Image Analysis Enhanced Event Detection from Geo-tagged Tweet Streams Article Swipe
Yi Han
,
Shanika Karunasekera
,
Christopher Leckie
·
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2002.04208
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2002.04208
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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- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2002.04208
- https://arxiv.org/pdf/2002.04208
- OA Status
- green
- References
- 25
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3005782209
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Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3005782209Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2002.04208Digital Object Identifier
- Title
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Image Analysis Enhanced Event Detection from Geo-tagged Tweet StreamsWork title
- Type
-
preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2020Year of publication
- Publication date
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2020-02-11Full publication date if available
- Authors
-
Yi Han, Shanika Karunasekera, Christopher LeckieList of authors in order
- Landing page
-
https://arxiv.org/abs/2002.04208Publisher landing page
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https://arxiv.org/pdf/2002.04208Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
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https://arxiv.org/pdf/2002.04208Direct OA link when available
- Concepts
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Event (particle physics), Computer science, Image (mathematics), Autoencoder, Anomaly detection, Artificial intelligence, Data mining, Pattern recognition (psychology), Cluster analysis, Precision and recall, Statistical hypothesis testing, Deep learning, Statistics, Mathematics, Quantum mechanics, PhysicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
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25Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.rejects | 202 |
| abstract_inverted_index.related | 21 |
| abstract_inverted_index.results | 214 |
| abstract_inverted_index.streams | 5 |
| abstract_inverted_index.trained | 131 |
| abstract_inverted_index.tweets, | 50 |
| abstract_inverted_index.whether | 66 |
| abstract_inverted_index.Although | 28 |
| abstract_inverted_index.analyses | 102 |
| abstract_inverted_index.analysis | 122, 223 |
| abstract_inverted_index.approach | 225 |
| abstract_inverted_index.cluster. | 195 |
| abstract_inverted_index.combines | 82 |
| abstract_inverted_index.compared | 185 |
| abstract_inverted_index.datasets | 165 |
| abstract_inverted_index.detected | 1 |
| abstract_inverted_index.enhanced | 224 |
| abstract_inverted_index.existing | 41 |
| abstract_inverted_index.finding, | 199 |
| abstract_inverted_index.increase | 228 |
| abstract_inverted_index.learning | 92 |
| abstract_inverted_index.millions | 216 |
| abstract_inverted_index.multiple | 164 |
| abstract_inverted_index.performs | 120 |
| abstract_inverted_index.progress | 30 |
| abstract_inverted_index.reliable | 63 |
| abstract_inverted_index.semantic | 99 |
| abstract_inverted_index.separate | 124 |
| abstract_inverted_index.streams, | 39 |
| abstract_inverted_index.textual, | 83 |
| abstract_inverted_index.training | 149, 178 |
| abstract_inverted_index.algorithm | 80, 96, 201 |
| abstract_inverted_index.approach. | 93 |
| abstract_inverted_index.candidate | 191, 204 |
| abstract_inverted_index.clusters, | 109 |
| abstract_inverted_index.detection | 36, 79 |
| abstract_inverted_index.detector, | 138 |
| abstract_inverted_index.efficient | 26 |
| abstract_inverted_index.following | 88 |
| abstract_inverted_index.indicator | 64 |
| abstract_inverted_index.non-event | 194 |
| abstract_inverted_index.precision | 230 |
| abstract_inverted_index.remaining | 153 |
| abstract_inverted_index.response. | 27 |
| abstract_inverted_index.Therefore, | 15 |
| abstract_inverted_index.accidents, | 11 |
| abstract_inverted_index.candidate, | 117 |
| abstract_inverted_index.considered | 45 |
| abstract_inverted_index.difference | 207 |
| abstract_inverted_index.disasters. | 14 |
| abstract_inverted_index.instances. | 160 |
| abstract_inverted_index.threshold. | 212 |
| abstract_inverted_index.autoencoder | 129 |
| abstract_inverted_index.corresponds | 113 |
| abstract_inverted_index.demonstrate | 219 |
| abstract_inverted_index.experiments | 162 |
| abstract_inverted_index.information | 54 |
| abstract_inverted_index.potentially | 59 |
| abstract_inverted_index.significant | 29 |
| abstract_inverted_index.statistical | 84, 101 |
| abstract_inverted_index.Experimental | 213 |
| abstract_inverted_index.information, | 87 |
| abstract_inverted_index.unsupervised | 90 |
| abstract_inverted_index.Specifically, | 94 |
| abstract_inverted_index.convolutional | 128 |
| abstract_inverted_index.significantly | 227 |
| abstract_inverted_index.non-events---a | 127 |
| abstract_inverted_index.reconstruction | 174 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 3 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/13 |
| sustainable_development_goals[0].score | 0.6800000071525574 |
| sustainable_development_goals[0].display_name | Climate action |
| citation_normalized_percentile |