The research starts from the assumption that digitality has fostered a post-situational order whereby technologies, bodies, and situations are amalgamated anew. This has reshaped human sexuality. Digisexual sociality builds on and requires new sets of skills and know-hows. Hence, in a post-situational era wherein so much of people's sexual conduct is entwined with sexual social media, we need to understand better how digitally mediated interactions are achieved, which sexual scripts guide them, and how digisexual sociality is practiced. However, the scholarship has left the praxeological and interactive dimensions of digital sex little understood, foregrounding instead structural explanations (e.g., sexism, heteronormativity). Consequently, how various digital environments foster specific sexual practices and how sexual interactions are practically achieved remain little understood. The proposed research seeks to amend this lacuna by conducting a computational analysis of data extracted from sexual social media. The research will establish what digital sex is, how people have sex in digital contexts, and the meanings they attach to it. It will demonstrate how people tease and flirt, imagine, get aroused, and do sexual things with or without human and non-human actants. Furthermore, identifying the host of sexual scripts that shape people's SIDCs is crucial for explaining just how sexual selves navigate themselves within digital contexts and how, moreover, digital sexual sociality is coordinated. It will also demonstrate the explanatory power of computational analysis to critical sexuality studies. In this, we offer a threefold contribution:
Preliminary results
A pilot study tested the applicability of computational methods to our research. An IRB-approved sampling, using the Chrome extension X Print Styles, yielded more than 3900 public posts (of which 2800 contain visual images). We conducted an image clustering analysis to identify patterns within interactive visual practices. The analysis resulted in seven clusters which, although gender-sensitive (there are men or women-only clusters), reveal other important scripts of visual self-representations as well. These pertain to bodily hexis (frontal close-ups vs. standing farther away, showing room surrounding; specific shown body parts vs. partly dressed; ways of securing interactant's privacy, etc.). Overall, the seven clusters indicate that there are distinct patterns of sexual self-exhibiting that draw on shared scripts of appropriateness. For example, whereas dick pics (male frontal close-ups) are the most common type of sexual exchange, indicating a general masculine script of self-exposure, they are still the highest among queer men. This trend changes when controlling for gender (men), where the highest rate of sharing dick pics is found among sex workers, and it is lower (and equal) for both homosexual and heterosexual interactants.