Confirmation Bias in Seeking Climate Information: Employing Relative Search Volume to Predict Partisan Climate Opinions

Wang, Yifei; Jaidka, Kokil

doi:10.1177/08944393231160963

Cited by 2 publications

(1 citation statement)

References 104 publications

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“…Earlier research has shown that group members anticipate conformity to the norm of ingroup favoritism and out-group disparagement among their peers. Wang and Jaidka (2023) investigated whether search methods corresponded with the predicted confirmation biases of areas that adhere to different partisan convictions. Burgers et al (2023) concentrated on four communication biases: volume, balance, fairness and recipient engagement.…”

Section: Biasmentioning

confidence: 99%

Diving into the divide: a systematic review of cognitive bias-based polarization on social media

Xing,

Zhang,

Storey

et al. 2024

JEIM

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PurposeThe global prevalence of social media and its potential to cause polarization are highly debated and impactful. The previous literature often assumes that the ideological bias of any media outlet remains static and exogenous to the polarization process. By studying polarization as a whole from an ecosystem approach, the authors aim to identify policies and strategies that can help mitigate the adverse effects of polarization and promote healthier online discourse.Design/methodology/approachTo investigate online polarization, the authors perform a systematic review and analysis of approximately 400 research articles to explore the connection between cognitive bias and polarization, examining both causal and correlational evidence. The authors extensively evaluate and integrate existing research related to the correlation between online polarization and crucial factors such as public engagement, selective exposure and political democracy. From doing so, the authors then develop a PolarSphere ecosystem that captures and illustrates the process of online polarization formation.FindingsThe authors' review uncovers a wide range of associations, including ideological cognition, bias, public participation, misinformation and miscommunication, political democracy, echo chambers and selective exposure, heterogeneity and trust. Although the impact of bias on social media polarization depends on specific environments and internal/external conditions, certain variables exhibit strong associations across multiple contexts. The authors use these observations as a basis from which to construct PolarSphere, an ecosystem of bias-based polarization on social media, to theorize the process of polarization formation.Originality/valueBased on the PolarSphere ecosystem, the authors argue that it is crucial for governments and civil societies to maintain vigilance and invest in further research to gain a deep comprehension of how cognitive bias affects online polarization, which could lead to ways to eliminate polarization.

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Section: Biasmentioning

confidence: 99%