Measuring user influence on Twitter: A survey

Riquelme, Fabián; González-Cantergiani, Pablo

doi:10.1016/j.ipm.2016.04.003

Cited by 373 publications

(268 citation statements)

References 123 publications

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“…Se debe tener en cuenta que la API de Twitter utilizada para la recolección sólo provee una proporción del total de publicaciones de Twitter existentes. Específicamente, esta proporción oscila entre el 1% y el 40% de las publicaciones generadas en tiempo real [23], [24], [25]. Con respecto a la recolección de alertas de Waze se empleó un filtro de delimitación geoespacial.…”

Section: Recolección Del Conjunto De Datosunclassified

Untitled

Vallejos¹,

Caimmi²,

Alonso³

et al. 2018

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Nowadays, social networks have become in a communication medium widely used to disseminate any type of information. In particular, the shared information in social networks usually includes a considerable number of traffic incidents reports of specific cities. In light of this, specialized social networks have emerged for detecting and disseminating traffic incidents, differentiating from generic social networks in which a wide variety of topics are communicated. In this context, Twitter is a case in point of a generic social network in which its users often share information about traffic incidents, while Waze is a social network specialized in traffic. In this paper we present a comparative study between Waze and an intelligent approach that detects traffic incidents by analyzing publications shared in Twitter. The comparative study was carried out considering Ciudad Autónoma de Buenos Aires (CABA), Argentina, as the region of interest. The results of this work suggest that both social networks should be considered as complementary sources of information. This conclusion is based on the fact that the proportion of mutual detections, i.e. traffic incidents detected by both approaches, was considerably low since it did not exceed 6% of the cases. Moreover, the results do not show that any of the approaches tend to anticipate in time to the other one in the detection of traffic incidents.Resumen Hoy en día, las redes sociales se han convertido en un medio de comunicación ampliamente utilizado para divulgar todo tipo de información. En particular, entre la información que es compartida se suelen incluir reportes de incidentes de tránsito de ciudades específicas. En vista de esto, aparte de las redes sociales genéricas en donde se comunican una amplia variedad de temas, han surgido redes sociales especializadas en la detección y divulgación de incidentes de tránsito. En este contexto, Twitter es un ejemplo de red social genérica en donde sus usuarios suelen informar incidentes de tránsito, mientras que Waze es una red social especializada en tránsito. En este artículo presentamos un estudio comparativo entre Waze y un enfoque inteligente que detecta incidentes de tránsito a partir del análisis de publicaciones compartidas en Twitter. El estudio comparativo fue realizado considerando a la Ciudad Autónoma de Buenos Aires (CABA), Argentina, como región de interés. Los resultados de este trabajo sugieren que ambos enfoques deberían ser considerados como fuentes de información complementarias. Esta conclusión se fundamenta en que la proporción de detecciones mutuas, es decir incidentes de transito detectados por ambos enfoques, resultó ser considerablemente baja no superando el 6% de los casos. Además, los resultados no evidencian que alguno de los enfoques tienda a anticipar temporalmente a su similar en la detección de incidentes.

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Section: Recolección Del Conjunto De Datosunclassified

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Vallejos¹,

Caimmi²,

Alonso³

et al. 2018

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“…Without the support from underlying data, these literatures provide very limited instruction to people how to predict mass incidents, how to prevent mass incidents, and how to manage mass incidents. From the perspective of research on message dissemination, social networks have emerged as a critical factor in the spread of information [11], marketing [12], innovation, and influence discovery. Data sets from social networks offer rich sources of evidences for studying the structure of social networks, the dynamics of individual, group behavior, global properties of information cascades [13], and identification of influential entities.…”

Section: Related Workmentioning

confidence: 99%

Based on Agent Model and K-Core Decomposition to Analyze the Diffusion of Mass Incident in Microblog

Pan¹,

Shen²,

Zhong³

2017

Complexity

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Mass incidents, which may influence the stability and security of the society in China, are getting more and more attentions not only from policy makers but also from Chinese social researchers. Catching the diffusion mechanism is believed to be critical to understand essential of these mass incidents since message dissemination plays important roles in every stage of mass incident. Recently, online social networks including Weibo (Chinese Twitter) become more and more popular in China. There are reports showing that Weibo discussion has accompanied the processes of most mass incidents happening in China in these few years. So, in this paper, we aim at introducing -Core decomposition method from complex network to the analysis on how to manage the diffusion of mass incident in Weibo based on agent based model which can simulate Weibo user's actions when mass incident happens. This work can help people understand how mass incident messages spread across the network. And then, people may have better strategy to manage the diffusion of mass incidents.

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“…A very early definition for influential people is "individuals who were likely to influence other persons in their immediate environment" (Katz 1957). Social influence has either been studied to identify influential users (opinion leaders or authorities), topical or topic-based influential users (Riquelme 2015).…”

Section: Introductionmentioning

confidence: 99%

Topic-based Social Influence Measurement for Social Networks

Hamzehei

Jiang²,

Koutra

et al. 2017

AJIS

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Social science studies have acknowledged that the social influence of individuals is not identical. Social networks structure and shared text can reveal immense information about users, their interests, and topic-based influence. Although some studies have considered measuring user influence, less has been on measuring and estimating topic-based user influence. In this paper, we propose an approach that incorporates network structure, usergenerated content for topic-based influence measurement, and user's interactions in the network. We perform experimental analysis on Twitter data and show that our proposed approach can effectively measure topic-based user influence.

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Measuring user influence on Twitter: A survey

Cited by 373 publications

References 123 publications

Untitled

Untitled

Based on Agent Model and K-Core Decomposition to Analyze the Diffusion of Mass Incident in Microblog

Topic-based Social Influence Measurement for Social Networks

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