DisinfoMeme: A Multimodal Dataset for Detecting Meme Intentionally Spreading Out Disinformation

• URL: http://arxiv.org/abs/2205.12617v1
• Date: Wed, 25 May 2022 09:54:59 GMT
• Title: DisinfoMeme: A Multimodal Dataset for Detecting Meme Intentionally Spreading Out Disinformation
• Authors: Jingnong Qu, Liunian Harold Li, Jieyu Zhao, Sunipa Dev, Kai-Wei Chang
• Abstract summary: We present DisinfoMeme to help detect disinformation memes. The dataset contains memes mined from Reddit covering three current topics: the COVID-19 pandemic, the Black Lives Matter movement, and veganism/vegetarianism.
• Score: 72.18912216025029
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Disinformation has become a serious problem on social media. In particular, given their short format, visual attraction, and humorous nature, memes have a significant advantage in dissemination among online communities, making them an effective vehicle for the spread of disinformation. We present DisinfoMeme to help detect disinformation memes. The dataset contains memes mined from Reddit covering three current topics: the COVID-19 pandemic, the Black Lives Matter movement, and veganism/vegetarianism. The dataset poses multiple unique challenges: limited data and label imbalance, reliance on external knowledge, multimodal reasoning, layout dependency, and noise from OCR. We test multiple widely-used unimodal and multimodal models on this dataset. The experiments show that the room for improvement is still huge for current models.

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