Clustering-based burst-detection algorithm for web-image document stream on social media

Tamura, Shigehiko; Tamura, Keiichi; Kitakami, Hajime; Hirahara, Kaishi

doi:10.1109/icsmc.2012.6377809

Cited by 6 publications

(3 citation statements)

References 24 publications

(32 reference statements)

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“…Kleinberg's () approach is analogous to models from queueing theory for ‘bursty’ network traffic and is based on modelling the document stream as a continuous state space in which bursts appear as state transitions. For example, when interest in a topic or event increases (diminishes), the number of documents that include particular words related to the event or topic will increase (decrease) (Tamura et al ., ). The approach has seen widespread adoption for bibliometric analyses (e.g., Mane and Börner, ) and has also been integrated in tools to visualize publication outputs (Börner et al ., ).…”

Section: Topic Emergencementioning

confidence: 97%

Machine Learning and Expert Judgement: Analyzing Emerging Topics in Accounting and Finance Research in the Asia–Pacific

Cai

Linnenluecke

Marrone

et al. 2019

Abacus

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In this paper, we focus on the question to what extent machine learning (ML) tools can be used to support systematic literature reviews. We apply a ML approach for topic detection to analyze emerging topics in the literature-our context is accounting and finance research in the Asia-Pacific region. To evaluate the robustness of the approach, we compare findings from the automated ML approach with the results from a manual analysis of the literature. The automated approach uses a keyword algorithm detection mechanism whereby the manual analysis uses common techniques for qualitative data analysis, that is, triangulation between researchers (expert judgement). From our paper, we conclude that both methods have strengths and weaknesses. The automated analysis works well for large corpora of text and provides a very standardized and non-biased way of analyzing the literature. However, the human researcher is potentially better equipped to evaluate current issues and future trends in the literature. Overall, the best results might be achieved when a variety of tools are used together.

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Section: Topic Emergencementioning

confidence: 97%

Machine Learning and Expert Judgement: Analyzing Emerging Topics in Accounting and Finance Research in the Asia–Pacific

Cai

Linnenluecke

Marrone

et al. 2019

Abacus

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“…A work on summarizing Sporting Events Using Twitter was done based on an automated method for implicitly crowd sourcing summaries of events using only status updates posted to Twitter as a source [8]. The detection of events in web image document stream on social media based on clustering technique integrates with Kleinberg's burst detection [9]. A work on document clustering with bursty information proposed bursty feature representations that perform better than VSM on various text mining tasks, such as document retrieval, topic modelling and text categorization [10].…”

Section: Related Workmentioning

confidence: 99%

Event Detection and Summarization Based on Social Networks and Semantic Query Expansion

Sathiyamurthy¹,

Shanmugavalli²,

Udayalakshmi³

2014

IJNLC

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Events can be characterized by a set of descriptive, collocated keywords extracted documents. Intuitively, documents describing the same event will contain similar sets of keywords, and the graph for a document collection will contain clusters individual events. Helping users to understand the event is an acute problem nowadays as the users are struggling to keep up with tremendous amount of information published every day in the Internet. The challenging task is to detect the events from online web resources, it is getting more attentions. The important data source for event detection is a Web search log because the information it contains reflects users' activities and interestingness to various real world events. There are three major issues playing role for event detection from web search logs: effectiveness, efficiency of detected events. We focus on modeling the content of events by their semantic relations with other events and generating structured summarization. Event mining is a useful way to understand computer system behaviors. The focus of recent works on event mining has been shifted to event summarization from discovering frequent patterns. Event summarization provides a comprehensible explanation of the event sequence based on certain aspects.

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“…Previous efforts on mining information from sets of images include detecting social events and tracking the corresponding related topics which can even include the identification of touristic attractions (Tamura et al, 2012).…”

Section: Related Workmentioning

confidence: 99%

Techniques for Effective and Efficient Fire Detection from Social Media Images

Bedo

Blanco

Oliveira

et al. 2015

Proceedings of the 17th International Conference on Enterprise Information Systems

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Abstract:Social media could provide valuable information to support decision making in crisis management, such as in accidents, explosions and fires. However, much of the data from social media are images, which are uploaded in a rate that makes it impossible for human beings to analyze them. Despite the many works on image analysis, there are no fire detection studies on social media. To fill this gap, we propose the use and evaluation of a broad set of content-based image retrieval and classification techniques for fire detection. Our main contributions are: (i) the development of the Fast-Fire Detection method (FFireDt), which combines feature extractor and evaluation functions to support instance-based learning; (ii) the construction of an annotated set of images with ground-truth depicting fire occurrences -the Flickr-Fire dataset; and (iii) the evaluation of 36 efficient image descriptors for fire detection. Using real data from Flickr, our results showed that FFireDt was able to achieve a precision for fire detection comparable to that of human annotators. Therefore, our work shall provide a solid basis for further developments on monitoring images from social media.

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Clustering-based burst-detection algorithm for web-image document stream on social media

Cited by 6 publications

References 24 publications

Machine Learning and Expert Judgement: Analyzing Emerging Topics in Accounting and Finance Research in the Asia–Pacific

Machine Learning and Expert Judgement: Analyzing Emerging Topics in Accounting and Finance Research in the Asia–Pacific

Event Detection and Summarization Based on Social Networks and Semantic Query Expansion

Techniques for Effective and Efficient Fire Detection from Social Media Images

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