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Found 9 papers, 0 papers with code
VL-BERT+: Detecting Protected Groups in Hateful Multimodal Memes
This paper describes our submission (winning solution for Task A) to the Shared Task on Hateful Meme Detection at WOAH 2021.
Analyzing the Real Vulnerability of Hate Speech Detection Systems against Targeted Intentional Noise
Hate speech detection systems have been shown to be vulnerable against obfuscation attacks, where a potential hater tries to circumvent detection by deliberately introducing noise in their posts.
Text or Image? What is More Important in Cross-Domain Generalization Capabilities of Hate Meme Detection Models?
This paper delves into the formidable challenge of cross-domain generalization in multimodal hate meme detection, presenting compelling findings.
HateProof: Are Hateful Meme Detection Systems really Robust?
Empirically, we find a noticeable performance drop of as high as 10% in the macro-F1 score for certain attacks.
Classification Approaches to Identify Informative Tweets
For users to be able to draw on benefits from news reported on social media it is necessary to reliably identify news content and differentiate it from non-news.
Identification of Good and Bad News on Twitter
We also perform a comparative analysis with sentiments showing that sentiment alone is not enough to distinguish between good and bad news.
LTL-UDE at SemEval-2019 Task 6: BERT and Two-Vote Classification for Categorizing Offensiveness
We present results for Subtask A and C of SemEval 2019 Shared Task 6.
Information Nutrition Labels: A Plugin for Online News Evaluation
This paper discusses the choice of the labels, their implementation and visualization.
