Humans and Algorithms Detecting Fake News: Effects of Individual and Contextual Confidence on Trust in Algorithmic Advice

Snijders, Chris; Conijn, Rianne; Fouw, Evie de; Berlo, Kilian van

doi:10.1080/10447318.2022.2097601

Cited by 12 publications

(3 citation statements)

References 41 publications

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“…Dabbous et al (2022) found through surveys that cultural background is an important factor affecting users’ fake news detection and trust. Snijders et al (2023) found through comparative experiments that confidence levels significantly affect users’ trust in the authenticity of information.…”

Section: Data Analysis Resultsmentioning

confidence: 99%

Advancements in Rumor Detection Research Based on Bibliometrics and S-curve Technology Evolution Theory

Zhao,

Liu,

Zhang

et al. 2023

Sage Open

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With the rapid development of social media, new opportunities have been provided for the generation and dissemination of online rumors, making systematic study of rumor detection of great significance for the control and governance of internet rumors. Addressing the limitations of past review studies on rumor detection which were characterized by a single perspective, reliance on subjective judgment, and lack of technological evolution theory, this paper reviews 983 rumor detection articles in the SCI-EXPANDED, SSCI, CPCI-S, CPCI-SSH, CCR-EXPANDED, and IC databases of the Web of Science. Utilizing Citespace and VOSviewer for visual analysis of the articles, and adopting bibliometric theories like network analysis and topic evolution analysis, this study identifies core research groups in the field of rumor detection based on author collaboration network and institution collaboration network. Through high-frequency keyword clustering graph and keyword co-occurrence graph, the study unveils topic associations and cluster structures among keywords, explicates research hotspots in the field of rumor detection, and conducts a fine-grained critical comparative analysis. According to keyword time graph, keyword bursts table, and trends in the number of publications in hot technology fields, combined with the S-curve technology evolution theory, the study discerns the life cycle and research trends of technologies in the rumor detection field. Compared to existing literature reviews, this paper is the first to propose integrating bibliometrics and S-curve technology evolution theory to reveal the state of relevant technologies and research frontiers.

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Section: Data Analysis Resultsmentioning

confidence: 99%

Advancements in Rumor Detection Research Based on Bibliometrics and S-curve Technology Evolution Theory

Zhao,

Liu,

Zhang

et al. 2023

Sage Open

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“…Reliance and trust may be affected by how much is known about the system and its performance: There are different ways in which humans can learn about the accuracy of a system. For example, system accuracy can be stated before or during an interaction [62]. It can also be that individuals can only deduce accuracy by interacting with a system and learning over time [20].…”

Section: Effects Of Ai Failure Over Time On Reliance and Trustmentioning

confidence: 99%

The Trust Recovery Journey. The Effect of Timing of Errors on the Willingness to Follow AI Advice.

Kahr,

Rooks,

Snijders

et al. 2024

Preprint

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Complementing human decision-making with AI advice offers substantial advantages. However, humans do not always trust AI advice appropriately and are overly sensitive to incidental AI errors, even in cases with overall good performance. Today’s research still needs to uncover the underlying aspects of trust decline and recovery over time in repeated human-AI interactions. Our work investigates the consequences of incidental AI error on (self-reported) trust and participants’ reliance on AI advice. Results from our experiment, where 208 participants evaluated 14 legal cases before and after receiving algorithmic advice, showed that trust significantly decreased after early and late errors but was rapidly restored in both scenarios. Reliance significantly dropped only for early errors but not for late errors. In both scenarios, reliance was able to be restored. Results suggest that late (compared to early) errors are less drastic in trust loss and allow quicker recovery. These findings align with an interpretation in which humans can build up trust over time if a system is performing well, making them more tolerant of incidental AI errors.

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“…Constant promotion of these products across numerous SM platforms contributes to their perceived authority [7]. Rapid technological advancements and data accessibility have significantly increased the effectiveness and precision of algorithms to combat fake news [8].…”

Section: Introductionmentioning

confidence: 99%

Social Media and Dental Practitioner's Knowledge of Misinformation, Infodemic and Fact-Checking on Dental Information

Naik,

Devang Divakar,

Jhugroo

2023

TIJPH

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Social media is currently among the most popular web activities among dental professionals. The study aimed to assess social media usage, knowledge about misinformation, infodemic and fact-checking among dental professionals on dental information. The current study design was a descriptive cross-sectional study conducted among dental professionals from Riyadh City, Saudi Arabia. The questionnaire comprised demographic data of study participants and knowledge about misinformation, infodemic and fact-checking about social media use for their dental profession. Descriptive statistics were used for analysis. A total of 1192 dental practitioners responded to the questionnaire. In our study, most participating dentists were young and had 1-5 years of experience (51%). Most participants responded that sometimes (49%), they fact-check social media content about dental information. The google is the most used to check the fact content. About 90% of dentists never received notification about factual errors. The knowledge about misinformation and fact-checking is moderate among dental practitioners. Keywords: Dental practitioner, Dentists, Descriptive study, Fact-checking, Misinformation, social media.

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Humans and Algorithms Detecting Fake News: Effects of Individual and Contextual Confidence on Trust in Algorithmic Advice

Cited by 12 publications

References 41 publications

Advancements in Rumor Detection Research Based on Bibliometrics and S-curve Technology Evolution Theory

Advancements in Rumor Detection Research Based on Bibliometrics and S-curve Technology Evolution Theory

The Trust Recovery Journey. The Effect of Timing of Errors on the Willingness to Follow AI Advice.

Social Media and Dental Practitioner's Knowledge of Misinformation, Infodemic and Fact-Checking on Dental Information

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