Power-law Verification for Event Detection at Multi-spatial Scales from Geo-tagged Tweet Streams. Article Swipe
Compared with traditional news media, social media nowadays provides a richer and more timely source of news. We are interested in multi-spatial level event detection from geo-tagged tweet streams. Specifically, in this paper we (1) examine the statistical characteristic for the time series of the number of geo-tagged tweets posted from specific regions during a short time interval, e.g., ten seconds or one minute; (2) verify from over thirty datasets that while almost all such time series exhibit self-similarity, those that correspond to events, especially short-term and unplanned outbursts, follow a power-law distribution; (3) demonstrate that these findings can be applied to facilitate event detection from tweet streams---we propose a simple algorithm that only checks the existence of power-law distributions in the time series from tweet streams at multi-spatial scales, without looking into the content of each tweet. Our experiments on multiple datasets show that by considering spatio-temporal statistical distributions of tweets alone, this seemingly naive algorithm achieves comparable results with event detection methods that perform semantic analysis. We further discuss how to integrate the proposed technique into existing algorithms for better performance.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://arxiv.org/pdf/1906.05063.pdf
- OA Status
- green
- References
- 31
- Related Works
- 20
- OpenAlex ID
- https://openalex.org/W2950775036
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W2950775036Canonical identifier for this work in OpenAlex
- Title
-
Power-law Verification for Event Detection at Multi-spatial Scales from Geo-tagged Tweet Streams.Work title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2019Year of publication
- Publication date
-
2019-06-12Full publication date if available
- Authors
-
Yi Han, Shanika Karunasekera, Christopher Leckie, Aaron HarwoodList of authors in order
- Landing page
-
https://arxiv.org/pdf/1906.05063.pdfPublisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://arxiv.org/pdf/1906.05063.pdfDirect OA link when available
- Concepts
-
Computer science, Event (particle physics), Data mining, STREAMS, Series (stratigraphy), Similarity (geometry), Social media, Artificial intelligence, Image (mathematics), Paleontology, Biology, Physics, Quantum mechanics, World Wide Web, Computer networkTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
0Total citation count in OpenAlex
- References (count)
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31Number of works referenced by this work
- Related works (count)
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20Other works algorithmically related by OpenAlex
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| sustainable_development_goals[0].id | https://metadata.un.org/sdg/16 |
| sustainable_development_goals[0].score | 0.7699999809265137 |
| sustainable_development_goals[0].display_name | Peace, Justice and strong institutions |
| citation_normalized_percentile |