; Zotov et al., 2020).”
From Deepfakes: Deceptions, mitigations, and opportunities (2023) · cites this paper as Torres et al., 2018
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Metadata
- Authors
- Torres, R., Gerhart, N., & Negahban, A.
- Affiliations
- University of North Texas (US)
- Creighton University (US)
- California State University, Chico (US)
- Venue
- ACM SIGMIS Database: the DATABASE for Advances in Information Systems
- Published
- 2018-07-25
- Volume
- 49
- Issue
- 3
- Pages
- 78-97
- ISSN
- 0095-0033 (ISSN-L)
- 1532-0936
- OpenAlex
- W2883396905
- Cited by
- 113
- References
- 113
- Citations by year
- 2026: 15 · 2025: 13 · 2024: 17 · 2023: 14 · 2022: 19 · 2021: 15 · 2020: 14 · 2019: 6
- FWCI
- 19.91
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- ACM SIGMIS Database the DATABASE for Advances in Information Systems (publishedVersion)
- Topics
- Social Sciences › Sociology and Political Science › Misinformation and Its Impacts
- Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › Complex Network Analysis Techniques
- Social Sciences › Sociology and Political Science › Islamic Finance and Communication
- Keywords
- fake news, fact-checking, social networking sites, epistemology of testimony, trust, epistemic trust, news sharing
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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