From 'Friends with Benefits' to 'Sextortion:' A Nuanced Investigation of Adolescents' Online Sexual Risk Experiences

Abstract

Sexual exploration is a natural part of adolescent development; yet, unmediated internet access has enabled teens to engage in a wider variety of potentially riskier sexual interactions than previous generations, from normatively appropriate sexual interactions to sexually abusive situations. Teens have turned to online peer support platforms to disclose and seek support about these experiences. Therefore, we analyzed posts (N=45,955) made by adolescents (ages 13--17) on an online peer support platform to deeply examine their online sexual risk experiences. By applying a mixed methods approach, we 1) accurately (average of AUC = 0.90) identified posts that contained teen disclosures about online sexual risk experiences and classified the posts based on level of consent (i.e., consensual, non-consensual, sexual abuse) and relationship type (i.e., stranger, dating/friend, family) between the teen and the person in which they shared the sexual experience, 2) detected statistically significant differences in the proportions of posts based on these dimensions, and 3) further unpacked the nuance in how these online sexual risk experiences were typically characterized in the posts. Teens were significantly more likely to engage in consensual sexting with friends/dating partners; unwanted solicitations were more likely from strangers and sexual abuse was more likely when a family member was involved. We contribute to the HCI and CSCW literature around youth online sexual risk experiences by moving beyond the false dichotomy of "safe" versus "risky". Our work provides a deeper understanding of technology-mediated adolescent sexual behaviors from the perspectives of sexual well-being, risk detection, and the prevention of online sexual violence toward youth.

Wrist-bound Guanxi, Jiazu, and Kuolie: Unpacking Chinese Adolescent Smartwatch-Mediated Socialization

“…Smartwatches also make adolescents feel "tethered" [49], particularly evident among dalao in biaoquan. The negative impacts of online socialization on adolescents, as documented in prior research, are also apparent in smartwatch-based interactions, including cyberbullying such as sexting [56,108], harassment [5], and drama [85]. Meanwhile, some findings complement prior research, reflected in the unique affordances of smartwatches and possibly cultural differences among Chinese users, which will be further discussed in Section 6.2 and Section 6.3.…”

“…During interview, 12 participants showcased various smartwatch content, including its basic functions, personal profiles, and Friend Circle. 9 participants provided more detailed content, including screenshots of features like personal profiles and chat records, as well as posters originating from smartwatch socialization and content directly related to smartwatch interactions on other platforms (e.g., Wechat 4 , Xiaohongshu 5 , and Douyin 6 ). These data Table 1: Basic information summary of participants interviewed.…”

“…Wechat is a Chinese instant messaging, social media, and mobile payment app5 Xiaohongshu is a Chinese social media and e-commerce platform. It sometimes referred to as "Chinese Instagram"6 Douyin is a Chinese counterpart of TikTok…”

Shortchanged: Uncovering and Analyzing Intimate Partner Financial Abuse in Consumer Complaints

“…Obtaining such first-hand insights into the financial actions of vulnerable service users to inform our gaps in knowledge about IPFA and digital systems is, understandably, challenging. For many consumers, the mere discussion of finances can be culturally taboo [10]; out of fear of manipulation by others [2], or by experiencing embarrassment or shame [106]. Financial institutions also go to great lengths to maintain customer confidentiality about information relating to their finances to both prevent fraud and protect against the disclosure of proprietary information.…”

“…Deep learning and natural language processing have been used to parse and analyze large datasets, which have enabled the inference of otherwise inaccessible characteristics [4,83,87]. Scholars have leveraged these approaches to elicit risk factors to vulnerable groups, contextualize taboo topic areas (e.g., end-of-life [111], climate change [38]), and identify under-recognized forms of sociotechnical harms [2,53]. In the context of abuse and hate, Bidirectional Encoder Representations from Transformers (BERT)-based transformer models have been deployed to detect hate speech online [63,110], identify descriptions of abuse in patient records [18], and detect abusive transactions [66].…”