A Fine-Grained Approach for Extracting Events on Microblogs

Zheng, Lizhou; Jin, Peiquan; Zhao, Jie; Yue, Lihua

doi:10.1007/978-3-319-10073-9_22

Cited by 18 publications

(9 citation statements)

References 8 publications

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“…Thus, people can expand their network in society and finally form a big social network. Generally, social networks can provide a number of new applications and services, such as news feature exploration [7], event monitoring [8,9], and sentiment analysis of reviews [10].…”

Section: Knowledge Sharing In Social Networkmentioning

confidence: 99%

User Willingness toward Knowledge Sharing in Social Networks

et al. 2018

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Social networks introduce new potential for people to share knowledge with others. However, it is not clear what factors influence user willingness toward knowledge sharing in social networks. Aiming to answer these questions, in this paper we analyze the major factors influencing user willingness toward knowledge sharing in social networks and propose a new research model that is inspired by the technology acceptance model (TAM). In particular, we introduce a new independent variable called perceived value which is described by four aspects: social value, entertainment value, emotion value, and information value. In addition, we introduce a new mediating variable, trust, to reflect the intermediating relationship between perceived value and knowledge-sharing willingness. We conduct an empirical analysis on questionnaire data and present comprehensive results on reliability and validity, factor analysis, correlation analysis, and mediating effects analysis. The results show that perceived value has a significant impact on knowledge-sharing willingness, and trust plays a partial intermediate role between perceived value and knowledge-sharing willingness. Further, we present some research implications for knowledge sharing and learning innovation in social networks, as well as some suggestions for organizations to advance knowledge sharing and learning innovation in the social-network age.

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Section: Knowledge Sharing In Social Networkmentioning

confidence: 99%

User Willingness toward Knowledge Sharing in Social Networks

et al. 2018

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“…[6] provides a 4-tuple (Time, Locations, Entities, and Keywords) structure of the detected events. Lizhou Zheng et al extracted 5W1H-tuple to describe an event [7]. However, words only are uneasy for people to understand an event.…”

Section: Related Workmentioning

confidence: 99%

Event Detection and Summarization on Microblogs

Zhao¹,

Liu²,

Cui³

2016

IJGDC

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“…For the algorithms of event detection, we propose to use a fine -grained approach [33,34], which is shown in Figure 5. As microblogs are very short, we directly use the POS (Part of Speech) tagging and grammar analysis to extract the description of WHO/WHY/HOW/WHAT.…”

Section: Event Detection and Clusteringmentioning

confidence: 99%