Research into the classification of Image with Text (IWT) troll memes has recently become popular. Since the online community utilizes the refuge of memes to express themselves, there is an abundance of data in the form of memes. These memes have the potential to demean, harras, or bully targeted individuals. Moreover, the targeted individual could fall prey to opinion manipulation. To comprehend the use of memes in opinion manipulation, we define three specific domains (product, political or others) which we classify into troll or not-troll, with or without opinion manipulation. To enable this analysis, we enhanced an existing dataset by annotating the data with our defined classes, resulting in a dataset of 8,881 IWT or multimodal memes in the English language (TrollsWithOpinion dataset). We perform baseline experiments on the annotated dataset, and our result shows that existing state-of-the-art techniques could only reach a weighted-average F1-score of 0.37. This shows the need for a development of a specific technique to deal with multimodal troll memes.

翻译：由于在线社区利用Memes的庇护来表达自己,因此有大量的Memes数据。这些Memes具有贬低、harras或欺凌目标个人的潜力。此外,目标个人可能会成为操纵舆论的牺牲品。为了理解Memes在舆论操纵中的使用,我们定义了我们分类为巨人或非巨人的三个特定领域(产品、政治或其他),无论是否进行意见操纵。为了进行这一分析,我们通过用我们定义的类别来说明数据,加强了现有数据集,从而产生了一套8 881 IWT或英语的多式模类数据(Trolls withoutOpinion数据集),我们在附加说明数据集上进行了基线实验,我们的结果显示,现有最先进的技术只能达到加权平均F1核心0.37,这表明需要开发一种具体技术来处理多式巨魔。