Trends in Social Media: Persistence and Decay

Asur, Sitaram; Huberman, Bernardo A.; Szabó, Gábor; Wang, Chunyan

doi:10.2139/ssrn.1755748

Cited by 262 publications

(220 citation statements)

References 13 publications

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“…However, in order to restore the original topic sequence, it is important to know the exact shape of srf (t). To model the shape of srf (t), we propose to employ a family of normalized decaying functions (Asur et al 2011) demonstrated in the following equations:…”

Section: Modelling Impacting Topicsmentioning

confidence: 99%

Topic detection and tracking on heterogeneous information

et al. 2017

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Given the proliferation of social media and the abundance of news feeds, a substantial amount of real-time content is distributed through disparate sources, which makes it increasingly difficult to glean and distill useful information. Although combining heterogeneous sources for topic detection has gained attention from several research communities, most of them fail to consider the interaction among different sources and their intertwined temporal dynamics. To address this concern, we studied the dynamics of topics from heterogeneous sources by exploiting both their individual properties (including temporal features) and their inter-relationships. We first implemented a heterogeneous topic model that enables topic-topic correspondence between the sources by iteratively updating its topic-word distribution. To capture temporal dynamics, the topics are then correlated with a time-dependent function that can characterise its social response and popularity over time. We extensively evaluate the proposed approach and compare to the state-of-the-art techniques on heterogeneous collection. Experimental results demonstrate that our approach can significantly outperform the existing ones.

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Section: Modelling Impacting Topicsmentioning

confidence: 99%

Topic detection and tracking on heterogeneous information

et al. 2017

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“…For that reason, a ghost followers industry exists for the sole purpose of increasing social proof on social media [27]. However, Sitaram Asur et al [28] empirically discovered that number of followers do not contribute to trend creation. Similarly, Thackeray et al [29] found that 'liking' the page does not result in purchasing the product.…”

Section: Social Proofmentioning

confidence: 99%

Social proof in social media shopping: An experimental design research

Talib¹,

Saat²

2017

SHS Web Conf.

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Abstract. The practices of social proof techniques to attract consumers to shop on social media have proliferated over time and been used extensively. The reviewed literature uncovers that social community recommendation, customers' ratings and reviews, celebrity's endorser and numbers of likes, affect consumers' purchasing decisions. However, the effect of different types of social proof techniques on purchasing intention is unknown. This study empirically compares the effect of number of followers, celebrity endorser and social community recommendation on consumers' purchasing intention. An experiment has been conducted and the results reveal that the consumers' purchasing intention differs between groups. Further analysis discovers that the impact on consumers' purchasing intention is different between high number of followers and low number of followers, and between having social community recommendation and not having social community recommendation. Though, the impact of these two techniques is equal wherein no technique is superior to other. In order to gain purchasing engagement and boost online sales, online businesses on social media are encouraged to use the power of social proof technique, either by increasing the number of followers or providing more social community recommendations.

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“…El comportamiento de los usuarios que hace de un tuit o un tema se convierta en trending topic se basa más en el contenido que no en los atributos del usuario (Asur, Szabo, Huberman & Wang, 2011).…”

Section: Patrones De éXito En Redes Socialesunclassified

Factores de éxito de las comunidades virtuales universitarias basadas en redes sociales. Análisis de XarFED

et al. 2016

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Resumen. El presente artículo analiza los factores de éxito de una pionera comunidad virtual universitaria basada en redes sociales, XarFED, que une en un solo espacio a profesores, alumnos y ex-alumnos de la Facultad de Educación de la Universidad de las Islas Baleares (UIB). Este nuevo espacio pretende ser el encuentro entre los entornos formales, no formales e informales de aprendizaje de los participantes. La revisión teórica, además de centrarse en el análisis de los factores que la investigación hasta la fecha ha observado como elementos clave para el éxito de las comunidades virtuales, aporta una nueva visión a partir de otros aspectos analizados en el contexto de las redes sociales. La recogida de datos se lleva a cabo a través de un cuestionario, diseñado a partir de un modelo de factores críticos de éxito. Los resultados obtenidos muestran una incipiente actividad participativa y colaborativa así como las percepciones positivas por parte de los usuarios. Las conclusiones plantean reflexiones para la práctica e interesantes líneas de investigación basadas en las potencialidades de las redes sociales para la creación de comunidades virtuales, así como la necesidad de responder a las demandas generadas por los nuevos entornos abiertos de aprendizaje. Palabras clave: comunidad virtual; redes sociales; entorno de aprendizaje abierto; Educación Superior; Universidad; entornos virtuales.[en] Success factors of virtual university communities based on social networks. Analysis of XarFED Abstract. The article analyses the elements of success factors in a new virtual university community called XarFED, which has been built based on social networks. Its keep connected lecturers, students and former students of the School of Education of the University of the Balearic Islands (UIB). The aim of this new space is to become a meeting point between the formal, non-formal and informal _____________

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Trends in Social Media: Persistence and Decay

Cited by 262 publications

References 13 publications

Topic detection and tracking on heterogeneous information

Topic detection and tracking on heterogeneous information

Social proof in social media shopping: An experimental design research

Factores de éxito de las comunidades virtuales universitarias basadas en redes sociales. Análisis de XarFED

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