Gender-Based Analysis of New Year Resolutions on Twitter: A Clustering Approach Using K-means Algorithm

Abstract

This study explores gender-based patterns in New Year resolutions shared on Twitter, focusing on differences in resolution content and social media engagement. By analyzing a dataset of 5,011 tweets categorized by user gender, resolution theme, and retweet count, the research aims to uncover how men and women express and share their personal goals in digital spaces. The study employs text preprocessing techniques to clean and normalize tweet texts, followed by Term Frequency-Inverse Document Frequency (TF-IDF) vectorization to convert textual data into numerical features. K-means clustering is then applied to group tweets into five thematic clusters, representing distinct resolution topics. The optimal number of clusters is determined using the Elbow Method, with clustering quality assessed via inertia and Silhouette Score metrics. Results reveal significant gender differences in both resolution categories and engagement patterns. For instance, female users tend to post more about health and fitness or humor-related resolutions, whereas male users show a higher presence in philanthropic and finance-related topics. Although the majority of tweets cluster into broad general resolution themes, specialized clusters reflect focused goal areas like quitting habits and financial improvement. Retweet engagement varies widely, with a skewed distribution where most tweets receive minimal retweets, but select tweets achieve high virality. Gender distributions within clusters were relatively balanced, though some clusters displayed subtle dominance by one gender, highlighting nuanced differences in thematic focus. The study’s findings offer valuable insights into digital self-expression and social sharing behaviors influenced by gender, contributing to the understanding of online social dynamics in the digital society. These insights have practical implications for content personalization, enabling brands and influencers to tailor New Year resolution-related campaigns to gender-specific preferences. Limitations include potential biases in data selection and categorization challenges, suggesting avenues for future research such as longitudinal trend analysis and intersectional demographic studies. Overall, this research advances knowledge on gendered digital communication, highlighting the role of social media in reflecting and shaping personal goal-setting behaviors.