  The spread of fake news, propaganda, misinformation, disinformation, and
harmful content online raised concerns among social media platforms, government
agencies, policymakers, and society as a whole. This is because such harmful or
abusive content leads to several consequences to people such as physical,
emotional, relational, and financial. Among different harmful content
\textit{trolling-based} online content is one of them, where the idea is to
post a message that is provocative, offensive, or menacing with an intent to
mislead the audience. The content can be textual, visual, a combination of
both, or a meme. In this study, we provide a comparative analysis of
troll-based memes classification using the textual, visual, and multimodal
content. We report several interesting findings in terms of code-mixed text,
multimodal setting, and combining an additional dataset, which shows
improvements over the majority baseline.
