Authors - Rani S. Lande, Amol P. Bhagat, Priti A. Khodke Abstract - Visual memes have become a pervasive form of communication in digital spaces, presenting a unique challenge and opportunity for content analysis due to their blend of visual, textual, and often humorous elements. This paper reviews and synthesizes methodologies employed in the analysis of visual memes, aiming to provide a comprehensive overview of current practices and future directions. The methodologies discussed encompass a range of approaches, including qualitative, quantitative, and mixed-methods strategies. Qualitative methods delve into semiotic analysis, exploring how visual and textual components interact to convey meaning and cultural references. Quantitative approaches employ computational tools to analyze large datasets, focusing on metrics such as image recognition, sentiment analysis, and virality metrics. Mixed-methods studies combine these approaches to offer nuanced insights into the multifaceted nature of visual memes. Challenges in visual memes content analysis include the rapid evolution of meme formats, cultural context sensitivity, and the ethical implications of meme reuse and modification. Additionally, the paper explores emerging trends such as deep learning techniques for image recognition and natural language processing for text analysis within memes. By synthesizing these methodologies, this paper aims to provide researchers and practitioners with a foundational understanding of how to effectively analyze visual memes, highlighting opportunities for interdisciplinary research and applications in fields ranging from communication studies to digital humanities and beyond.
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