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Online hate speech has become increasingly prevalent on social media platforms, causing harm to individuals and society. While efforts have been made to combat this issue through content moderation, the potential of user-driven counterspeech as an alternative solution remains underexplored. Existing counterspeech methods often face challenges such as fear of retaliation and skill-related barriers. To address these challenges, we introduce CounterQuill, an AI-mediated system that assists users in composing effective and empathetic counterspeech. CounterQuill provides a three-step process: (1) a learning session to help users understand hate speech and counterspeech; (2) a brainstorming session that guides users in identifying key elements of hate speech and exploring counterspeech strategies; and (3) a co-writing session that enables users to draft and refine their counterspeech with CounterQuill. We conducted a within-subjects user study with 20 participants to evaluate CounterQuill in comparison to ChatGPT. Results show that CounterQuill's guidance and collaborative writing process provided users a stronger sense of ownership over their co-authored counterspeech. Users perceived CounterQuill as a writing partner and thus were more willing to post the co-written counterspeech online compared to the one written with ChatGPT.

Despite the existence of search-based recommender systems like Google, Netflix, and Spotify, online users sometimes may turn to crowdsourced recommendations in places like the r/ifyoulikeblank subreddit. In this exploratory study, we probe why users go to r/ifyoulikeblank, how they look for recommendation, and how the subreddit users respond to recommendation requests. To answer, we collected sample posts from r/ifyoulikeblank and analyzed them using a qualitative approach. Our analysis reveals that users come to this subreddit for various reasons, such as exhausting popular search systems, not knowing what or how to search for an item, and thinking crowd have better knowledge than search systems. Examining users query and their description, we found novel information users provide during recommendation seeking using r/ifyoulikeblank. For example, sometimes they ask for artifacts recommendation based on the tools used to create them. Or, sometimes indicating a recommendation seeker's time constraints can help better suit recommendations to their needs. Finally, recommendation responses and interactions revealed patterns of how requesters and responders refine queries and recommendations. Our work informs future intelligent recommender systems design., Comment: page 23

Efficient task planning is essential for productivity and mental well-being, yet individuals often struggle to create realistic plans and reflect upon their productivity. Leveraging the advancement in artificial intelligence (AI), conversational agents have emerged as a promising tool for enhancing productivity. Our work focuses on externalizing plans through conversation, aiming to solidify intentions and foster focused action, thereby positively impacting their productivity and mental well-being. We share our plan of designing a conversational agent to offer insightful questions and reflective prompts for increasing plan adherence by leveraging the social interactivity of natural conversations. Previous studies have shown the effectiveness of such agents, but many interventions remain static, leading to decreased user engagement over time. To address this limitation, we propose a novel rotation and context-aware prompting strategy, providing users with varied interventions daily. Our system, PITCH, utilizes large language models (LLMs) to facilitate externalization and reflection on daily plans. Through this study, we investigate the impact of externalizing tasks with conversational agents on productivity and mental well-being, and the effectiveness of a rotation strategy in maintaining user engagement.

Joint reading is a key activity for early learners, with caregiver-child interactions such as questioning and feedback playing an essential role in children's cognitive and linguistic development. However, for some parents, actively engaging children in storytelling can be challenging. To address this, we introduce TaleMate a platform designed to enhance shared reading by leveraging conversational agents that have been shown to support children's engagement and learning. TaleMate enables a dynamic, participatory reading experience where parents and children can choose which characters they wish to embody. Moreover, the system navigates the challenges posed by digital reading tools, such as decreased parent-child interaction, and builds upon the benefits of traditional and digital reading techniques. TaleMate offers an innovative approach to fostering early reading habits, bridging the gap between traditional joint reading practices and the digital reading landscape., Comment: 4 pages, 2 figures, CHI 2024 Workshop on Child-centred AI Design

As large language models (LLMs) become more powerful and ubiquitous, systems like ChatGPT are increasingly used by students to help them with writing tasks. To better understand how these tools are used, we investigate how students might use an LLM for essay writing, for example, to study the queries asked to ChatGPT and the responses that ChatGPT gives. To that end, we plan to conduct a user study that will record the user writing process and present them with the opportunity to use ChatGPT as an AI assistant. This study's findings will help us understand how these tools are used and how practitioners -- such as educators and essay readers -- should consider writing education and evaluation based on essay writing., Comment: 5 pages, 2 figures, submitted and accepted to ACM CHI Workshop In2Writing in 2024

Cybergrooming emerges as a growing threat to adolescent safety and mental health. One way to combat cybergrooming is to leverage predictive artificial intelligence (AI) to detect predatory behaviors in social media. However, these methods can encounter challenges like false positives and negative implications such as privacy concerns. Another complementary strategy involves using generative artificial intelligence to empower adolescents by educating them about predatory behaviors. To this end, we envision developing state-of-the-art conversational agents to simulate the conversations between adolescents and predators for educational purposes. Yet, one key challenge is the lack of a dataset to train such conversational agents. In this position paper, we present our motivation for empowering adolescents to cope with cybergrooming. We propose to develop large-scale, authentic datasets through an online survey targeting adolescents and parents. We discuss some initial background behind our motivation and proposed design of the survey, such as situating the participants in artificial cybergrooming scenarios, then allowing participants to respond to the survey to obtain their authentic responses. We also present several open questions related to our proposed approach and hope to discuss them with the workshop attendees.

To support efficient, balanced news consumption, merging articles from diverse sources into one, potentially through crowdsourcing, could alleviate some hurdles. However, the merging process could also impact annotators' attitudes towards the content. To test this theory, we propose comparative news annotation, i.e., annotating similarities and differences between a pair of articles. By developing and deploying NewsComp -- a prototype system -- we conducted a between-subjects experiment(N=109) to examine how users' annotations compare to experts', and how comparative annotation affects users' perceptions of article credibility and quality. We found that comparative annotation can marginally impact users' credibility perceptions in certain cases. While users' annotations were not on par with experts', they showed greater precision in finding similarities than in identifying disparate important statements. The comparison process led users to notice differences in information placement/depth, degree of factuality/opinion, and empathetic/inflammatory language use. We discuss implications for the design of future comparative annotation tasks., Comment: 2023 ACM CHI Conference on Human Factors in Computing Systems, 17 pages

The East Asian VLBI Network (EAVN) is an international VLBI facility in East Asia and is operated under mutual collaboration between East Asian countries, as well as part of Southeast Asian and European countries. EAVN currently consists of 16 radio telescopes and three correlators located in China, Japan, and Korea, and is operated mainly at three frequency bands, 6.7, 22, and 43 GHz with the longest baseline length of 5078 km, resulting in the highest angular resolution of 0.28 milliarcseconds at 43 GHz. One of distinct capabilities of EAVN is multi-frequency simultaneous data reception at nine telescopes, which enable us to employ the frequency phase transfer technique to obtain better sensitivity at higher observing frequencies. EAVN started its open-use program in the second half of 2018, providing a total observing time of more than 1100 hours in a year. EAVN fills geographical gap in global VLBI array, resulting in enabling us to conduct contiguous high-resolution VLBI observations. EAVN has produced various scientific accomplishments especially in observations toward active galactic nuclei, evolved stars, and star-forming regions. These activities motivate us to initiate launch of the 'Global VLBI Alliance' to provide an opportunity of VLBI observation with the longest baselines on the earth., Comment: 27 pages, appeared in Galaxies special issue 'Challenges in Understanding Black Hole Powered Jets with VLBI' as an invited review

Modern knowledge workers typically need to use multiple resources, such as documents, web pages, and applications, at the same time. This complexity in their computing environments forces workers to restore various resources in the course of their work. However, conventional curation methods like bookmarks, recent document histories, and file systems place limitations on effective retrieval. Such features typically work only for resources of one type within one application, ignoring the interdependency between resources needed for a single task. In addition, text-based handles do not provide rich cues for users to recognize their associated resources. Hence, the need to locate and reopen relevant resources can significantly hinder knowledge workers' productivity. To address these issues, we designed and developed Scrapbook, a novel application for digital resource curation across applications that uses screenshot-based bookmarks. Scrapbook extracts and stores all the metadata (URL, file location, and application name) of windows visible in a captured screenshot to facilitate restoring them later. A week-long field study indicated that screenshot-based bookmarks helped participants curate digital resources. Additionally, participants reported that multimodal -- visual and textual -- data helped them recall past computer activities and reconstruct working contexts efficiently.

To promote engagement, recommendation algorithms on platforms like YouTube increasingly personalize users' feeds, limiting users' exposure to diverse content and depriving them of opportunities to reflect on their interests compared to others'. In this work, we investigate how exchanging recommendations with strangers can help users discover new content and reflect. We tested this idea by developing OtherTube -- a browser extension for YouTube that displays strangers' personalized YouTube recommendations. OtherTube allows users to (i) create an anonymized profile for social comparison, (ii) share their recommended videos with others, and (iii) browse strangers' YouTube recommendations. We conducted a 10-day-long user study (n=41) followed by a post-study interview (n=11). Our results reveal that users discovered and developed new interests from seeing OtherTube recommendations. We identified user and content characteristics that affect interaction and engagement with exchanged recommendations; for example, younger users interacted more with OtherTube, while the perceived irrelevance of some content discouraged users from watching certain videos. Users reflected on their interests as well as others', recognizing similarities and differences. Our work shows promise for designs leveraging the exchange of personalized recommendations with strangers., Comment: CHI 2022, 17 pages

For a write request, today flash storage cannot distinguish the logical object it comes from. In such object-oblivious flash devices, concurrent writes from different objects are simply packed in their arrival order to flash memory blocks; hence objects with different lifetimes are multiplexed onto the same flash blocks. This multiplexing incurs write amplification, worsening the performance. Tackling the multiplexing problem, we propose a novel interface for flash storage, FlashAlloc. It is used to pass the logical address ranges of logical objects to the flash storage and thus enlighten the storage to stream writes by objects. The object-aware flash storage can de-multiplex writes from different objects with distinct deathtimes into per-object dedicated flash blocks. Given that popular data stores separate writes using objects (e.g., SSTables in RocksDB), we can achieve, unlike the existing solutions, transparent write streaming just by calling FlashAlloc upon object creation. Our experimental results using an open-source SSD prototype demonstrate that FlashAlloc can reduce write amplification factor (WAF) in RocksDB, F2FS, and MySQL by 1.5, 2.5, and 0.3, respectively and thus improve throughput by 2x, 1.8x, and 1.2x, respectively. In particular, FlashAlloc will mitigate the interference among multitenants. When RocksDB and MySQL were run together on the same SSD, FlashAlloc decreased WAF from 4.2 to 2.5 and doubled their throughputs.