A Real-Time Burst Detection Method

Nakamura, Kenji; Oyanagi, Shigeru

doi:10.1109/ictai.2011.177

Cited by 21 publications

(13 citation statements)

References 12 publications

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“…In our system we adopt an approach based on a burst detection algorithm. A burst is defined as a large number of occurrences of a phenomenon within a short time window [15]. caused by the large number of messages shared on social media by the people who actually felt the shaking.…”

Section: Emergency Detectionmentioning

confidence: 99%

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Pulling Information from social media in the aftermath of unpredictable disasters

Avvenuti

Vigna

Cresci

et al. 2015

2015 2nd International Conference on Information and Communication Technologies for Disaster Management (ICT-DM)

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Social media have become a primary communication channel among people and are continuously overwhelmed by huge volumes of User Generated Content. This is especially true in the aftermath of unpredictable disasters, when users report facts, descriptions and photos of the unfolding event. This material contains actionable information that can greatly help rescuers to achieve a better response to crises, but its volume and variety render manual processing unfeasible. This paper reports the experience we gained from developing and using a web-enabled system for the online detection and monitoring of unpredictable events such as earthquakes and floods. The system captures selected message streams from Twitter and offers decision support functionalities for acquiring situational awareness from textual content and for quantifying the impact of disasters. The software architecture of the system is described and the approaches adopted for messages filtering, emergency detection and emergency monitoring are discussed. For each module, the results of real-world experiments are reported. The modular design makes the system easy configurable and allowed us to conduct experiments on different crises, including Emilia earthquake in 2012 and Genoa flood in 2014. Finally, some possible functionalities relying on the analysis of multimedia information are introduced.

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Section: Emergency Detectionmentioning

confidence: 99%

“…Experiments. Building on the "bursty" characteristics of emergency reports, we adapted the burst detection algorithm originally proposed in [15] and we applied it to the detection of earthquakes in Italy. The detection is performed solely from Twitter data.…”

Section: Emergency Detectionmentioning

confidence: 99%

Pulling Information from social media in the aftermath of unpredictable disasters

Avvenuti

Vigna

Cresci

et al. 2015

2015 2nd International Conference on Information and Communication Technologies for Disaster Management (ICT-DM)

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“…Works in [10], [32] and [16] discuss various burst detection algorithms. In EARS we chose to employ a simplified version of the hierarchical algorithm proposed in [10] since it is computationally light and can adapt well to both big and small bursts.…”

Section: Event Detectionmentioning

confidence: 99%

“…In EARS we chose to employ a simplified version of the hierarchical algorithm proposed in [10] since it is computationally light and can adapt well to both big and small bursts. An efficient algorithm is necessary because of The detection of a burst is based on the calculation of the frequency (C f req ) of messages in a short-term time window.…”

Section: Event Detectionmentioning

confidence: 99%

EARS (earthquake alert and report system)

Avvenuti

Cresci

Marchetti

et al. 2014

Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

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Social sensing is based on the idea that communities or groups of people can provide a set of information similar to those obtainable from a sensor network. Emergency management is a candidate field of application for social sensing. In this work we describe the design, implementation and deployment of a decision support system for the detection and the damage assessment of earthquakes in Italy. Our system exploits the messages shared in real-time on Twitter, one of the most popular social networks in the world. Data mining and natural language processing techniques are employed to select meaningful and comprehensive sets of tweets. We then apply a burst detection algorithm in order to promptly identify outbreaking seismic events. Detected events are automatically broadcasted by our system via a dedicated Twitter account and by email notifications. In addition, we mine the content of the messages associated to an event to discover knowledge on its consequences. Finally we compare our results with official data provided by the National Institute of Geophysics and Volcanology (INGV), the authority responsible for monitoring seismic events in Italy. The INGV network detects shaking levels produced by the earthquake, but can only model the damage scenario by using empirical relationships. This scenario can be greatly improved with direct information site by site. Results show that the system has a great ability to detect events of a magnitude in the region of 3.5, with relatively low occurrences of false positives. Earthquake detection mostly occurs within seconds of the event and far earlier than the notifications shared by Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from permissions@acm.org.

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“…In telecommunication, a burst of access requests within a short period of time might indicate a DoS attack, which must be protected against immediately. Various methods have been proposed for burst analysis [1][2][3][4][5] and applied to the fields such as blog analysis, 6,7 clustering, 8,9 search 10 and personalization. 11 This work proposes a new burst analysis method that reduces computation by avoiding redundant data aggregation.…”

Section: Introductionmentioning

confidence: 99%

A Real-Time Burst Analysis Method

Nakamura

Oyanagi

2013

Int. J. Artif. Intell. Tools

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A burst is an unusually large number of events occurring within a certain period of time. Various burst analysis methods over multiple window size have been proposed. However, real-time burst analysis is difficult owing to the huge amount of computation. This article proposes a method to analyze a burst in real-time. The proposed method reduces computation by avoiding redundant data updates of no event occurrence, and by suppressing data within a certain period in the case of emergent increase of events. In addition, a level and a rank of a burst can be analyzed simultaneously. The effectiveness of the proposed method is evaluated by experiments with real data. The result shows that the proposed method is more effective in analyzing real data, and more efficient in handling a large amount of events.

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A Real-Time Burst Detection Method

Cited by 21 publications

References 12 publications

Pulling Information from social media in the aftermath of unpredictable disasters

Pulling Information from social media in the aftermath of unpredictable disasters

EARS (earthquake alert and report system)

A Real-Time Burst Analysis Method

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