Details
Originalsprache | Englisch |
---|---|
Titel des Sammelwerks | Cross-lingual Event-centric Open Analytics |
Untertitel | Proceedings of the 2nd International Workshop on Cross-lingual Event-centric Open Analytics co-located with the 30th The Web Conference (WWW 2021) |
Seiten | 32-46 |
Seitenumfang | 15 |
Publikationsstatus | Veröffentlicht - 2021 |
Extern publiziert | Ja |
Veranstaltung | 2nd International Workshop on Cross-Lingual Event-Centric Open Analytics, CLEOPATRA 2021 - Virtual, Ljubljana, Slowenien Dauer: 12 Apr. 2021 → … |
Publikationsreihe
Name | CEUR Workshop Proceedings |
---|---|
Herausgeber (Verlag) | CEUR WS |
Band | 2829 |
ISSN (Print) | 1613-0073 |
Abstract
Fake news is a severe problem in social media. In this paper, we present an empirical study on visual, textual, and multimodal models for the tasks of claim, claim check-worthiness, and conspiracy detection, all of which are related to fake news detection. Recent work suggests that images are more influential than text and often appear alongside fake text. To this end, several multimodal models have been proposed in recent years that use images along with text to detect fake news on social media sites like Twitter. However, the role of images is not well understood for claim detection, specifically using transformer-based textual and multimodal models. We investigate state-of-the-art models for images, text (Transformer-based), and multimodal information for four different datasets across two languages to understand the role of images in the task of claim and conspiracy detection.
ASJC Scopus Sachgebiete
- Informatik (insg.)
- Allgemeine Computerwissenschaft
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- BibTex
- RIS
Cross-lingual Event-centric Open Analytics: Proceedings of the 2nd International Workshop on Cross-lingual Event-centric Open Analytics co-located with the 30th The Web Conference (WWW 2021). 2021. S. 32-46 (CEUR Workshop Proceedings; Band 2829).
Publikation: Beitrag in Buch/Bericht/Sammelwerk/Konferenzband › Aufsatz in Konferenzband › Forschung › Peer-Review
}
TY - GEN
T1 - On the role of images for analyzing claims in social media
AU - Cheema, Gullal S.
AU - Hakimov, Sherzod
AU - Müller-Budack, Eric
AU - Ewerth, Ralph
N1 - Funding information: This work was funded by European Union’s Horizon 2020 research and innovation programme under the Marie Sk lodowska-Curie grant agreement no 812997. This work was funded by European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement no 812997.
PY - 2021
Y1 - 2021
N2 - Fake news is a severe problem in social media. In this paper, we present an empirical study on visual, textual, and multimodal models for the tasks of claim, claim check-worthiness, and conspiracy detection, all of which are related to fake news detection. Recent work suggests that images are more influential than text and often appear alongside fake text. To this end, several multimodal models have been proposed in recent years that use images along with text to detect fake news on social media sites like Twitter. However, the role of images is not well understood for claim detection, specifically using transformer-based textual and multimodal models. We investigate state-of-the-art models for images, text (Transformer-based), and multimodal information for four different datasets across two languages to understand the role of images in the task of claim and conspiracy detection.
AB - Fake news is a severe problem in social media. In this paper, we present an empirical study on visual, textual, and multimodal models for the tasks of claim, claim check-worthiness, and conspiracy detection, all of which are related to fake news detection. Recent work suggests that images are more influential than text and often appear alongside fake text. To this end, several multimodal models have been proposed in recent years that use images along with text to detect fake news on social media sites like Twitter. However, the role of images is not well understood for claim detection, specifically using transformer-based textual and multimodal models. We investigate state-of-the-art models for images, text (Transformer-based), and multimodal information for four different datasets across two languages to understand the role of images in the task of claim and conspiracy detection.
KW - 5G
KW - Claim detection
KW - Computer vision
KW - Conspiracy detection
KW - COVID-19
KW - Fake news detection
KW - Multilingual NLP
KW - Multimodal analysis
KW - Transformers
KW - Twitter
UR - http://www.scopus.com/inward/record.url?scp=85103154894&partnerID=8YFLogxK
U2 - 10.48550/arXiv.2103.09602
DO - 10.48550/arXiv.2103.09602
M3 - Conference contribution
AN - SCOPUS:85103154894
T3 - CEUR Workshop Proceedings
SP - 32
EP - 46
BT - Cross-lingual Event-centric Open Analytics
T2 - 2nd International Workshop on Cross-Lingual Event-Centric Open Analytics, CLEOPATRA 2021
Y2 - 12 April 2021
ER -