Twitter-COMMs: Detecting Climate, COVID, and Military Multimodal Misinformation

Giscard Biamby, Grace Luo, Trevor Darrell, Anna Rohrbach


Abstract
Detecting out-of-context media, such as “miscaptioned” images on Twitter, is a relevant problem, especially in domains of high public significance. In this work we aim to develop defenses against such misinformation for the topics of Climate Change, COVID-19, and Military Vehicles. We first present a large-scale multimodal dataset with over 884k tweets relevant to these topics. Next, we propose a detection method, based on the state-of-the-art CLIP model, that leverages automatically generated hard image-text mismatches. While this approach works well on our automatically constructed out-of-context tweets, we aim to validate its usefulness on data representative of the real world. Thus, we test it on a set of human-generated fakes, created by mimicking in-the-wild misinformation. We achieve an 11% detection improvement in a high precision regime over a strong baseline. Finally, we share insights about our best model design and analyze the challenges of this emerging threat.
Anthology ID:
2022.naacl-main.110
Volume:
Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
Month:
July
Year:
2022
Address:
Seattle, United States
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1530–1549
Language:
URL:
https://aclanthology.org/2022.naacl-main.110
DOI:
10.18653/v1/2022.naacl-main.110
Bibkey:
Cite (ACL):
Giscard Biamby, Grace Luo, Trevor Darrell, and Anna Rohrbach. 2022. Twitter-COMMs: Detecting Climate, COVID, and Military Multimodal Misinformation. In Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 1530–1549, Seattle, United States. Association for Computational Linguistics.
Cite (Informal):
Twitter-COMMs: Detecting Climate, COVID, and Military Multimodal Misinformation (Biamby et al., NAACL 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.naacl-main.110.pdf
Code
 giscardbiamby/twitter-comms
Data
Twitter-COMMs