The expansion of the social media environment has created its own linguistic realities which involve more colloquial communication and practical employment of language. This research focuses on nominalizations in detail, which are originally formed words that have been changed for a noun role. These nominalizations are examined within the context of Facebook posts. The research aims to discover the various nominalizations used and how often they appear in a large sample of data from Public Facebook Posts Corpora. Computational linguistics opened new fields of study and enabled researchers to study large amounts of data easily, making it easier to identify patterns. Two computational methods of identifying nominalization in a large dataset were tested and evaluated from different aspects. Additionally, machine learning analysis tools were conducted to determine how nominalization affects the complexity and readability of the texts. Accordingly, Flesch-Kincaid Grade Level and Flesch Readability results were studied to identify nominalizations’ effect on the texts. The results present a high density of verbal nominalizations indicating that there is a preference to refer to actions or processes in an abbreviated manner. Instead, the study also explores the semantic and functional properties of nominalizations, discovering their employment for emotions and opinions, focusing on the ideas and informal nature of Facebook. Despite what the readability formulas imply about the relation between nominalization density and text difficulty, the research does not discount the fact that these metrics have their shortcomings, especially when comprehending direct user understanding is not taken under consideration. In conclusion, this study adds to the current literature on nominalizations by identifying their role in the social media discourse and importing important knowledge on the progressive changes in online linguistic usage