Life Cycle Modeling of News Events Using Aging Theory

Chen, Chia Ling; Chen, Yao-Tsung; Sun, Yeali S.; Chen, Meng Chang

doi:10.1007/978-3-540-39857-8_7

Cited by 66 publications

(61 citation statements)

References 8 publications

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“…This disqualified them because we set the proviso that the results should be independent from the initial conditions -our method of axial K-means (Lelu, 1994) is part of this family; the local optima for a given number of clusters mostly reveal the same main clusters which are often trivial but can also make the most interesting ones of average or low size appear/disappear/amalgamate or split. Quite a lot of incremental variants of these methods have been proposed (Binztock and Gallinari, 2002;Chen et al, 2003) and a partial review of these can be found in Lin et al (2004). Many come from the DARPA-TDT research programme such as Gaudin and Nicoloyannis (2005), Gaber et al (2005).…”

Section: Adapting Methods With Mobile Centres To Incrementalitymentioning

confidence: 99%

Paving the way for next generation data-stream clustering: towards a unique and statistically valid cluster structure at any time step

Cuxac

Lelu

Cadot

2011

IJDMMM

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Abstract:In the domain of data-stream clustering, e.g., dynamic text mining as our application domain, our goal is two-fold and a long term one: 1 at each data input, the resulting cluster structure has to be unique, independent of the order the input vectors are presented 2 this structure has to be meaningful for an expert, e.g., not composed of a huge 'catch-all' cluster in a cloud of tiny specific ones, as is often the case with large sparse data tables.The first preliminary condition is satisfied by our Germen density-mode seeking algorithm, but the relevance of the clusters vis-à-vis expert judgment relies on the definition of a data density, relying itself on the type of graph chosen for embedding the similarities between text inputs. Having already demonstrated the dynamic behaviour of Germen algorithm, we focus here on appending a Monte-Carlo method for extracting statistically valid inter-text links, which looks promising applied both to an excerpt of the Pascal bibliographic database, and to the Reuters-RCV1 news test collection. Though not being a central issue here, the time complexity of our algorithms is eventually discussed. 342P. Cuxac et al.

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Section: Adapting Methods With Mobile Centres To Incrementalitymentioning

confidence: 99%

Paving the way for next generation data-stream clustering: towards a unique and statistically valid cluster structure at any time step

Cuxac

Lelu

Cadot

2011

IJDMMM

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“…Approaches in TDT were mainly variants and improvements of the single pass method and agglomerative clustering algorithms [2,3,7,14,15,16,19,21,22,23]. Although [3] concluded that time information "did not help" improve the new event detection results, some recent work has utilized the aging theory or timeline analysis, and achieved good performance in TDT and hot topic extraction [4,5]. The state-of-the-art TDT techniques are used to generate topics from news stories in our work.…”

Section: Related Workmentioning

confidence: 98%

“…Previous approaches listed above analyzed the characteristics of features from a fixed corpus on the whole timeline, and hence have to be adjusted to suit to online use. Our system deals with dynamic-increasing news data online, and makes use of an aging theory [4] in topic detection and tracking.…”

Section: Related Workmentioning

confidence: 99%

Automatic online news topic ranking using media focus and user attention based on aging theory

Wang

Zhang

et al. 2008

Proceedings of the 17th ACM Conference on Information and Knowledge Management

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News topics, which are constructed from news stories using the techniques of Topic Detection and Tracking (TDT), bring convenience to users who intend to see what is going on through the Internet. However, it is almost impossible to view all the generated topics, because of the large amount. So it will be helpful if all topics are ranked and the top ones, which are both timely and important, can be viewed with high priority. Generally, topic ranking is determined by two primary factors. One is how frequently and recently a topic is reported by the media; the other is how much attention users pay to it. Both media focus and user attention varies as time goes on, so the effect of time on topic ranking has already been included. However, inconsistency exists between both factors. In this paper, an automatic online news topic ranking algorithm is proposed based on inconsistency analysis between media focus and user attention. News stories are organized into topics, which are ranked in terms of both media focus and user attention. Experiments performed on practical Web datasets show that the topic ranking result reflects the influence of time, the media and users. The main contributions of this paper are as follows. First, we present the quantitative measure of the inconsistency between media focus and user attention, which provides a basis for topic ranking and an experimental evidence to show that there is a gap between what the media provide and what users view. Second, to the best of our knowledge, it is the first attempt to synthesize the two factors into one algorithm for automatic online topic ranking.

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“…C. Chen proposes an aging theory to model the life cycle of event [4]. The thought of TDT and aging theory are employed in our work.…”

Section: ) Detection the Cycle Life Of Eventmentioning

confidence: 99%

“…Some scholars rank news event by efficiency and transfer information of it [2], and others sort web pages by the layout of page and news transfer information [3]. In addition, with the presence of the aging theory [4] for news event life-cycle modelling, the influence of a news event is to decay over time. The biological aging rate is not a constant ratio.…”

Section: Introductionmentioning

confidence: 99%

Event Multiple Influence Calculation and Relationship of Multiple Influence Discovery

Xue¹,

Xu²,

Yu³

2016

International Conferences on Software Engineering and Knowledge Engineering

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Abstract-Due to the development of web technology, the research on Internet big data become more and more popular. However, information presents polymorphism and complexity under the status of the rapid development of Internet big data. Most of the traditional methods of event influence calculation use observable data to evaluate and ignore the decay influence of event. In fact, event influence growths from the moment of event happening to the end, meanwhile, it accompanis with a certain decay. What's more, event influence presents observable influence and decay influence on different network media. This paper proposes a new method to calculate event influence on different network media and find the relationships between observable influence and decay influence on different stages of event. Experiments show that better influence calculation will be achieved by decay algorithm based on Ebbinghaus forgetting curve and information fusion by considering interaction between observable influence and decay influence.

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Life Cycle Modeling of News Events Using Aging Theory

Cited by 66 publications

References 8 publications

Paving the way for next generation data-stream clustering: towards a unique and statistically valid cluster structure at any time step

Paving the way for next generation data-stream clustering: towards a unique and statistically valid cluster structure at any time step

Automatic online news topic ranking using media focus and user attention based on aging theory

Event Multiple Influence Calculation and Relationship of Multiple Influence Discovery

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