TrioVecEvent

Zhang, Chao; Liu, Liyuan; Lei, Dongming; Yuan, Quan; Zhuang, Honglei; Hanratty, Timothy; Han, Jiawei

doi:10.1145/3097983.3098027

Cited by 103 publications

(10 citation statements)

References 39 publications

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“…The following two methods are chosen as the baselines: (1) Geoburst [28], a widely cited event detection algorithm that considers temporal, spatial and semantic information. Although improved versions exist (Geoburst+ [26], TrioVec [27]), we do not use them as baselines in this work as they are supervised approaches, while both Geoburst and our method use unsupervised approaches;…”

Section: Methodsmentioning

confidence: 99%

“…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%

“…Note also that since the ground truth of the three datasets are not given, it is difficult to calculate the true recall. Therefore, we adopt a similar approach as in [26,27] and calculate the pseudo recall = N true /N total , where N true is the number of true events detected by a method, and N total is the number of true Insert t into the current node; 10 Embedding…”

Section: Quantitative 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%

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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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Section: Methodsmentioning

confidence: 99%

Section: Autoencoder Based Image Analysismentioning

confidence: 99%

Section: Quantitative Analysismentioning

confidence: 99%

Section: Clustering Based Event Detectionmentioning

confidence: 99%

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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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“…Moreover, it requires the involved meta-paths to be specified as input, while our method is completely unsupervised and can automatically select aspect using statistics of the given HIN. Embedding in the context of HIN has also been studied to address various application tasks with additional supervision [3, 8, 11, 26, 27]. These methods either yield features specific to given tasks or do not generate node features, and therefore fall outside of the scope of unsupervised HIN embedding that we study.…”

Section: Related Workmentioning

confidence: 99%

AspEm: Embedding Learning by Aspects in Heterogeneous Information Networks

Shi¹,

Gui²,

Zhu³

et al. 2018

Proceedings of the 2018 SIAM International Conference on Data Mining

Self Cite

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Heterogeneous information networks (HINs) are ubiquitous in real-world applications. Due to the heterogeneity in HINs, the typed edges may not fully align with each other. In order to capture the semantic subtlety, we propose the concept of aspects with each aspect being a unit representing one underlying semantic facet. Meanwhile, network embedding has emerged as a powerful method for learning network representation, where the learned embedding can be used as features in various downstream applications. Therefore, we are motivated to propose a novel embedding learning framework—ASPEM—to preserve the semantic information in HINs based on multiple aspects. Instead of preserving information of the network in one semantic space, ASPEM encapsulates information regarding each aspect individually. In order to select aspects for embedding purpose, we further devise a solution for ASPEM based on dataset-wide statistics. To corroborate the efficacy of ASPEM, we conducted experiments on two real-words datasets with two types of applications—classification and link prediction. Experiment results demonstrate that ASPEM can outperform baseline network embedding learning methods by considering multiple aspects, where the aspects can be selected from the given HIN in an unsupervised manner.

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