At a time when diverse forms of automated visual feedback systems on speech are introduced to the Human–Computer Interaction (HCI) community, our research investigates the possible design spaces and design implications to consider when creating them. We utilised a Wizard of Oz prototype that recognises fillers during speech and sends out real-time feedback in the form of a flickering screen and a post-report with graphic charts. We let 27 participants use this prototype and interviewed them about their general experience of using such systems. Based on thematic analysis, we propose eight design implications to consider when designing an automated visual feedback system on speech. Additionally, we discuss the possible future for automated visual feedback systems on speech where they collaborate with fields such as personal informatics (quantified self), self-determination theory (SDT) and motivation, and other socio-ethnic areas

Although suicide among older adults is increasing in severity in the aging among South Koreans, older adult depression and anxiety, which are two crucial factors affecting suicide in this population, are not adequately addressed by the status quo. Existing products and services emphasize physical needs; however, they still lack a user-oriented approach. Moreover, offers for mental health care are costly and exclude potential users from the development process, thus inhibiting older adults from having better access to such services. To address this problem, we designed and developed a mental health care chatbot, Yeonheebot, to decrease anxiety and depression among older adults. Yeonheebot initiates conversations regarding daily care for the well-being of older adults and provides features related to their interests as initiatives for using the mental health care chatbot. To examine whether a short-term, yet repeating conversation with the agent could help reduce anxiety and depression among older adults, we conducted a 2-week field study with 25 users. The results indicate that the constant use of Yeonheebot decreased the levels of anxiety and depression by 36% and 18%, respectively. Our research implies that simple and repeated interactions could help Korean older adults cope with anxiety and depression.

To understand the current state of a discipline and to discover new knowledge of a certain theme, one builds bibliometric content networks based on the present knowledge entities. However, such networks can vary according to the collection of data sets relevant to the theme by querying knowledge entities. In this study we classify three different bibliometric content networks. The primary bibliometric network is based on knowledge entities relevant to a keyword of the theme, the secondary network is based on entities associated with the lower concepts of the keyword, and the tertiary network is based on entities influenced by the theme. To explore the content and properties of these networks, we propose a tomographic content analysis that takes a slice‐and‐dice approach to analyzing the networks. Our findings indicate that the primary network is best suited to understanding the current knowledge on a certain topic, whereas the secondary network is good at discovering new knowledge across fields associated with the topic, and the tertiary network is appropriate for outlining the current knowledge of the topic and relevant studies.

Journal rankings, frequently determined by the journal impact factor or similar indices, are quantitative measures for evaluating a journal's performance in its discipline, which is presently a major research thrust in the bibliometrics field. Recently, text mining was adopted to augment journal ranking‐based evaluation with the content analysis of a discipline taking a time‐variant factor into consideration. However, previous studies focused mainly on a silo analysis of a discipline using either citation‐or content‐oriented approaches, and no attempt was made to analyze topical journal ranking and its change over time in a seamless and integrated manner. To address this issue, we propose a journal‐time‐topic model, an extension of Dirichlet multinomial regression, which we applied to the field of bioinformatics to understand journal contribution to topics in a field and the shift of topic trends. The journal‐time‐topic model allows us to identify which journals are the major leaders in what topics and the manner in which their topical focus. It also helps reveal an interesting distinct pattern in the journal impact factor of high‐ and low‐ranked journals. The study results shed a new light for understanding topic specific journal rankings and shifts in journals' concentration on a subject.