Abstract The increased prevalence of misinformation and inflammatory rhetoric online has amplified polarization on social media platforms in the United States, propelling a feedback loop resulting in the erosion of democratic norms. We conducted a study assessing how a social media platform employing appointed moderators would impact the polarization of its users compared to a peer-based digital jury moderation system, which may be better able to harness community knowledge and cultural nuances while fostering a sense of inclusion and trust in the moderation process. Although our study did not observe a significant impact on the polarization of moderators or users, moderators on average viewed the system as just, legitimate and effective at reducing harmful content. Furthermore, there were no significant differences between user perceptions of the content they were shown from either system, indicating that implementing such a peer-based system has the benefit of providing users agency in platform governance without adversely impacting user experience.

Technology advances and lower equipment costs are enabling non-invasive, convenient recording of brain data outside of clinical settings in more real-world environments, and by non-experts. Despite the growing interest in and availability of brain signal datasets, most analytical tools are made for experts in the specific device technology, and have rigid constraints on the type of analysis available. We developed BrainEx to support interactive exploration and discovery within brain signals datasets. BrainEx takes advantage of algorithms that enable fast exploration of complex, large collections of time series data, while being easy to use and learn. This system enables researchers to perform similarity search, explore feature data and natural clustering, and select sequences of interest for future searches and exploration, while also maintaining the usability of a visual tool. In addition to describing the distributed architecture and visual design for BrainEx, this paper reports on a benchmark experiment showing that it outperforms other existing systems for similarity search. Additionally, we report on a preliminary user study in which domain experts used the visual exploration interface and affirmed that it meets the requirements. Finally, it presents a case study using BrainEx to explore real-world, domain-relevant data.

In human-computer interaction (HCI), there has been a push towards open science, but to date, this has not happened consistently for HCI research utilizing brain signals due to unclear guidelines to support reuse and reproduction. To understand existing practices in the field, this paper examines 110 publications, exploring domains, applications, modalities, mental states and processes, and more. This analysis reveals variance in how authors report experiments, which creates challenges to understand, reproduce, and build on that research. It then describes an overarching experiment model that provides a formal structure for reporting HCI research with brain signals, including definitions, terminology, categories, and examples for each aspect. Multiple distinct reporting styles were identified through factor analysis and tied to different types of research. The paper concludes with recommendations and discusses future challenges. This creates actionable items from the abstract model and empirical observations to make HCI research with brain signals more reproducible and reusable.