<p>As the number of individuals identifying as non-binary gender rises, specifically, younger people under the age of thirty in the United States, there is an interest to study this phenomenon (Brown et al. in The experiences, challenges and hopes of transgender and nonbinary U.S. adults, Pew Research Center, 2022). This study examined discussions by users who posted on non-binary <i>Reddit</i> subreddits to understand the main themes and sentiment in their self-expressed experiences. Using previously untapped modeling approaches on this topic, unsupervised machine learning models, such as topic modeling and sentiment analyses were employed to examine the self-described experiences of non-binary people and delineate their discussion themes. The findings indicated that non-binary people’s posts demonstrated a positive sentiment, and two major discussion themes emerged which included: (1) gender identity and seeking support, and (2) concerns with physical appearance. The findings demonstrated that non-binary individuals would like to express gender identity as they see fit and express their authentic selves to affirm their gender identity. The implications of this research call for wider acceptance of non-binary individuals’ gender identities, greater tolerance, and support.</p>

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Exploring the invisible: examining the experiences of non-binary gendered individuals with unsupervised machine learning and sentiment analysis

  • Ksenia Polson

摘要

As the number of individuals identifying as non-binary gender rises, specifically, younger people under the age of thirty in the United States, there is an interest to study this phenomenon (Brown et al. in The experiences, challenges and hopes of transgender and nonbinary U.S. adults, Pew Research Center, 2022). This study examined discussions by users who posted on non-binary Reddit subreddits to understand the main themes and sentiment in their self-expressed experiences. Using previously untapped modeling approaches on this topic, unsupervised machine learning models, such as topic modeling and sentiment analyses were employed to examine the self-described experiences of non-binary people and delineate their discussion themes. The findings indicated that non-binary people’s posts demonstrated a positive sentiment, and two major discussion themes emerged which included: (1) gender identity and seeking support, and (2) concerns with physical appearance. The findings demonstrated that non-binary individuals would like to express gender identity as they see fit and express their authentic selves to affirm their gender identity. The implications of this research call for wider acceptance of non-binary individuals’ gender identities, greater tolerance, and support.