In-game team communication in online multiplayer games has shown the potential to foster efficient collaboration and positive social interactions. Yet players often associate communication within ad hoc teams with frustration and wariness. Though previous works have quantitatively analyzed communication patterns at scale, few have identified the motivations of how a player makes in-the-moment communication decisions. In this paper, we conducted an observation study with 22 League of Legends players by interviewing them during Solo Ranked games on their use of four in-game communication media (chat, pings, emotes, votes). We performed thematic analysis to understand players' in-context assessment and perception of communication attempts. We demonstrate that players evaluate communication opportunities on proximate game states bound by player expectations and norms. Our findings illustrate players' tendency to view communication, regardless of its content, as a precursor to team breakdowns. We build upon these findings to motivate effective player-oriented communication design in online games.

Social media users employ a variety of methods to avoid unwanted content in their personalized feeds. Platforms like Instagram and YouTube offer the ''Not Interested'' feature, allowing users to signal their preference to see less of certain content or similar types. Despite its wide availability on social media platforms, the ''Not Interested'' feature has received little academic attention, leaving a gap in understanding how it is perceived, used, and interpreted. This study investigates (1) the types of content users mark as ''Not Interested'', (2) their expectations about its effects, and (3) how they interpret its outcome. We conducted semi-structured interviews with 28 Instagram users who had used the ''Not Interested'' button more than 12 times over the past year, focusing on their experiences with this feature. Users used the ''Not Interested'' feature to avoid different types of content, ranging from problematic and personally discomforting to unrewarding. ''Not Interested'' feedback was considered unique in that it would remove similar content from their feeds without harming the creator. Users had mixed expectations about whose feed they wished their feedback to have an impact on. After submitting the feedback and observing the changes in the personalized feed, they were often uncertain of how each of their interactions was reflected in the algorithm. We discuss user characteristics that social media platforms need to consider, including diverse motivations related to content avoidance, users' demands to influence a broad range of content curation, and the need for granular control and deeper understanding of personalized algorithms.

While personalized recommendations improve user experience by aligning information with interests, they are alleged to narrow information acquisition, leading to a filter bubble. Prior studies have shown mixed findings on whether these systems limit or enhance content diversity. This study examines whether the YouTube recommendation system forms the filter bubbles and allows exits from the filter bubbles by ensuring the diversity of information. To assess the information dynamics by user-accessible data, we propose embedding similarity analysis based on feed-level video information. Through simulations of diverse user scenarios, our results demonstrate the dual role of recommendation systems in both fostering and hindering information diversity. It highlights the user's role in breaking the filter bubble on YouTube and suggests applications that help users recognize the current state of personalization.

This study investigated how older adults’ informational, social, and recreational motives predict their Internet use and life satisfaction. In doing so, we tested how two distinct forms of digital skills—receptive and participatory—affect the ways in which older people fulfil their needs through the Internet as well as the extent to which their Internet use leads to psychological benefits. A total of 200 Internet users in their 60s joined a face-to-face survey. As predicted, the older users’ informational, social, and recreational motives predicted their corresponding Internet use and life satisfaction. In particular, participatory skills affected how the use of the Internet predicts life satisfaction, such that only those with moderate or higher participatory skills obtained mental benefits through their Internet use. Lastly, we discussed older adults’ online engagement and its impacts on their well-being, with an emphasis on digital competencies.

Online harassment, especially networked harassment at scale, has become an increasingly serious issue that pervades many social media platforms. In this study, we investigated the nature and harms of networked harassment on Twitter through design workshops (n = 11) and developed a set of design goals focusing on empowering the individual to fight back against harassment. We designed Re:SPect, an anti-harassment tool promoting scalable and active responses to networked harassment. We evaluated Re:SPect through a simulated scenario-based study with Twitter users (n = 18) who had directly or indirectly experienced networked harassment. Our findings reveal that users felt safer and more empowered as Re:SPect enabled them to manage interactions with a larger audience. Users felt less anxious about the potential of being harassed, while the summarization features of Re:SPect allowed users to perceive the situation more objectively. Based on the findings, we discuss how Re:SPect's features could be utilized in promoting healthier online discussion, as well as theoretical implications in designing such systems.

Simple up/downvotes, arguably the most widely used reaction design across social media platforms, allow users to efficiently express their opinions and quickly evaluate others’ opinions from aggregated votes. However, such design forces users to project their diverse opinions onto dichotomized reactions and provides limited information to readers on why a comment was up/downvoted. We explore user-generated labels (UGLs) as an alternative reaction design to capture the rich context of user reactions to comments. We conducted a between-subjects study with 218 participants to understand how people use and are influenced by UGLs compared to up/downvotes. Specifically, we examine how UGLs affect users’ ability to express and perceive diverse opinions. Participants generated 234 unique labels on diverse aspects of a comment. Leaving more reactions than participants in the up/downvotes condition, participants reported that the ability to express their opinions improved with UGLs. UGLs also enabled participants to better understand the multifacetedness of public evaluation of a comment.

Calling out, a phenomenon where people publicly broadcast their critiques of someone to a larger audience using, has become increasingly common on social media. However, there has been concerns that it could develop into harassment, deteriorating the quality of public discourse by over-punishing individuals for minor transgressions. To investigate this phenomenon, we interviewed 32 Twitter users who had been called out, had called out, or had witnessed a calling out on Twitter. We found that a key determining factor that distinguishes criticism from harassment was the callee's ability to respond to or engage with the criticism, and that different stakeholders hold different perspectives toward how online harassment is defined. We also discovered that the distinction between callers and callees was not absolute, and that there was high interchangeability of roles both within and across events. Through these findings, we discuss design implications for the platform in promoting healthy discourse while preventing toxic behavior on social media.