Semi-supervised Content-based Detection of Misinformation via Tensor Embeddings

Guacho, Gisel Bastidas; Abdali, Sara; Shah, Neil P.; Papalexakis, Evangelos E.

doi:10.48550/arxiv.1804.09088

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2023

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“…Often contradicts experts (EAVI, 2018). Promotes metaphysics, naturalistic fallacies, and other (Guacho et al, 2018). The actors hijack scientific legitimacy for profit or fame (Forstorp, 2005).…”

Section: Nomentioning

confidence: 99%

Nuevos agentes de desinformación masiva. Análisis de las publicaciones de los influencers políticos españoles en Instagram

Palacios López,

Bonete Vizcaíno,

Gelado Marcos

2023

Estud. mensaje periodís.

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Social media has impacted the way content is both produced and consumed at a mass scale. Influencers have gained, in this new scenario, a notable ascendence over their large audiences; and, though there have been several attempts at analyzing their role, many have lacked systematicity (Riedl et al., 2021). This research aims to shed light on two main research goals: determining whether political influencers disinform; and, if so, detecting possible patterns in the disinforming content that reaches their audiences. In order to do so, the most relevant political influencers were identified, and, subsequently, the contents produced by them were analyzed using the taxonomy on disinformation proposed by Kapantai et al. (2020). Findings show that influencers play a key role in disseminating disinformation on Instagram, since the vast majority of the accounts analyzed (92%) do generate this type of content, which have a large impact on young audiences

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Section: Nomentioning

confidence: 99%