Publicación: Detecting Influencers in Social Media using information from their followers
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2020-03
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Atribución-NoComercial-SinDerivadas 4.0 Internacional
info:eu-repo/semantics/openAccess
info:eu-repo/semantics/openAccess
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Sociedad Española para el Procesamiento del Lenguaje Natural
Resumen
Dada la tarea de encontrar influencers en un dominio dado (i.e. banking) en una red social, en este artículo investigamos (i) la importancia de caracterizar a los seguidores para la detección automática de influencers; (ii) la manera más efectiva de combinar señales obtenidas de los seguidores y de los perfiles principales para la detección automática de influencers. En este trabajo, hemos modelado el discurso usado por los usuarios en dos dominios, automotive y banking, así como el lenguaje utilizado por los influencers en dichos dominios y por sus seguidores, y utilizamos estos Modelos de Lenguaje para estimar la probabilidad de ser un influencer. Nuestro mayor descubrimiento es que los influencers no sólo dependen de su conocimiento sobre el dominio sino del de sus seguidores; por lo tanto, cuanto mayor conocimiento y número de expertos haya entre sus seguidores, mayor será la probabilidad que el perfil sea de un influencer.
Given the task of finding influencers of a given domain (i.e. banking) in a social network, in this paper we investigate (i) the importance of characterizing followers for the automatic detection of influencers; (ii) the most effective way to combine signals obtained from followers and from the main profiles for the automatic detection of influencers. In this work, we have modeled the discourse used in two domains, banking and automotive, as well as the language used by the influencers in such domains and by their followers, and used these Language Models to estimate the probability of being influencer. Our most remarkable finding is that influencers not only depend on their expertise on the domain but also on that of their followers, so that the more knowledge and number of experts among their followers, the more probability of being influencer a profile has.
Given the task of finding influencers of a given domain (i.e. banking) in a social network, in this paper we investigate (i) the importance of characterizing followers for the automatic detection of influencers; (ii) the most effective way to combine signals obtained from followers and from the main profiles for the automatic detection of influencers. In this work, we have modeled the discourse used in two domains, banking and automotive, as well as the language used by the influencers in such domains and by their followers, and used these Language Models to estimate the probability of being influencer. Our most remarkable finding is that influencers not only depend on their expertise on the domain but also on that of their followers, so that the more knowledge and number of experts among their followers, the more probability of being influencer a profile has.
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Categorías UNESCO
Palabras clave
Learning to Rank, Web and social media search, Information extraction, Social Network Analysis, Natural Language Processing, Social Media Influencers, Búsqueda Web y en Medios Sociales, Extracción de Información, Análisis de Redes Sociales, Procesamiento de Lenguaje Natural, Influencers en Redes Sociales
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Centro
E.T.S. de Ingeniería Informática
Departamento
Lenguajes y Sistemas Informáticos