How live Twitter commentaries by professional sports clubs can reveal intergroup dynamics

Burgers, Christian; Beukeboom, Camiel J.; Smith, Pamela A.L.; Biemen, Tammie van

doi:10.1016/j.chb.2022.107528

Cited by 4 publications

(2 citation statements)

References 39 publications

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“…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. The results showed that teams tend to allocate more of their feed to positive events related to the in-group than to the out-group.…”

Section: Biasmentioning

confidence: 99%

“…Potential research directions Psychology of cognitive biases on user perception (Andreotta et al, 2022;Barbera et al, 2015;Beck and Clark, 1997;De Zuniga et al, 2022;Lee, 2022;Messing and Westwood, 2012) User psychological profiles and perceptions of controversial issues Impact of selective exposure on polarization Emotional reactions to the event Cognitive de-biasing techniques development In-group and out-group affiliation Changes in user behavior Distorted view of reality, reinforcing stereotypes and deepening polarization Technical aspects of quantifying polarization (Barrett et al, 2021;Burgers et al, 2023;Grover et al, 2019;Lu et al, 2022;Modgil et al, 2021;Park, 2021;Tornberg, 2021) Influence of cognitive biases on people's reading and sharing behavior Fact-checking tools Polarization's impact on people's mental health Interventions to recognize and counteract biases Development and spreading of biases in digital communities AI and machine learning in studying bias-based polarization Inventions include mindfulness, cognitive behavioral therapy techniques, or metacognitive strategies Information system (IS) perspective on polarization (Allcott and Gentzkow, 2017, Au et al, 2021, Barfar, 2019Kaiser et al, 2022, Miller et al, 2022Vicario et al, 2019;Wang, 2015) Designing intelligent systems to mitigate polarization User interaction with personalized content The role of misinformation Longitudinal effects Evaluation of existing efforts The implications of decentralized social networks Comparative study across different platforms Management strategies on polarization (Feezell et al, 2021;Guan et al, 2023;Klein and Robison, 2019;Medaglia and Zhu, 2017) Reputation of organization in a polarized environment Education and media literacy Ethical considerations Crisis communication and response strategies for potential bias-based backlash Engagement management for balance among communities Internal communication and training to understand and recognize bias Content moderation and guidelines Source(s): Authors' own creation Table…”

Section: Perspectivesmentioning

confidence: 99%

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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%