Details
| Originalsprache | Englisch |
|---|---|
| Titel des Sammelwerks | Advances in Information Retrieval |
| Untertitel | 45th European Conference on Information Retrieval, ECIR 2023, Dublin, Ireland, April 2–6, 2023, Proceedings, Part II |
| Herausgeber/-innen | Jaap Kamps, Lorraine Goeuriot, Fabio Crestani, Maria Maistro, Hideo Joho, Brian Davis, Cathal Gurrin, Annalina Caputo, Udo Kruschwitz |
| Herausgeber (Verlag) | Springer Science and Business Media Deutschland GmbH |
| Seiten | 204-220 |
| Seitenumfang | 17 |
| ISBN (elektronisch) | 978-3-031-28238-6 |
| ISBN (Print) | 9783031282379 |
| Publikationsstatus | Veröffentlicht - 17 März 2023 |
| Veranstaltung | 45th European Conference on Information Retrieval, ECIR 2023 - Dublin, Irland Dauer: 2 Apr. 2023 → 6 Apr. 2023 |
Publikationsreihe
| Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
|---|---|
| Band | 13981 LNCS |
| ISSN (Print) | 0302-9743 |
| ISSN (elektronisch) | 1611-3349 |
Abstract
The widespread growth of multimodal news requires sophisticated approaches to interpret content and relations of different modalities. Images are of utmost importance since they represent a visual gist of the whole news article. For example, it is essential to identify the locations of natural disasters for crisis management or to analyze political or social events across the world. In some cases, verifying the location(s) claimed in a news article might help human assessors or fact-checking efforts to detect misinformation, i.e., fake news. Existing methods for geolocation estimation typically consider only a single modality, e.g., images or text. However, news images can lack sufficient geographical cues to estimate their locations, and the text can refer to various possible locations. In this paper, we propose a novel multimodal approach to predict the geolocation of news photos. To enable this approach, we introduce a novel dataset called Multimodal Geolocation Estimation of News Photos (MMG-NewsPhoto). MMG-NewsPhoto is, so far, the largest dataset for the given task and contains more than half a million news texts with the corresponding image, out of which 3000 photos were manually labeled for the photo geolocation based on information from the image-text pairs. For a fair comparison, we optimize and assess state-of-the-art methods using the new benchmark dataset. Experimental results show the superiority of the multimodal models compared to the unimodal approaches.
ASJC Scopus Sachgebiete
- Mathematik (insg.)
- Theoretische Informatik
- Informatik (insg.)
- Allgemeine Computerwissenschaft
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Advances in Information Retrieval : 45th European Conference on Information Retrieval, ECIR 2023, Dublin, Ireland, April 2–6, 2023, Proceedings, Part II. Hrsg. / Jaap Kamps; Lorraine Goeuriot; Fabio Crestani; Maria Maistro; Hideo Joho; Brian Davis; Cathal Gurrin; Annalina Caputo; Udo Kruschwitz. Springer Science and Business Media Deutschland GmbH, 2023. S. 204-220 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Band 13981 LNCS).
Publikation: Beitrag in Buch/Bericht/Sammelwerk/Konferenzband › Aufsatz in Konferenzband › Forschung › Peer-Review
}
TY - GEN
T1 - Multimodal Geolocation Estimation of News Photos
AU - Tahmasebzadeh, Golsa
AU - Hakimov, Sherzod
AU - Ewerth, Ralph
AU - Müller-Budack, Eric
N1 - Funding Information: research and innovation program under the Marie Sk lodowska-Curie grant agreement no. 812997 (CLEOPATRA ITN), and by the Ministry of Lower Saxony for Science and Culture (Responsible AI in digital society, project no. 51171145). Funding Information: This work was partially funded by the EU Horizon 2020 research and innovation program under the Marie Sk̷lodowska-Curie grant agreement no. 812997 (CLEOPATRA ITN), and by the Ministry of Lower Saxony for Science and Culture (Responsible AI in digital society, project no. 51171145). Funding Information: Acknowledgements. This work was partially funded by the EU Horizon 2020
PY - 2023/3/17
Y1 - 2023/3/17
N2 - The widespread growth of multimodal news requires sophisticated approaches to interpret content and relations of different modalities. Images are of utmost importance since they represent a visual gist of the whole news article. For example, it is essential to identify the locations of natural disasters for crisis management or to analyze political or social events across the world. In some cases, verifying the location(s) claimed in a news article might help human assessors or fact-checking efforts to detect misinformation, i.e., fake news. Existing methods for geolocation estimation typically consider only a single modality, e.g., images or text. However, news images can lack sufficient geographical cues to estimate their locations, and the text can refer to various possible locations. In this paper, we propose a novel multimodal approach to predict the geolocation of news photos. To enable this approach, we introduce a novel dataset called Multimodal Geolocation Estimation of News Photos (MMG-NewsPhoto). MMG-NewsPhoto is, so far, the largest dataset for the given task and contains more than half a million news texts with the corresponding image, out of which 3000 photos were manually labeled for the photo geolocation based on information from the image-text pairs. For a fair comparison, we optimize and assess state-of-the-art methods using the new benchmark dataset. Experimental results show the superiority of the multimodal models compared to the unimodal approaches.
AB - The widespread growth of multimodal news requires sophisticated approaches to interpret content and relations of different modalities. Images are of utmost importance since they represent a visual gist of the whole news article. For example, it is essential to identify the locations of natural disasters for crisis management or to analyze political or social events across the world. In some cases, verifying the location(s) claimed in a news article might help human assessors or fact-checking efforts to detect misinformation, i.e., fake news. Existing methods for geolocation estimation typically consider only a single modality, e.g., images or text. However, news images can lack sufficient geographical cues to estimate their locations, and the text can refer to various possible locations. In this paper, we propose a novel multimodal approach to predict the geolocation of news photos. To enable this approach, we introduce a novel dataset called Multimodal Geolocation Estimation of News Photos (MMG-NewsPhoto). MMG-NewsPhoto is, so far, the largest dataset for the given task and contains more than half a million news texts with the corresponding image, out of which 3000 photos were manually labeled for the photo geolocation based on information from the image-text pairs. For a fair comparison, we optimize and assess state-of-the-art methods using the new benchmark dataset. Experimental results show the superiority of the multimodal models compared to the unimodal approaches.
KW - Information retrieval
KW - Multimodal photo geolocalization
KW - News analytics
UR - http://www.scopus.com/inward/record.url?scp=85150957809&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-28238-6_14
DO - 10.1007/978-3-031-28238-6_14
M3 - Conference contribution
AN - SCOPUS:85150957809
SN - 9783031282379
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 204
EP - 220
BT - Advances in Information Retrieval
A2 - Kamps, Jaap
A2 - Goeuriot, Lorraine
A2 - Crestani, Fabio
A2 - Maistro, Maria
A2 - Joho, Hideo
A2 - Davis, Brian
A2 - Gurrin, Cathal
A2 - Caputo, Annalina
A2 - Kruschwitz, Udo
PB - Springer Science and Business Media Deutschland GmbH
T2 - 45th European Conference on Information Retrieval, ECIR 2023
Y2 - 2 April 2023 through 6 April 2023
ER -