Evaluation of Elements of a Prospective System to Alert Users to Intentionally Deceptive Content

Spradling, Matthew; Straub, Jeremy

doi:10.1109/csci51800.2020.00045

Cited by 2 publications

(1 citation statement)

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“…They found that overall the labels decreased the likelihood of a reader sharing a story and increased fake news recognition, although people's trust in the labels themselves were low. Previous work also investigated the benefits and limitations of different types of labels (i.e., blocking, intermediate, content warnings) [27]. This paper goes beyond this prior work by evaluating which labels and presentations enhance recognition and thus may decrease the spread of fake news and misinformation.…”

Section: Basic Warning Labelsmentioning

confidence: 99%

University Community Members’ Perceptions of Labels for Online Media

et al. 2021

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Fake news is prevalent in society. A variety of methods have been used in an attempt to mitigate the spread of misinformation and fake news ranging from using machine learning to detect fake news to paying fact checkers to manually fact check media to ensure its accuracy. In this paper, three studies were conducted at two universities with different regional demographic characteristics to gain a better understanding of respondents’ perception of online media labeling techniques. The first study deals with what fields should appear on a media label. The second study looks into what types of informative labels respondents would use. The third focuses on blocking type labels. Participants’ perceptions, preferences, and results are analyzed by their demographic characteristics.

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Section: Basic Warning Labelsmentioning

confidence: 99%

University Community Members’ Perceptions of Labels for Online Media

et al. 2021

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"I'm Constantly in This Dilemma": How Migrant Technology Professionals Perceive Social Media Recommendation Algorithms

Pyle,

Zhang,

Haimson

et al. 2024

Proc. ACM Hum.-Comput. Interact.

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Migrants experience unique needs and use social media, in part, to address them. While prior work has primarily focused on migrant populations who are vulnerable socio-economically and legally, less is known about how highly educated migrant populations use social media. Additionally, a growing body of work focuses on algorithmic perceptions and resistance, primarily from laypersons' perspectives rather than people with high degrees of algorithmic literacy. To address these gaps, we draw from interviews with 20 Chinese-born migrant technology professionals. We found that social media played an integral role in helping participants meet their unique needs but that participants perceived social media algorithms to negatively shape the content they consumed, which ultimately influenced their mobility-related aspirations and goals. We discuss how findings challenge the promise of algorithmic literacy and contribute to a human-centered conceptualization of algorithmic mobility as socially and algorithmically produced motion that concerns the movement of physical bodies and interactions as well as associated digital movement. Specifically, we introduce a fourth dimension of algorithmic mobility: algorithmically curated content on social media and elsewhere based on facets of users' identities directly influences users' mobility-related aspirations and goals, such as how, when, and where they go. Finally, we call for transnational policy interventions related to algorithms and highlight design considerations around content moderation, algorithmic user-control, and contestability.

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Evaluation of Elements of a Prospective System to Alert Users to Intentionally Deceptive Content

Cited by 2 publications

References 13 publications

University Community Members’ Perceptions of Labels for Online Media

University Community Members’ Perceptions of Labels for Online Media

"I'm Constantly in This Dilemma": How Migrant Technology Professionals Perceive Social Media Recommendation Algorithms

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