Understanding localness of knowledge sharing

Abstract

In location-based social Q&A, the questions related to a local community (e.g. local services and places) are typically answered by local residents (i.e. who have the local knowledge). In this work, we wanted to deepen our understanding of the localness of knowledge sharing through investigating the topical and typological patterns related to the geographic characteristics, geographic locality of user activities, and motivations of local knowledge sharing. To this end, we analyzed a 12-month period Q&A dataset from Naver KiN "Here" and a supplementary survey dataset from 285 mobile users. Our results revealed several unique characteristics of location-based social Q&A. When compared with conventional social Q&A sites, Naver KiN "Here" had very different topical/typological patterns. Naver KiN "Here" exhibited a strong spatial locality where the answerers mostly had 1-3 spatial clusters of contributions, the topical distributions varied widely across different districts, and a typical cluster spanned a few neighboring districts. In addition, we uncovered unique motivators, e.g. ownership of local knowledge and sense of local community. The findings reported in the paper have significant implications for the design of Q&A systems, especially location-based social Q&A systems.

Cross-Lingual Suicidal-Oriented Word Embedding toward Suicide Prevention

“…To collect suicide-related and non-suicide-related posts, we selected Naver Cafe operated by Naver, one of the most popular web-based services in Korea (Nam et al, 2009;Park et al, 2014). Like a subreddit in Reddit, a user can create a topic-based community in Naver Cafe, named a 'cafe', where members in a cafe can communicate with others via writing posts or commenting posts.…”

Technology literacy in poor infrastructure environments

“…Park et al [21] analyzed datasets from Naver KiN "Here", a mobile app for location-based social Q&A in Korea. In comparison with conventional social Q&A sites, Naver KiN "Here" had very different topical/typological patterns, where the responses exhibited a strong spatial locality, topical distributions varied widely across different districts, and a typical cluster spanned a few neighboring districts.…”

“…Over the recent years, the HCI community has woken up to the realities of developing countries, and witnessed increasing attention to Human Computer Interaction for development, or HCI4D [28,40,30,26]. While initially the focus was on infrastructure-rich settings, and specifically on issues relating to the digital mission of access anytime, anywhere [22], recent research ranges from focusing on qualitative exploratory fieldwork for technology practices in infrastructure-poor settings [2,16,41], introducing new tools in such communities [15,18], to quantitatively assessing knowledge sharing in location-based social Q&A [21].…”

Community Reminder: Participatory contextual reminder environments for local communities

“…Thus, we argue for a system that exploits a participatory approach to create and use contextual reminders. A key differentiation between our proposed idea and conventional systems for sharing information in the local community (e.g., LOCQL (LOCQL, n.d.) and Naver KiN 'here' (Park et al, 2014)) is that the latter require proactive searching and do not necessary react to users' context.…”