Unpacking Neutrality Discourse in Social Work: Exploring Twitter Responses to Anti-Trans Legislation

Background and Purpose:

Considering the anti-trans political climate in some American states and the glaring disparities in transgender health, this study explores language and subsequent sentiments embedded in tweets about transgender communities and anti-trans legislation. Twitter is accessible, and tweets about transgender communities are comments and conversations that can be a doorway to the general population’s unfettered opinion about transgender people and anti-trans legislation. While research articles and news outlets discuss positive and negative opinions about this social debate, we place neutrality under a microscope to scrutinize its structure, components, and use by social workers.

This exploration then examines the differences between social workers, transgender people, and the general population of tweets to contrast neutrality’s structure, components, and use. Understanding neutrality’s structure, components, and use in public discourse can expose the harmful effects of seemingly objective statements and evoke social work actions to recenter trans-affirming knowledge and care.

Methods:

A novel mixed methods approach utilized natural language processing along with traditional quantitative (ANCOVA) and qualitative (thematic) analysis methods on a dataset of over two million tweets about transgender people and anti-trans legislation. Natural language processing is a machine learning approach wherein languages are analyzed for sentiments in the text. Next, utilizing both Queer Theory and Minority Stress Theory as guiding frameworks, ANCOVA were used to compare the sentiments of social workers and the general population in the dataset. Independent and group coding using an iterative analysis strategy (e.g., inductive, open coding) and thematic analysis was then applied to randomly selected neutral Tweets (n= 900). The quantitative and qualitative findings were compared and integrated for a unified explanation of the structure, components, and use of neutrality by social workers.

Results:

Findings indicated that neutral tweets by social workers or transgender people were not significantly different from tweets in the general population. Our findings also indicate that self-identified social workers posting about transgender people on Twitter are maintaining neutrality. Qualitative findings reinforced our quantitative results as three emergent themes were identified regarding neutrality related to anti-trans sentiment: 1) Rigid Construction of Transgender Identities; 2) Wishy Washy: Neutrality Through Indecision, and 3) Neutrality Through the Use of a Reporting Style. These findings suggest a general lack of understanding of gender identity or actions in line with the Social Work Code of Ethics.

Conclusions and Implications:

Knowledge and language construction has been a powerful tool leveraged by LGBTQIA+ communities to address social injustices. Our findings contribute to the social work literature by deepening our understanding of the expression of neutral sentiments in the public sphere, which supports social work educators and practitioners in identifying neutral language and discourse within the classroom and practice settings that negatively impact transgender communities. Relatedly, this study also identifies indicators of neutral discourse to support social work students, educators, and practitioners in building the capacity to actively resist neutrality, challenge complicity and harmful discourse, and promote discourse aligned with the profession's values.