Social media platforms and content exposure: How to restore users’ control

Stasi, Maria Luisa

doi:10.1177/1783591719847545

Cited by 6 publications

(6 citation statements)

References 17 publications

(13 reference statements)

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“…Nevertheless, those who chose “moderate” attitudes regarding vaccination are likely the main target of anti-vaccine propaganda, which can further strengthen the self-image of opponents and undermine the confidence among supporters. At present, this can be executed through online social media, which have become the main channel for people to communicate, share content, and access information [ 45 ]. The algorithms behind these platforms can amplify the controversial, often scientifically unsupported content, as such a strategy increases user engagement [ 46 ].…”

Section: Discussionmentioning

confidence: 99%

Impact of the COVID-19 Pandemic on Attitudes toward Vaccination: Representative Study of Polish Society

2023

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The study explored the association between individuals’ attitudes toward vaccination and their actual vaccination behavior. We also examined the impact of the coronavirus disease 2019 (COVID-19) pandemic and the ongoing vaccination debate on changing attitudes towards vaccination, specifically within different demographic groups. The survey was conducted among a representative sample of Poles (N = 805) using computer-assisted web interview (CAWI) technology. As demonstrated, those who identified themselves as strong vaccine supporters were statistically significantly more frequently to be vaccinated with COVID-19 booster doses, to follow a physician’s recommendation on any vaccine without hesitation, and to be strengthened in their confidence in vaccines during the COVID-19 pandemic (p < 0.001 for all). However, over half of the responders declared themselves as moderate vaccine supporters/opponents, the groups whose further attitudes are likely to be affected by (mis)communication. Importantly, more than half of moderate vaccine supporters declared that their vaccine confidence was weakened during the COVID-19 pandemic, while 43% were not vaccinated against COVID-19. In addition, the study demonstrated that older and better-educated individuals were more likely to be COVID-19-vaccinated (p < 0.001 and p = 0.013, respectively). The results of this study imply that, in order to improve vaccine acceptance, it is essential to strengthen public health communication and avoid communication errors conducted during the COVID-19 pandemic.

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Section: Discussionmentioning

confidence: 99%

Impact of the COVID-19 Pandemic on Attitudes toward Vaccination: Representative Study of Polish Society

2023

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“…Social media platforms have become the main channel for people to communicate, access information, and share content (Stasi, 2019). With the emergence of the citizens' ability to produce content, citizen journalism has become an alternative to content produced by mainstream media, especially after the presence of social media (Nuswantari & Alyasuci, 2023).…”

Section: Citizen Videos Are Growing In Line Withmentioning

confidence: 99%

Advocating transmigrants through videos on the YouTube channel Asian Survivor

Sirait,

Abdullah

2024

PTVF

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Background: Revival Rival shares videos through the YouTube channel “Asian Survival.” The video is about the situation in the latest transmigrant location in Tanjung Buka Settlement Unit (SP) 10, Tanjung Palas Tengah District, Bulungan Regency, North Kalimantan. He practiced citizen journalism by raising topics that were not touched by the mainstream mass media. Purpose: This research aims to discover how Asian Survivor conducts advocacy through its citizen journalism video content. Methods: This is descriptive research using several data collection techniques, namely content analysis, interviews, and literature studies. Results: The Asian Survivor advocacy is carried out through the production of video content consisting of four topics, namely, donation distribution, human interest, actual information, and inspiring profile. In addition, Revival Rival manages donations from viewers touched by the news shared by maintaining accountability through a personalized reporting process. Conclusion: Asian Survivor is an advocacy media for new transmigrants in Tanjung Buka SP 10 through a variety of video content presented as a picture of the life struggles of new transmigrants in marginalized areas; social criticism presented implicitly to maintain media sustainability and to raise donations from netizens sent to help the lives of transmigrants. Raising netizen donations for charitable activities is a form of social media monetization. Implications: This research has implications for further research on the power of utilizing YouTube channels as advocacy media based on citizen journalism. Another research development is related to the ethical issues of raising donations made by YouTube content as another form of monetization of social media accounts.

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“…However, depending on how power is distributed between the algorithm and the user, personalised technologies have the potential to improve on self-determination and autonomy, by promoting a sense of origin and thus transforming the technology to 'my technology' [176, p.1]. The benefits of more user control in content moderation technologies have also been noted [207]. Personalisation can centre the end-user in the designation of model behaviours, allowing them to exert more control over their interactions [33], and become a "perceived locus of casuality" [176, p.5].…”

Section: Ib3 Autonomymentioning

confidence: 99%

Personalisation within bounds: A risk taxonomy and policy framework for the alignment of large language models with personalised feedback

Kirk¹,

Vidgen²,

Röttger³

et al. 2023

Preprint

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Large language models (LLMs) are used to generate content for an increasingly wide range of tasks, and are set to reach a growing audience in coming years due to integration in product interfaces like ChatGPT or search engines like Bing. This intensifies the need to ensure that models are aligned with human preferences and do not produce unsafe, inaccurate or toxic outputs. While alignment techniques like reinforcement learning with human feedback (RLHF) and red-teaming can mitigate some safety concerns and improve model capabilities, it is unlikely that an aggregate fine-tuning process can adequately represent the full range of users' preferences and values. Different people may legitimately disagree on their preferences for language and conversational norms, as well as on values or ideologies which guide their communication. Personalising LLMs through micro-level preference learning processes may result in models that are better aligned with each user. However, there are several normative challenges in defining the bounds of a societally-acceptable and safe degree of personalisation. In this paper, we ask how, and in what ways, LLMs should be personalised. First, we review literature on current paradigms for aligning LLMs with human feedback, and identify issues including (i) a lack of clarity regarding what alignment means; (ii) a tendency of technology providers to prescribe definitions of inherently subjective preferences and values; and (iii) a "tyranny of the crowdworker", exacerbated by a lack of documentation in who we are really aligning to. Second, we present a taxonomy of benefits and risks associated with personalised LLMs, for individuals and society at large. Finally, we propose a three-tiered policy framework that allows users to experience the benefits of personalised alignment, while restraining unsafe and undesirable LLM-behaviours within (supra-)national and organisational bounds.

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Social media platforms and content exposure: How to restore users’ control

Cited by 6 publications

References 17 publications

Impact of the COVID-19 Pandemic on Attitudes toward Vaccination: Representative Study of Polish Society

Impact of the COVID-19 Pandemic on Attitudes toward Vaccination: Representative Study of Polish Society

Advocating transmigrants through videos on the YouTube channel Asian Survivor

Personalisation within bounds: A risk taxonomy and policy framework for the alignment of large language models with personalised feedback

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