The presence of non-binary gender individuals in social networks is increasing; however, the relationship between gender and activity within online communities is not well understood and limited by the failures of automated gender recognition algorithms to recognize non-binary individuals. We use natural language processing to investigate individual identity on the Twitter platform, focusing on gender expression as represented by users' chosen pronouns from among 14 different pronoun groups. We find that non-binary groups tend to be more active on the platform, preferring to post messages rather than liking others' messages, compared to binary groups. Additionally, non-binary groups receive more replies, but their messages are reshared and liked less. We also find significant variation in the emotional expressions within non-binary groups. The study highlights the importance of considering gender as a spectrum, rather than a binary, in understanding online interactions and expression.