Social virtual worlds allow immersive interactive experiences mediated by an avatar, the embodiment of one’s virtual identity. Consequently, avatar customization is of great importance for users, but excessive control and ownership practices over in-world assets by traditional platform operators have given way to less centralized alternatives. Herein, I present the case study of content generation for avatar customization on Decentraland, a blockchain-based social virtual world touted to be built and owned by its community, in a decentralized and transparent manner. The study follows a mixed-methods approach to analyze four years of data from different sources (e.g., asset metadata, community vote results, forum posts, etc.) to investigate the governance, creation experience, and production of avatar collectibles (wearables and emotes) on Decentraland. Findings indicate that: content governance is still mainly driven by the platform operator and a few privileged users; financial barriers and cumbersome onboarding deter potential creators; and there has been a marked decline in content creation, reflecting a decreased interest in Decentraland over time.

AI-generated counterspeech offers a promising and scalable strategy to curb online toxicity through direct replies that promote civil discourse. However, current counterspeech is one-size-fits-all, lacking adaptation to the moderation context and the users involved. We propose and evaluate multiple strategies for generating tailored counterspeech that is adapted to the moderation context and personalized for the moderated user. We instruct a LLaMA2-13B model to generate counterspeech, experimenting with various configurations based on different contextual information and fine-tuning strategies. We identify the configurations that generate persuasive counterspeech through a combination of quantitative indicators and human evaluations collected via a pre-registered mixed-design crowdsourcing experiment. Results show that contextualized counterspeech can significantly outperform state-of-the-art generic counterspeech in adequacy and persuasiveness, without compromising other characteristics. Our findings also reveal a poor correlation between quantitative indicators and human evaluations, suggesting that these methods assess different aspects and highlighting the need for nuanced evaluation methodologies. The effectiveness of contextualized AI-generated counterspeech and the divergence between human and algorithmic evaluations underscore the importance of increased human-AI collaboration in content moderation.

Since September 2023, the Digital Services Act (DSA) obliges large online platforms to submit detailed data on each moderation action they take within the European Union (EU) to the DSA Transparency Database. From its inception, this centralized database has sparked scholarly interest as an unprecedented and potentially unique trove of data on real-world online moderation. Here, we thoroughly analyze all 353.12M records submitted by the eight largest social media platforms in the EU during the first 100 days of the database. Specifically, we conduct a platform-wise comparative study of their: volume of moderation actions, grounds for decision, types of applied restrictions, types of moderated content, timeliness in undertaking and submitting moderation actions, and use of automation. Furthermore, we systematically cross-check the contents of the database with the platforms' own transparency reports. Our analyses reveal that (i) the platforms adhered only in part to the philosophy and structure of the database, (ii) the structure of the database is partially inadequate for the platforms' reporting needs, (iii) the platforms exhibited substantial differences in their moderation actions, (iv) a remarkable fraction of the database data is inconsistent, (v) the platform X (formerly Twitter) presents the most inconsistencies. Our findings have far-reaching implications for policymakers and scholars across diverse disciplines. They offer guidance for future regulations that cater to the reporting needs of online platforms in general, but also highlight opportunities to improve and refine the database itself.

The subreddit r/The_Donald was repeatedly denounced as a toxic and misbehaving online community, reasons for which it faced a sequence of moderation interventions by Reddit administrators. It was quarantined in June 2019, restricted in February 2020, and finally banned in June 2020, but despite precursory work on the matter, the effects of this sequence of interventions are still unclear. In this work, we follow a multidimensional causal inference approach, with data containing more than 15M posts made in a time frame of 2 years, to examine the effects of such interventions inside and outside of the subreddit. We find that the interventions greatly reduced the activity of problematic users. However, the interventions also caused an increase in toxicity and led users to share more polarized and less factual news. In addition, the restriction had stronger effects than the quarantine, and core users of r/The_Donald suffered stronger effects than the rest of users. Overall, our results provide evidence that the interventions had mixed effects and paint a nuanced picture of the consequences of community-level moderation strategies. We conclude by reflecting on the challenges of policing online platforms and on the implications for the design and deployment of moderation interventions.

This work aims to promote musical learning in order to ease the inclusion of students with autism in the classroom and during musical performances. To this end, a web application was developed using a user-centered design approach involving professionals experienced in training people with autism, to help end users learn the basic concepts of music. The app allows teachers or caregivers to set up a customized learning environment according to each student's needs, so that these students can play songs in collaboration with classmates. Preliminary results suggest a key role for technology in facilitating music teaching and in supporting the learning process for students with autism.

The menopause transition is associated with physiological changes that increase women's cardiovascular and metabolic risk. Healthier and more conscious behavior in specific areas (diet, physical activity, smoking, etc.) can mitigate this risk. However, in order to modify such behavior, women must be aware of the need to use endogenous and exogenous strategies to improve their lifestyle habits. Despite the explosion of health-related apps, there are currently no innovative examples addressing a self-care approach to menopause by applying personalization, adaptability, and persuasion to induce women to improve their health-related lifestyle. Therefore, this paper describes the theoretical foundations and design phases of a system that coaches women to improve their lifestyle during menopause. To this end, we used a Participatory Design approach involving different groups of women along the different design phases to define the system's user interfaces and interaction mechanisms.

In this report, we share our experience and observations on the challenges blind people face with text entry on touch-based mobile phones, particularly from the perspective of one of the authors, who is blind. To better understand these issues we developed and tested Multimodal Text Input Touchscreen Keypad (MTITK), an audio-tactile text entry prototype based on multitap, which relies on a telephone keypad layout organized into five key groups with distinct audio-tactile feedback. Users explore the screen to identify the current selected key, tap to enter text, and gesture to edit it, while receiving the corresponding voice, audio, and tactile feedback; no additional equipment is necessary in our software-only approach. We implemented a prototype on Android and tested its usability with visually impaired participants; they welcomed its multimodality and the familiar layout, but also expressed the need to increase vibration pattern differentiation and refine the character selection mechanism.