Details
| Original language | English |
|---|---|
| Title of host publication | Cross-lingual Event-centric Open Analytics |
| Subtitle of host publication | Proceedings of the 2nd International Workshop on Cross-lingual Event-centric Open Analytics co-located with the 30th The Web Conference (WWW 2021) |
| Pages | 32-46 |
| Number of pages | 15 |
| Publication status | Published - 2021 |
| Externally published | Yes |
| Event | 2nd International Workshop on Cross-Lingual Event-Centric Open Analytics, CLEOPATRA 2021 - Virtual, Ljubljana, Slovenia Duration: 12 Apr 2021 → … |
Publication series
| Name | CEUR Workshop Proceedings |
|---|---|
| Publisher | CEUR WS |
| Volume | 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.
Keywords
- 5G, Claim detection, Computer vision, Conspiracy detection, COVID-19, Fake news detection, Multilingual NLP, Multimodal analysis, Transformers, Twitter
ASJC Scopus subject areas
- Computer Science(all)
- General Computer Science
Cite this
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- BibTeX
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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. p. 32-46 (CEUR Workshop Proceedings; Vol. 2829).
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › 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 -