  Harmful or abusive online content has been increasing over time, raising
concerns for social media platforms, government agencies, and policymakers.
Such harmful or abusive content can have major negative impact on society,
e.g., cyberbullying can lead to suicides, rumors about COVID-19 can cause
vaccine hesitance, promotion of fake cures for COVID-19 can cause health harms
and deaths. The content that is posted and shared online can be textual,
visual, or a combination of both, e.g., in a meme. Here, we describe our
experiments in detecting the roles of the entities (hero, villain, victim) in
harmful memes, which is part of the CONSTRAINT-2022 shared task, as well as our
system for the task. We further provide a comparative analysis of different
experimental settings (i.e., unimodal, multimodal, attention, and
augmentation). For reproducibility, we make our experimental code publicly
available. \url{https://github.com/robi56/harmful_memes_block_fusion}
