Fake news believability: The effects of political beliefs and espoused cultural values

Gupta, Michèle; Dennehy, Denis; Parra, Carlos M.; Mäntymäki, Matti; Dwivedi, Yogesh K.

doi:10.1016/j.im.2022.103745

Cited by 22 publications

(7 citation statements)

References 123 publications

(183 reference statements)

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“…Finally, we found that, generally, older participants showed better discrimination, and more right-wing participants showed worse discrimination. The effect of political orientation is consistent with the literature (Calvillo et al, 2020;Garrett & Bond, 2021;Gupta et al, 2023;, whereas the effect of age is consistent with some studies (Calvillo et al, 2020;Gupta et al, 2023) but not others . These mixed results can be attributed to the fact that demographic differences are largely item-and sample-specific and should be considered within the particular methodological context in which they occur (Calvillo et al, 2020;Calvillo, Rutchick, & Garcia, 2021;Halpern et al, 2019).…”

Section: Discussionsupporting

confidence: 91%

Wordless wisdom: The dominant role of tacit knowledge in true and fake news discrimination.

Modirrousta-Galian,

Higham,

Seabrooke

2024

Journal of Applied Research in Memory and Cognition

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We would like to thank Denis Drieghe and Guido Maiello for their reading of the manuscript and valuable suggestions.The data, analytic code, and materials needed to replicate this study are available on OSF (https://osf.io/qfe3g/). This study was preregistered (https://aspredicted.org/n7v2z.pdf).We have no conflicts of interest to disclose. Our work was funded by the Economic and Social Research Council South Coast Doctoral Training Partnership. The funding source had no other involvement other than financial support.

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Section: Discussionsupporting

confidence: 91%

Wordless wisdom: The dominant role of tacit knowledge in true and fake news discrimination.

Modirrousta-Galian,

Higham,

Seabrooke

2024

Journal of Applied Research in Memory and Cognition

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“…Finally, we found that, generally, older participants showed better discrimination, and more right-wing participants showed worse discrimination. The effect of political orientation is consistent with the literature (Calvillo et al, 2020;Calvillo et al, 2021a;Garrett & Bond, 2021;Gupta et al, 2023;, whereas the effect of age is consistent with some studies (Calvillo et al, 2020;Gupta et al, 2023) but not others . These mixed results can be attributed to the fact that demographic differences are largely item-and sample-specific and should be considered within the particular methodological context in which they occur (Calvillo et al, 2020;Calvillo et al, 2021b;Halpern et al, 2019).…”

Section: Discussionsupporting

confidence: 91%

Wordless Wisdom: The Dominant Role of Tacit Knowledge in True and Fake News Discrimination

Modirrousta-Galian¹,

Higham²,

Seabrooke³

2023

Preprint

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In this preregistered study, we investigated the type of knowledge people use to discriminate between true and fake news by asking participants (N = 327 Prolific users residing in the United States) to rate the veracity of different news headlines and indicate what decision strategy they used to make each rating (guess, intuition, familiarity, prior knowledge, rule, or other). We found that participants discriminated well between true and fake news headlines, and predominantly chose decision strategies that suggested they were using tacit knowledge (knowledge that is not easily articulated) rather than explicit knowledge (knowledge that is easily articulated). For example, guess and intuition were chosen 63% of the time, and participants’ discrimination was good even when they claimed to be guessing. The fact that tacit knowledge formed the dominant basis of participants’ discriminative ability speaks to the types of interventions that are likely to be successful in improving this skill.

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“…The rapid advancement of technology and the ubiquity of social media platforms have facilitated the spread of misinformation at an unprecedented rate, making it difficult for individuals to discern fact from fiction (Muhammed and Mathew, 2022 ). This has led to many negative consequences, including the erosion of trust in institutions (Nahum et al, 2021 ), the polarization of society (Gupta et al, 2023 ), and the hindrance of adequate response to crises such as the COVID-19 pandemic (Agley and Xiao, 2021 ).…”

Section: Introductionmentioning

confidence: 99%

A novel approach to fake news classification using LSTM-based deep learning models

Padalko,

Chomko,

Chumachenko

2024

Front. Big Data

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The rapid dissemination of information has been accompanied by the proliferation of fake news, posing significant challenges in discerning authentic news from fabricated narratives. This study addresses the urgent need for effective fake news detection mechanisms. The spread of fake news on digital platforms has necessitated the development of sophisticated tools for accurate detection and classification. Deep learning models, particularly Bi-LSTM and attention-based Bi-LSTM architectures, have shown promise in tackling this issue. This research utilized Bi-LSTM and attention-based Bi-LSTM models, integrating an attention mechanism to assess the significance of different parts of the input data. The models were trained on an 80% subset of the data and tested on the remaining 20%, employing comprehensive evaluation metrics including Recall, Precision, F1-Score, Accuracy, and Loss. Comparative analysis with existing models revealed the superior efficacy of the proposed architectures. The attention-based Bi-LSTM model demonstrated remarkable proficiency, outperforming other models in terms of accuracy (97.66%) and other key metrics. The study highlighted the potential of integrating advanced deep learning techniques in fake news detection. The proposed models set new standards in the field, offering effective tools for combating misinformation. Limitations such as data dependency, potential for overfitting, and language and context specificity were acknowledged. The research underscores the importance of leveraging cutting-edge deep learning methodologies, particularly attention mechanisms, in fake news identification. The innovative models presented pave the way for more robust solutions to counter misinformation, thereby preserving the veracity of digital information. Future research should focus on enhancing data diversity, model efficiency, and applicability across various languages and contexts.

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Fake news believability: The effects of political beliefs and espoused cultural values

Cited by 22 publications

References 123 publications

Wordless wisdom: The dominant role of tacit knowledge in true and fake news discrimination.

Wordless wisdom: The dominant role of tacit knowledge in true and fake news discrimination.

Wordless Wisdom: The Dominant Role of Tacit Knowledge in True and Fake News Discrimination

A novel approach to fake news classification using LSTM-based deep learning models

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