Real-time event detection from the Twitter data stream using the TwitterNews+ Framework

Hasan, Mahmud; Orgun, Mehmet A.; Schwitter, Rolf

doi:10.1016/j.ipm.2018.03.001

Cited by 141 publications

(101 citation statements)

References 9 publications

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“…Additionally, we utilize a list of common suffixes of location names to recognize locations. The suffix list -a part of which is shown in Table 4 -comprises different naming conventions for landforms 13 , roads 14 15 , buildings 16 and towns.…”

Section: Extracting Geographical Locationsmentioning

confidence: 99%

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Utilizing microblogs for assisting post-disaster relief operations via matching resource needs and availabilities

Dutt

Basu

Ghosh

et al. 2019

Information Processing & Management

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During a disaster event, two types of information that are especially useful for coordinating relief operations are needs and availabilities of resources (e.g., food, water, medicines) in the affected region. Information posted on microblogging sites is increasingly being used for assisting post-disaster relief operations. In this context, two practical challenges are (i) to identify tweets that inform about resource needs and availabilities (termed as need-tweets and availability-tweets respectively), and (ii) to automatically match needs with appropriate availabilities. While several works have addressed the first problem, there has been little work on automatically matching needs with availabilities. The few prior works that attempted matching only considered the resources, and no attempt has been made to understand other aspects of needs/availabilities that are essential for matching in practice. In this work, we develop a methodology for understanding five important aspects of need-tweets and availability-tweets, including what resource and what quantity is needed/available, the geographical location of the need/availability, and who needs / is providing the resource. Understanding these aspects helps us to address the need-availability matching problem considering not only the resources, but also other factors such as the geographical proximity between the need and the availability. To the best of our knowledge, this study is the first attempt to develop methods for understanding the semantics of need-tweets and availability-tweets. We also develop a novel methodology for matching needtweets with availability-tweets, considering both resource similarity and geographical proximity. Experiments on two datasets corresponding to two disaster events, demonstrate that our proposed methods perform substantially better matching than those in prior works. Additionally, our proposed methodologies are reusable across different types of disaster events.

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Section: Extracting Geographical Locationsmentioning

confidence: 99%

“…https://en.wikipedia.org/wiki/List of landforms 14 https://wiki.waze.com/wiki/India/Editing/Roads 15 http://www.haringey.gov.uk16 https://en.wikipedia.org/wiki/List of building types…”

mentioning

confidence: 99%

Utilizing microblogs for assisting post-disaster relief operations via matching resource needs and availabilities

Dutt

Basu

Ghosh

et al. 2019

Information Processing & Management

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“…A common type of solution to the above problem takes the clustering based approach [3,10,12,[25][26][27][28], which generates a list of event candidates by clustering the tweets according to their semantic, spatial and temporal information, and then removes non-event clusters via supervised or unsupervised methods. In this work, we focus on how image analysis can be used to enhance the second step.…”

Section: Autoencoder Based Image Analysismentioning

confidence: 99%

“…This type of detection method takes a two-step approach [3,4,10,12,22,23,[25][26][27][28]. First, tweets are clustered based on their temporal, spatial, semantic, frequency and user information.…”

Section: Clustering Based Event Detectionmentioning

confidence: 99%

Image Analysis Enhanced Event Detection from Geo-Tagged Tweet Streams

Han

Karunasekera

Leckie

2020

Advances in Knowledge Discovery and Data Mining

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Events detected from social media streams often include early signs of accidents, crimes or disasters. Therefore, they can be used by related parties for timely and efficient response. Although significant progress has been made on event detection from tweet streams, most existing methods have not considered the posted images in tweets, which provide richer information than the text, and potentially can be a reliable indicator of whether an event occurs or not. In this paper, we design an event detection algorithm that combines textual, statistical and image information, following an unsupervised machine learning approach. Specifically, the algorithm starts with semantic and statistical analyses to obtain a list of tweet clusters, each of which corresponds to an event candidate, and then performs image analysis to separate events from non-events-a convolutional autoencoder is trained for each cluster as an anomaly detector, where a part of the images are used as the training data and the remaining images are used as the test instances. Our experiments on multiple datasets verify that when an event occurs, the mean reconstruction errors of the training and test images are much closer, compared with the case where the candidate is a non-event cluster. Based on this finding, the algorithm rejects a candidate if the difference is larger than a threshold. Experimental results over millions of tweets demonstrate that this image analysis enhanced approach can significantly increase the precision with minimum impact on the recall.

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“…This special issue covers a wide range of applications that rely on real-time processing of social media, including event detection [8,14,7], cybersecurity [10], opinion mining [5] and automatic geo-localization [7]. The diversity of submissions received shows the need for furthering research in processing social media streams in a (near) real-time.…”

Section: Conclusion and Future Research Directionsmentioning

confidence: 99%