Memes function as containers for their producers' ideologies, often resorting to satire to convey insidious favouritism of men over women. This bias manifests in both Algerian and American memes shared in their respective social networks. The latter remains unexplained, particularly in light of the technological advancement of the modern world. One of the primordial objectives of this doctoral thesis is to raise awareness among social media users about the gendered viral content embedded in memes, which frequently propagate harmful narratives about women and, less often, men. This study adopts a corpus-based, comparative approach with multiple objectives. First, it explores how semiotic resources are used to construct gendered meanings in memes circulating on Algerian and American social networks, focusing on visual analysis supported by linguistic examination. Second, it investigates the visual representation of gender in memes shared on platforms, e.g. Facebook, Instagram, and Pinterest. The analyzed corpus includes one hundred memes, divided equally on fifty memes from each virtual setting, collected over two years (2019-2021). These memes comprise 176 visual elements and 208 linguistic elements, all analyzed to examine gender representation visually and linguistically. The study employs a Multimodal Analysis, including Kress and Van Leuuwen's (2006) Grammar Visual Analysis (GVD) and Fairclough's (2003) Critical Discourse Analysis (CDA) to identify the semiotic resources employed to represent gender. Additionally, it uses a checklist grounded on Lameiras' et al. (2013) Gender Stereotype Model (GSM) to assess the gendered content within the selected memes to identify the existing stereotypes within the selected memes. A cross-cultural comparison is conducted to identify the disparities in visual stereotypical representation of gender between Algerian and American memes. The findings reveal significant differences: Algerian memes predominantly feature men, while American memes more often depict women. Linguistically, women are more visible than men in both samples, highlighting an overlap in gender representation. These results align with Lameiras' et al. GSM and confirm the persistence of stereotypical gender representations in both samples. Nevertheless, adapting this model was essential to address the intricate nature of memes as a digital corpus, particularly their content, which incorporates new structures of stereotypes that reflect contemporary social dynamics in virtual settings. Such representations contribute to the perpetuation of gendered content that extend real-world gender inequalities to digital environments, facilitated by the viral nature of memes as a new form of digital communication. This study emphasizes the need for certain digital literacy and consciousness in consuming and sharing content online. It advocates for measures to control the spread of harmful digital content, which contributes to digital pollution within Algerian and American social networks, ultimately perpetuating societal inequalities.