Media headlines shape our initial interpretation of news, framing narratives that influence societal engagement with political and social issues. Yet, they often rely on sensationalism and bias to capture readers’ attention.In this paper, we aim to uncover distinct patterns in Italian headline composition, examining how language and framing vary across political leanings. We analyze a dataset of daily Italian newspaper articles from two outlets with opposing political perspectives, anonymized as Newspaper A and Newspaper B. Our study encompasses the entire set of news and a subset of topics (n = 8) likely to contain stereotypes or clickbait headlines identified using a Large Language Model. Our methodology combines (1) a lexicometric analysis to identify characteristic words of each newspaper, and (2) the training of an accurate deep learning classifier (F 1 = 0.84) to learn specific patterns for categorizing headlines into these two perspectives and leveraging explainability techniques to extract and interpret these patterns.Our analysis reveals distinct tonal differences between the two newspapers: Newspaper A generally adopts a more balanced and nuanced approach, while Newspaper B often favors a more direct and sometimes provocative style, especially regarding topics like immigration and social justice. Additionally, Newspaper B’s headlines tend to be brief and punchy, in contrast to the longer, more detailed ones from Newspaper A. Despite these tonal differences, both outlets exhibit similar stereotypical patterns in their coverage, such as consistently emphasizing nationality and group distinctions in ways that can reinforce social stereotypes. This shared tendency suggests that, although their narrative strategies differ, both outlets could contribute to a broader pattern of stereotype reinforcement.

Today’s data-driven systems and official statistics often oversimplify the concept of gender, reducing it to binary data, with far-reaching implications for policy development and equitable access to services. This simplification can lead to misclassification and discrimination against individuals who identify as non-binary.We are working to advance our research in this area to develop new, more equitable approaches that can avoid discrimination based on gender identity. Within this research framework, our primary focus is on mitigating the problem of underrepresentation and, in some cases, the complete absence of non-binary individuals in data collection.With this goal in mind, we present the GINN Gender InclusioNeural Network. This is our first attempt to develop an equitable neural network that accurately identifies gender in a multiclass context and includes individuals whose gender identity does not fall on the binary spectrum. To achieve this goal, we conducted a comprehensive comparative analysis of several fine-tuned neural network models. Our goal was to gain a deep understanding of the crucial distinguishing features in gender identify classification and to highlight the limitation of current methods using explainable AI techniques.The initial results are promising and demonstrate the effectiveness of a fine-tuned EfficientNetB0 model in accurately categorizing images of individuals into their self-reported gender, but we are skeptical about the application in a real-world scenario because of the amount of data available about non-binary people at the moment.