In Brazil and in India, WhatsApp groups were used to bypass mainstream media and speak directly to citizens, spreading disinformation and polarizing voters along moral and religious lines. This chapter aims to identify features and patterns in the content shared that reveal narratives developed by the Bolsonaro campaign to win the Brazilian presidential election. The Brazilian media landscape shows a high concentration of audience and property, lack of transparency, and economic, political and religious interference. The flow of persuasive disinformation depends not only on the technology and its social uses, but also on the media and information ecosystem and the environmental conditions for social influence. We conclude by highlighting several challenges related to multimodal semiotics, technological constraints and non-trivial social engagement, and we present several open-ended aspects such as delineating online harm and empirically examining related frameworks and assistive interventions, which we believe will motivate and drive future research. Another observation is that memes can propagate globally through repackaging in different languages and that they can also be multilingual, blending different cultures. We further find that existing datasets mostly capture multi-class scenarios, which are not inclusive of the affective spectrum that memes can represent. One interesting finding is that many types of harmful memes are not really studied, e.g., such featuring self-harm and extremism, partly due to the lack of suitable datasets. Based on a systematic analysis of recent literature, we first propose a new typology of harmful memes, and then we highlight and summarize the relevant state of the art. With this in mind, here we offer a comprehensive survey with a focus on harmful memes.

Yet, harmful content often combines multiple modalities, as in the case of memes, which are of particular interest due to their viral nature. Researchers have studied textual, visual, and audio content, but typically in isolation.

The automatic identification of harmful content online is of major concern for social media platforms, policymakers, and society.

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