Review on event detection techniques in social multimedia

Garg, Muskan; Kumar, Mukesh

doi:10.1108/oir-08-2015-0281

Cited by 26 publications

(8 citation statements)

References 36 publications

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“…Leidig and Teeuw (2015) examined the characteristics of geographic information software used in a disaster context, providing insights for further studies. Garg and Kumar (2016) reviewed research on the role of social media in spreading information and attracting attention during emergency situations. However, these studies focussed solely on one specific IT, and comprehensive research mapping the landscape of the research area is still lacking.…”

Section: Introductionmentioning

confidence: 99%

A bibliometric analysis of emergency management using information systems (2000-2016)

Chu³

et al. 2017

OIR

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Purpose The purpose of this paper is to present a statistical analysis of research into emergency management (EM) using information systems (IS) for the period 2000-2016. Design/methodology/approach In this study, research trends in the area of EM using IS are analysed using various parameters, including trends on publications and citations, disciplinary distribution, journals, research institutions and regional cooperation. Through a keyword co-occurrence analysis, this study identifies the evolution of the main keywords in this area, and examines the changes and developments in the main focus of scholars in this period. The study also explores the main research orientations in the field by analysing and integrating the results of two cluster analyses conducted from keyword- and reference-based perspectives, respectively. Findings The area of EM using IS has received increased attention and interest by researchers and practitioners. It is suggested that more cooperation among research institutions is required to help facilitate the further development of the area. Six main research orientations are identified: namely Web 2.0-enabled research, geographic information technology (IT), IT-based research, the contextual use of IT, crisis collaboration research and mass media communication research, since the research area first became popular in 2006. Originality/value This study is the first to comprehensively map the landscape of EM by conducting a bibliometric analysis of the research using IS. The authors’ findings can help academics and emergency managers gain a comprehensive understanding of the research area, and guide scholars towards producing more effective findings.

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Section: Introductionmentioning

confidence: 99%

A bibliometric analysis of emergency management using information systems (2000-2016)

Chu³

et al. 2017

OIR

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“…The data collected in this paper are six different categories of popular events in Sina Weibo, and the six events are as follows: "Xi'an Benz Rights Protection", "Tianlin Zhai academic fraud", "African locust plague", "2020 college entrance examination postponed", "Fang Fang's Diary" and "Teacher salary is not lower than civil servant". The data were all taken within a month after the event was exposed [16]. The data generation time, and the amount of data of specific event are shown in Table 9 of Appendix 1.…”

Section: Cases From Weibo Mediamentioning

confidence: 99%

Tracking Attention of Social Media Event by Hidden Markov Model–Cases from Sina Weibo

Ma¹,

Jiao²,

Song³

2021

IEEE Access

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Sina Weibo has significantly impact on the information diffusion processes in many realworld social events. A large number of active users on Sina Weibo not only push the opinion diffusion, but also increase the influence abilities of events which conversely attracted much attentions of users to follow them. How to effectively track the event attention of users is one of the most important channels to get the public opinions. In order to predict the event attention more accurately, motivated by observations of social events' influence concerning with users and microblogs, we quantify the user popularity from the four dimensions: the user activity, the user behavior, the user authenticity and the user infection ability. And the non-collinearity of these four dimensions is tested to ensure the comprehensiveness and nonredundancy of the evaluation. Then, combining with the logic framework of Hidden Markov Model, we propose an algorithm to predict the Weibo event attention by using the user popularity. Meanwhile, in order to better detect the performance of the prediction algorithm, we integrate the static and dynamic information of microblog content to directly quantify the current Weibo event attention as a benchmark, and the performance of four prediction algorithms (including our algorithm) is tested with six real data sets which are chosen from the popular events in China from 2019 to 2020. Through comparison, we find that the user popularity can be used to predict the event attention, and the Hidden Markov Model prediction method by using the user popularity shows good prediction performance.

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“…Example of such models is ED model for news data, whereby this data covers events from different domains such as politics, natural disaster, airplane crash, conflict, sports, education and so forth. However, build open domain ED model for news data that is published by several sources is very challenging task (Beigh et al, 2016;Chen et al, 2016;Garg and Kumar, 2016;Goswami and Kumar, 2016;Ramadan and Mohd, 2011;Zarrinkalam and Bagheri, 2017;Zhou et al, 2017). This is due to the high dimensional data which includes irrelevant, duplicated and noisy features that eventually, decrease the overall accuracy of ED model (Allahyari et al, 2017;Beigh et al, 2016;Bharti and Singh, 2016;Chen et al, 2016;Panagiotou et al, 2016).…”

Section: Build Open Domain Event Detection Modelmentioning

confidence: 99%

A Survey on Event Detection Models for Text Data Streams

Al-Dyani¹,

Ahmad²,

Kamaruddin³

2020

Journal of Computer Science

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Event Detection (ED) is a study area that attracts the attention of decision-makers from various disciplines in order to help them in taking the right decision. ED has been examined on various text streams like Twitter, Facebook, Emails, Blogs, Web Forums and newswires. Many ED models have been proposed in literature. In general, ED model consists of six main phases: Data collection, pre-processing, feature selection, event detection, performance evaluation and result representation. Among these phases, event detection phase has a vital rule in the performance of the ED model. Consequently, numerous supervised, unsupervised, semi-supervised detection methods have been introduced for this phase. However, unsupervised methods have been extensively utilized as ED process is considered as unsupervised task. Hence, such methods need to be categorized on such a way so it can help researchers to understand and identified the limitations lay in these methods. In this survey, ED models for text data from various Social Network sites (SNs) are analyzed based on domain type, detection methods, type of detection task. In addition, main categories for unsupervised detection methods are explicitly mentioned with revising their related works. Moreover, the major open challenges faced by researchers for building ED models are explained and discussed in detail. The main objective of this survey paper is to provide a complete view of the recent developments in ED field. Hence, help scholars to identify the limitations of existing ED models for text data and help them to recognize the interesting future works directions.

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Review on event detection techniques in social multimedia

Cited by 26 publications

References 36 publications

A bibliometric analysis of emergency management using information systems (2000-2016)

A bibliometric analysis of emergency management using information systems (2000-2016)

Tracking Attention of Social Media Event by Hidden Markov Model–Cases from Sina Weibo

A Survey on Event Detection Models for Text Data Streams

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