  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.
