Predicting the content dissemination trends by repost behavior modeling in mobile social networks

Xia, Lu; Yu, Zhiwen; Guo, Bin; Zhou, Xingshe

doi:10.1016/j.jnca.2014.01.015

Cited by 53 publications

(25 citation statements)

References 26 publications

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“…Specifically, the authors found that a low link density and a deep diffusion usually lead to wide spreading, that is, a diverse group of individuals is able to disseminate a message to wider audience than a dense group. For example, with a better understanding of the discovered diffusion patterns, we can develop more fine grained prediction models (e.g., retweeting behavior prediction) to estimate the propagation of crucial messages [Lu et al 2014;.…”

Section: Trending Predictionmentioning

confidence: 99%

Discovering Information Propagation Patterns in Microblogging Services

Wang

et al. 2015

ACM Trans. Knowl. Discov. Data

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During the last decade, microblog has become an important social networking service with billions of users all over the world, acting as a novel and efficient platform for the creation and dissemination of real-time information. Modeling and revealing the information propagation patterns in microblogging services cannot only lead to more accurate understanding of user behaviors and provide insights into the underlying sociology, but also enable useful applications such as trending prediction, recommendation and filtering, spam detection and viral marketing. In this article, we aim to reveal the information propagation patterns in Sina Weibo, the biggest microblogging service in China. First, the cascade of each message is represented as a tree based on its retweeting process. Afterwards, we divide the information propagation pattern into two levels, that is, the macro level and the micro level. On one hand, the macro propagation patterns refer to general propagation modes that are extracted by grouping propagation trees based on hierarchical clustering. On the other hand, the micro propagation patterns are frequent information flow patterns that are discovered using tree-based mining techniques. Experimental results show that several interesting patterns are extracted, such as popular message propagation, artificial propagation, and typical information flows between different types of users.

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Section: Trending Predictionmentioning

confidence: 99%

Discovering Information Propagation Patterns in Microblogging Services

Wang

et al. 2015

ACM Trans. Knowl. Discov. Data

Self Cite

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“…De acordo com Lin e Lu (2011), um dos motivos para as pessoas ingressarem em redes sociais virtuais é a complementariedade percebida por meio de conteúdos passíveis de serem consumidos e compartilhados, como fotografias, mensagens e vídeos. O mecanismo de compartilhamento ou repostagem de um conteúdo é normalmente estudado no contexto da difusão de informação, e pesquisadores recorrem aos microblogs como ambiente de operacionalização de suas investigações (Lu, Yu, & Zhou, 2014 …”

Section: Desenvolvimento Das Hipóteses De Pesquisaunclassified

Quem Lidera sua Opinião? Influência dos Formadores de Opinião Digitais no Engajamento

et al. 2018

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Resumo Influenciadores digitais são formadores de opinião virtuais que representam uma alternativa para empresas que confiam na comunidade reunida em torno desses perfis como público-alvo de divulgação. Esses indivíduos expandiram conceitos de teorias consolidadas que versam sobre o processo de difusão de inovações e o fluxo comunicacional entre líderes de opinião e seguidores. Pesquisas sobre o poder dos formadores de opinião nas redes sociais virtuais estão nos estágios iniciais e uma lacuna é a mensuração da sua capacidade de influência sobre o engajamento. Este artigo apresenta uma abordagem que mensura essa influência no Instagram por meio da análise de todas as postagens publicadas ao longo de 2015 em quatro perfis de grande audiência. Três hipóteses avaliaram diferenças nas métricas de engajamento provocadas por formadores de opinião individuais (perfis de pessoas) e institucionais. Os modelos de regressão em dois estágios confirmaram as hipóteses de que são os formadores de opinião individuais que provocam mais engajamento: postagens em perfis individuais produzem significativamente mais curtidas, comentários e boca a boca que publicações em perfis institucionais. Os resultados da pesquisa são particularmente relevantes para empresas que buscam aumentar os níveis de interação com seus públicos nas redes sociais virtuais.

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“…The term mobile social networking is an extension of social networking where individuals with similar interests converse and connect with one another through their mobile phones and/or P/C tablets and the use of existing social networks to create native communities and promote discovery, and webbased social networks to take advantage of mobile features and accessibility (Lu, Yu, Guo, & Zhou, 2014). However, m-learning or mobile learning is simply defined as learning across multiple contexts, through social and content interactions, using personal electronic devices (Lu et al, 2014).…”

Section: Revisiting the Term Mobile Social Networkingmentioning

confidence: 99%

“…However, m-learning or mobile learning is simply defined as learning across multiple contexts, through social and content interactions, using personal electronic devices (Lu et al, 2014). Therefore, it can be seen as a form of virtual distance education, which facilitates m-learners to use mobile device for educational purposes at any time and any place.…”

Section: Revisiting the Term Mobile Social Networkingmentioning

confidence: 99%

The perceived benefits and barriers to the application of mobile and social networking technologies in Higher Learning Institutions

Chakuzira

Kadyamatimba

2017

Problems and Perspectives in Management

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This research investigated on the perceived benefits and barriers to the application of mobile and social networking technologies in South African Higher Learning Institutions (HLIs). A quantitative orientation was used in carrying out the study through self-administered questionnaires and a descriptive quantitative method of analysis was applied. The research population consisted of some students and lecturers from University of Venda (Univen) and University of Limpopo (UL). The research results showed that indeed HLIs in South Africa enjoy some benefits using mobile and social networking technologies which makes students and lecturers participate in virtual learning. The results also confirmed that HLIs in South Africa are faced with various barriers affecting mobile and social networking technology adaptations. Five noteworthy barriers identified were connectivity, security problems, small screens, lack of equipment and technology and lack of training among users. Benefits which were identified included, improved participation, improved communication between students and lecturers and enhancement of teaching and learning processes in HLIs. The research recommended HLIs to be aware of the major Learning Management Systems (LMS) which they should use together with social networking platforms to successfully gain full benefits of using mobile and social networking technologies in the education sector.

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Predicting the content dissemination trends by repost behavior modeling in mobile social networks

Cited by 53 publications

References 26 publications

Discovering Information Propagation Patterns in Microblogging Services

Discovering Information Propagation Patterns in Microblogging Services

Quem Lidera sua Opinião? Influência dos Formadores de Opinião Digitais no Engajamento

The perceived benefits and barriers to the application of mobile and social networking technologies in Higher Learning Institutions

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