@inproceedings{4cfdb97e74004e6c8a159ccdce508ad2,
title = "KnowMeme: A knowledge-enriched graph neural network solution to offensive meme detection",
abstract = "This paper studies a critical problem of identifying offensive meme posts on online social media where images are superimposed with deliberately altered or crafted captions to deliver offensive information. Existing solutions often ignore the implicit relationship between visual and textual contents in the meme and their implied meanings, which are critical to accurately detect offensive memes. Two important challenges exist in solving the problem: i) how to effectively incorporate human commonsense knowledge to capture the symbolic or implied meaning of the meme contents that implicitly deliver offensive information? ii) How to accurately identify the cross-modal knowledge-based relations between entities in both the visual and textual content of the meme that jointly insinuate offensive messages? To address the above challenges, we develop KnowMeme, a knowledge-enriched graph neural network solution that leverages knowledge facts from human commonsense knowledge to effectively detect offensive meme posts on online social media. Evaluation results show that KnowMeme achieves significant performance gains compared to the state-of-the-art baseline methods in accurately detecting offensive memes.",
keywords = "Hate Speech, Knowledge Base, Offensive Meme, Online Social Media",
author = "Lanyu Shang and Christina Youn and Yuheng Zha and Yang Zhang and Dong Wang",
note = "Funding Information: ACKNOWLEDGMENT This research is supported in part by the National Science Foundation under Grant No. IIS-2008228, CNS-1845639, CNS-1831669, Army Research Office under Grant W911NF-17-1-0409. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the Army Research Office or the U.S. Government. The U.S. Government is authorized to reproduce and distribute reprints for Government purposes notwithstanding any copyright notation here on. Publisher Copyright: {\textcopyright} 2021 IEEE.; 17th IEEE International Conference on eScience, eScience 2021 ; Conference date: 20-09-2021 Through 23-09-2021",
year = "2021",
month = sep,
doi = "10.1109/eScience51609.2021.00029",
language = "English (US)",
series = "Proceedings - IEEE 17th International Conference on eScience, eScience 2021",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "186--195",
booktitle = "Proceedings - IEEE 17th International Conference on eScience, eScience 2021",
address = "United States",
}