An automatic topic ranking approach for event detection on microblogging messages

Lee, Chung-Hong; Chien, Tzan-Feng; Yang, Hsin-Chang

doi:10.1109/icsmc.2011.6083847

Cited by 11 publications

(5 citation statements)

References 8 publications

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“…These candidate neighbours will be taken to calculate the distance that represents the extent of temporal text similarity between these messages. The similarity measure also considers the timing effects for the reduction of similarity for two messages with a different time distance [37]. Consequently, the exact neighbour set of the message will be established to further support text clustering.…”

Section: The Proposed Methods For Stream Mining and Clustering On mentioning

confidence: 99%

“…In equation (2), the standard cosine function of similarity computation is used for content-based similarity measurement. For the consideration of temporal information, the temporal penalty tp ( m a ,m b ) is an exponential distribution that can calculate the timing effects for the reduction of similarity for two documents with different time distances [37]. Consequently, the exact neighbour set of the message m will be established to further support text clustering.…”

Section: The Proposed Methods For Stream Mining and Clustering On mentioning

confidence: 99%

“…Therefore, the developers should design more efficient algorithms to rank posts, especially avoiding the use of a factor that will calculate the entire collection each time. In order to satisfy such an information need, we developed an approach called topic energy [37] to evaluate the significance of each topic. To understand the topic evolution over time, the topic energy uses records to evaluate the extent of significance of a given topic at each time point over a time period.…”

Section: The Proposed Methods For Stream Mining and Clustering On mentioning

confidence: 99%

“…However, in streaming data, the patterns of a priori knowledge may possibly cause errors because the concept of streaming data is rapidly changing. Hence a good text stream clustering should not segment an evolving topic into separated topics [37]. To overcome this problem, Aggarwal et al [26] proposed a micro-clustering approach to storing summaries in macro-clusters as snapshots.…”

Section: Related Workmentioning

confidence: 99%

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Leveraging microblogging big data with a modified density-based clustering approach for event awareness and topic ranking

Lee

Chien

2013

Journal of Information Science

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Although diverse groups argue about the potential and true value benefits from social-media big data, there is no doubt that the era of big data exploitation has begun, driving the development of novel data-centric applications. Big data is notable not only because of its size, but also because of the complexity caused by its relationality to other data. In the past, owing to the limited possibilities of accessing big data, few data sources were available to allow researchers to develop advanced data-driven applications, such as monitoring of emerging real-world events. In fact, social media is greatly impacting the growth of big data; and big data is providing enterprises with the data to help them understand how to better detect marketing demands. Microblogging is a social network service capable of aggregating messages to explore facts and unknown knowledge. Nowadays, people often attempt to search for trending news and hot topics in real time from microblogging messages to satisfy their information needs. Under such a circumstance, a real demand is to find a way to allow users to organize a large number of microblogging messages into understandable events. In this work, we attempt to tackle such challenges by developing an online text-stream clustering approach using a modified density-based clustering model with collected microblogging big data. The system kernel combines three technical components, including a dynamic term weighting scheme, a neighbourhood generation algorithm and an online density-based clustering technique. After acquiring detected event topics by the system, our system provides functions for recommending top-priority event information to assist people to effectively organize emerging event data through the developed topic ranking algorithm.

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Section: The Proposed Methods For Stream Mining and Clustering On mentioning

confidence: 99%

Section: The Proposed Methods For Stream Mining and Clustering On mentioning

confidence: 99%

Section: The Proposed Methods For Stream Mining and Clustering On mentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

See 2 more Smart Citations

Leveraging microblogging big data with a modified density-based clustering approach for event awareness and topic ranking

Lee

Chien

2013

Journal of Information Science

Self Cite

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“…Researches on the recognition and discovery of emerging topics on online social networks had got much attention in the past few years (Lavrenko, Allan, DeGuzman et al, 2002;Yang, Ault, Pierce et al, 2000;Zhang, Li, Chao, 2012;Lee, Chien, & Yang, 2011). TDT (Topic Detection and Tracking, TDT) is the basis of emergency topic detection and trend prediction.…”

Section: Emerging Topics Discoveringmentioning

confidence: 99%

A Study on Online Social Networks Theme Semantic Computing Model

Yihan

2016

International Journal of Web Services Research

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The widespread use of Mobile Intelligent Terminals and ubiquitous access to networks has enabled online information sources including Weibo and Wechat to bring huge impact to the society. Only a few words of network information can expand rapidly and catalyze the generation of a huge amount of information. The highly real-time content, fission-like spreading rate and enormous public opinion guiding forces created in this process will cast great influence on the society. Thus, semantic computing on online social networks and research on topics about emergencies have great significance. In this article, a numerical model of text semantic analysis based on artificial neural network is proposed, and a semantic computational algorithm for social network texts as well as a discovery algorithm for emergencies is provided with reference to the information provided by the social nodes itself and the semantic of the text. Through the numerization of text, the calculation and comparison of semantic distance, the classification of nodes and the discovery of community can be realized. In this article, semantic vector of micro-information for nodes and closure extension of semantic extensions are defined in order to build up an equivalence of short sentences, and in turn realize the discovery of emergencies. Then, huge quantities of Sina Weibo contents are collected to verify the model and algorithm put forward in this article. In the end, outlooks for future jobs are provided.

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Monitoring Social Events on Sina Weibo

Zhang

Jiang

2015

Trustworthy Computing and Services

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An automatic topic ranking approach for event detection on microblogging messages

Cited by 11 publications

References 8 publications

Leveraging microblogging big data with a modified density-based clustering approach for event awareness and topic ranking

Leveraging microblogging big data with a modified density-based clustering approach for event awareness and topic ranking

A Study on Online Social Networks Theme Semantic Computing Model

Monitoring Social Events on Sina Weibo

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