Spatio-Temporally Exclusive Topic Discovery for Anomalous Event Detection
Spatio-Temporally Exclusive Topic Discovery for Anomalous Event Detection
- 주제(키워드) anomalous event detection , social network analysis , spatio-temporal event analytics
- 발행기관 고려대학교 대학원
- 지도교수 주재걸
- 발행년도 2019
- 학위수여년월 2019. 2
- 유형 Text
- 학위구분 석사
- 학과 대학원 컴퓨터학과(정보대학)
- 세부전공 소프트웨어전공
- 원문페이지 42 p
- 실제URI http://www.dcollection.net/handler/korea/000000083426
- UCI I804:11009-000000083426
- DOI 10.23186/korea.000000083426.11009.0000929
- 본문언어 영어
- 제출원본 000045978879
초록/요약
Understanding newly emerging events or topics associated with a particular region of a given day can provide deep insight on the critical events occurring in highly evolving metropolitan cities. We propose herein a novel topic modeling approach on text documents with spatio-temporal information (e.g., when and where a document was published) such as location-based social media data to discover prevalent topics or newly emerging events with respect to an area and a time point. We consider a map view composed of regular grids or tiles with each showing topic keywords from documents of the corresponding region. To this end, we present a tile-based spatio-temporally exclusive topic modeling approach called STExNMF, based on a novel nonnegative matrix factorization (NMF) technique. STExNMF mainly works based on the two following stages: (1) first running a standard NMF of each tile to obtain general topics of the tile and (2) running a spatio-temporally exclusive NMF on a weighted residual matrix. These topics likely reveal information on newly emerging events or topics of interest within a region. We demonstrate the advantages of our approach using the geo-tagged Twitter data of New York City. We also provide quantitative comparisons in terms of the topic quality, spatio-temporal exclusiveness, topic variation, and qualitative evaluations of our method using several usage scenarios. In addition, we present a fast topic modeling technique of our model by leveraging parallel computing.
more목차
1. Introduction 8
2. Related Works 12
2.1 Discriminative Topic Modeling 12
2.2 Topic Modeling on Social Media 13
2.3 Spatio-Temporal Event Analytics for Social Media 14
3. STExNMF 16
3.1 Initial Topic Modeling on Spatio-Temporal Tiles 17
3.2 Spatio-Temporally Exclusive Topic Modeling 18
3.3 Efficient Algorithm for STExNMF 21
3.4 STExNMF Parallelization 26
4. Experiments 27
4.1 Experimental Setup 27
4.2 Quantitative Comparison 31
4.3 Use Cases for Event Detection 34
5. Conclusion and Future Work 38
References 39
6. References
