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Cyborgs for strategic communication on social media (preprint)
arxiv; 2024.
Preprint en Inglés | PREPRINT-ARXIV | ID: ppzbmed-2401.06582v1
ABSTRACT
Social media platforms are a key ground of information consumption and dissemination. Key figures like politicians, celebrities and activists have leveraged on its wide user base for strategic communication. Strategic communications, or StratCom, is the deliberate act of information creation and distribution. Its techniques are used by these key figures for establishing their brand and amplifying their messages. Automated scripts are used on top of personal touches to quickly and effectively perform these tasks. The combination of automation and manual online posting creates a Cyborg social media profile, which is a hybrid between bot and human. In this study, we establish a quantitative definition for a Cyborg account, which is an account that are detected as bots in one time window, and identified as humans in another. This definition makes use of frequent changes of bot classification labels and large differences in bot likelihood scores to identify Cyborgs. We perform a large-scale analysis across over 3.1 million users from Twitter collected from two key events, the 2020 Coronavirus pandemic and 2020 US Elections. We extract Cyborgs from two datasets and employ tools from network science, natural language processing and manual annotation to characterize Cyborg accounts. Our analyses identify Cyborg accounts are mostly constructed for strategic communication uses, have a strong duality in their bot/human classification and are tactically positioned in the social media network, aiding these accounts to promote their desired content. Cyborgs are also discovered to have long online lives, indicating their ability to evade bot detectors, or the graciousness of platforms to allow their operations.

Texto completo: Disponible Colección: Preprints Base de datos: PREPRINT-ARXIV Idioma: Inglés Año: 2024 Tipo del documento: Preprint

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Texto completo: Disponible Colección: Preprints Base de datos: PREPRINT-ARXIV Idioma: Inglés Año: 2024 Tipo del documento: Preprint