Demo Data from SIGIR 2026 / reviewers in the wild / expert
Ka Wing Tsang
dblp:264/7331
· DBLP profile ↗
2ranked-venue papers
0as first author
1since 2021 · last 2021
0000-0003-0182-3255ORCID · corroborated
Domains — the database's venue-derived domains; a paper can count in several
Human-computer interaction and ubiquitous computing · 2 · 1 since 2021
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Human-computer interaction and pervasive computing
2 papers |
Health and well-being technologies · 38% Collaborative and social computing · 26% User interface design and tools · 18% | |
| Software engineering, system software, and programming languages
1 paper |
Empirical software engineering · 100% |
Topics — the 2 heaviest of 5, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Health and well-being technologies › mental health technology
online mental health community |
0.9 | 2 | 2021 | Effects of Support-Seekers' Community Knowledge on Their Expressed Satisfaction with the Received Comments in Mental Health Communities · CHI 2021 Exploring the Effects of Technological Writing Assistance for Support Providers in Online Mental Health Community · CHI 2020 |
User interface design and tools › authoring tools
writing support tools |
0.4 | 1 | 2020 | Exploring the Effects of Technological Writing Assistance for Support Providers in Online Mental Health Community · CHI 2020 |
Methods — techniques the papers use, named apart from their topics
satisfaction modeling · 1.0quantitative analysis · 1.0text assessment · 0.4mixed-design study · 0.4example recommendation · 0.4
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2021 | Effects of Support-Seekers' Community Knowledge on Their Expressed Satisfaction with the Received Comments in Mental Health CommunitiesabstractOnline mental health communities (OMHCs) are prominent resources for improving people’s mental wellbeing. An immediate cue of such improvement is support-seekers’ satisfaction expressed in their replies to the received comments. However, the comments that seekers find satisfying may change with their community knowledge, e.g., measured by tenure and posting experience in that community. In this paper, we first model the amount of satisfaction conveyed in the support-seekers’ replies to the received comments. Then we quantitatively examine how seekers’ expressed satisfaction is affected by their community knowledge, sought and received support in an OMHC. Results show that support-seekers with more posting experience generally display less contentment to the received comments. Compared to newcomers, higher tenured members express less satisfaction when receiving informational support. We also found that support matching positively predicts seekers’ satisfaction regardless of their community knowledge. Our findings have implications for OMHCs to satisfy support-seekers through their community knowledge. Zhenhui Peng, Xiaojuan Ma, Diyi Yang, Ka Wing Tsang, Qingyu Guo |
CHI | 4 |
| 2020 | Exploring the Effects of Technological Writing Assistance for Support Providers in Online Mental Health CommunityabstractTextual comments from peers with informational and emotional support are beneficial to members of online mental health communities (OMHCs). However, many comments are not of high quality in reality. Writing support technologies that assess (AS) the text or recommend (RE) writing examples on the fly could potentially help support providers to improve the quality of their comments. However, how providers perceive and work with such technologies are under-investigated. In this paper, we present a technological prototype MepsBot which offers providers in-situ writing assistance in either AS or RE mode. Results of a mixed-design study with 30 participants show that both types of MepsBots improve users' confidence in and satisfaction with their comments. The AS-mode MepsBot encourages users to refine expressions and is deemed easier to use, while the RE-mode one stimulates more support-related content re-editions. We report concerns on MepsBot and propose design considerations for writing support technologies in OMHCs. Zhenhui Peng, Qingyu Guo, Ka Wing Tsang, Xiaojuan Ma |
CHI | 3 |