Capturing information needs in disaster situations by using temporal and spatial offset learning (TSOL)
This paper presents a framework for identifying the offline information needs of persons in disaster situations by analyzing online behavioral logs and utilizing the users' location and search history. Two main challenges are addressed: accurately identifying persons most affected by the situat...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-01-01
|
| Series: | Progress in Disaster Science |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2590061724000826 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850044153423986688 |
|---|---|
| author | Kota Tsubouchi Shuji Yamaguchi |
| author_facet | Kota Tsubouchi Shuji Yamaguchi |
| author_sort | Kota Tsubouchi |
| collection | DOAJ |
| description | This paper presents a framework for identifying the offline information needs of persons in disaster situations by analyzing online behavioral logs and utilizing the users' location and search history. Two main challenges are addressed: accurately identifying persons most affected by the situation from noisy location data and distinguishing event-related search queries from unrelated ones. To tackle these challenges, we propose a machine-learning method, called temporal and spatial offset learning (TSOL), that incorporates both temporal and spatial distinctiveness. TSOL assigns heavier weights to these dimensions, in order to offset complexities and uncertainties surrounding user information. We validated the effectiveness of TSOL through experiments in actual disaster situations. The proposed framework and TSOL offer a promising approach to capturing and analyzing the information needs of individuals affected by disasters. The captured information needs in disaster situations have often been reported on TV in Japan as a support of those affected by disasters. |
| format | Article |
| id | doaj-art-2e97564f976144eea706f9a9338738f7 |
| institution | DOAJ |
| issn | 2590-0617 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Progress in Disaster Science |
| spelling | doaj-art-2e97564f976144eea706f9a9338738f72025-08-20T02:55:03ZengElsevierProgress in Disaster Science2590-06172025-01-012510039210.1016/j.pdisas.2024.100392Capturing information needs in disaster situations by using temporal and spatial offset learning (TSOL)Kota Tsubouchi0Shuji Yamaguchi1LY Corporation, 1-3, Kioicho, Chiyoda-ku, 102-8282 Tokyo, JapanCorresponding author.; LY Corporation, 1-3, Kioicho, Chiyoda-ku, 102-8282 Tokyo, JapanThis paper presents a framework for identifying the offline information needs of persons in disaster situations by analyzing online behavioral logs and utilizing the users' location and search history. Two main challenges are addressed: accurately identifying persons most affected by the situation from noisy location data and distinguishing event-related search queries from unrelated ones. To tackle these challenges, we propose a machine-learning method, called temporal and spatial offset learning (TSOL), that incorporates both temporal and spatial distinctiveness. TSOL assigns heavier weights to these dimensions, in order to offset complexities and uncertainties surrounding user information. We validated the effectiveness of TSOL through experiments in actual disaster situations. The proposed framework and TSOL offer a promising approach to capturing and analyzing the information needs of individuals affected by disasters. The captured information needs in disaster situations have often been reported on TV in Japan as a support of those affected by disasters.http://www.sciencedirect.com/science/article/pii/S2590061724000826Temporal and spatial data miningSearch query analysisDisaster situation |
| spellingShingle | Kota Tsubouchi Shuji Yamaguchi Capturing information needs in disaster situations by using temporal and spatial offset learning (TSOL) Progress in Disaster Science Temporal and spatial data mining Search query analysis Disaster situation |
| title | Capturing information needs in disaster situations by using temporal and spatial offset learning (TSOL) |
| title_full | Capturing information needs in disaster situations by using temporal and spatial offset learning (TSOL) |
| title_fullStr | Capturing information needs in disaster situations by using temporal and spatial offset learning (TSOL) |
| title_full_unstemmed | Capturing information needs in disaster situations by using temporal and spatial offset learning (TSOL) |
| title_short | Capturing information needs in disaster situations by using temporal and spatial offset learning (TSOL) |
| title_sort | capturing information needs in disaster situations by using temporal and spatial offset learning tsol |
| topic | Temporal and spatial data mining Search query analysis Disaster situation |
| url | http://www.sciencedirect.com/science/article/pii/S2590061724000826 |
| work_keys_str_mv | AT kotatsubouchi capturinginformationneedsindisastersituationsbyusingtemporalandspatialoffsetlearningtsol AT shujiyamaguchi capturinginformationneedsindisastersituationsbyusingtemporalandspatialoffsetlearningtsol |