As governments increasingly adopt digital tools, public service chatbots have emerged as a growing communication channel.This paper explores the design considerations and engagement opportunities of public service chatbots, using a 311 chatbot from a metropolitan city as a case study.Our qualitative study consisted of official survey data and 16 interviews examining stakeholder experiences and design preferences for the chatbot.We found two key areas of concern regarding these public chatbots: individual-level and community-level.At the individual level, citizens experience three key challenges: interpretation, transparency, and social contextualization.Moreover, the current chatbot design prioritizes the efficient completion of individual tasks but neglects the broader community perspective.It overlooks how individuals interact and discuss problems collectively within their communities.To address these concerns, we offer design opportunities for creating more intelligent, transparent, community-oriented chatbots that better engage individuals and their communities.

Researchers across various fields have investigated how users experience moderation through different perspectives and methodologies. At present, there is a pressing need of synthesizing and extracting key insights from prior literature to formulate a systematic understanding of what constitutes a moderation experience and to explore how such understanding could further inform moderation-related research and practices. To answer this question, we conducted a systematic literature review (SLR) by analyzing 42 empirical studies related to moderation experiences and published between January 2016 and March 2022. We describe these studies' characteristics and how they characterize users' moderation experiences. We further identify five primary perspectives that prior researchers use to conceptualize moderation experiences. These findings suggest an expansive scope of research interests in understanding moderation experiences and considering moderated users as an important stakeholder group to reflect on current moderation design but also pertain to the dominance of the punitive, solutionist logic in moderation and ample implications for future moderation research, design, and practice.

Chatbot-based symptom checker (CSC) apps have become increasingly popular in healthcare. These apps engage users in human-like conversations and offer possible medical diagnoses. The conversational design of these apps can significantly impact user perceptions and experiences, and may influence medical decisions users make and the medical care they receive. However, the effects of the conversational design of CSCs remain understudied, and there is a need to investigate and enhance users’ interactions with CSCs. In this article, we conducted a two-stage exploratory study using a human-centered design methodology. We first conducted a qualitative interview study to identify key user needs in engaging with CSCs. We then performed an experimental study to investigate potential CSC conversational design solutions based on the results from the interview study. We identified that emotional support, explanations of medical information, and efficiency were important factors for users in their interactions with CSCs. We also demonstrated that emotional support and explanations could affect user perceptions and experiences, and they are context-dependent. Based on these findings, we offer design implications for CSC conversations to improve the user experience and health-related decision-making.

Online symptom checkers (OSC) are widely used intelligent systems in health contexts such as primary care, remote healthcare, and epidemic control. OSCs use algorithms such as machine learning to facilitate self-diagnosis and triage based on symptoms input by healthcare consumers. However, intelligent systems’ lack of transparency and comprehensibility could lead to unintended consequences such as misleading users, especially in high-stakes areas such as healthcare. In this paper, we attempt to enhance diagnostic transparency by augmenting OSCs with explanations. We first conducted an interview study (N=25) to specify user needs for explanations from users of existing OSCs. Then, we designed a COVID-19 OSC that was enhanced with three types of explanations. Our lab-controlled user study (N=20) found that explanations can significantly improve user experience in multiple aspects. We discuss how explanations are interwoven into conversation flow and present implications for future OSC designs.

Recently, consumer-facing health technologies such as Artificial Intelligence (AI)-based symptom checkers (AISCs) have sprung up in everyday healthcare practice. AISCs solicit symptom information from users and provide medical suggestions and possible diagnoses, a responsibility that people usually entrust with real-person authorities such as physicians and expert patients. Thus, the advent of AISCs begs a question of whether and how they transform the notion of medical authority in people's everyday healthcare practice. To answer this question, we conducted an interview study with thirty AISC users. We found that users assess the medical authority of AISCs using various factors including AISCs’ automated decisions and interaction design patterns, associations with established medical authorities like hospitals, and comparisons with other health technologies. We reveal how AISCs are used in healthcare delivery, discuss how AI transforms conventional understandings of medical authority, and derive implications for designing AI-enabled health technology.

This paper proposes a new vision-based method for extracting academic information from conference Web pages. The main contributions include: (1) An new vision-based page segmentation algorithm is proposed to improve the result of classical VIPS algorithm. This algorithm can divide pages into text blocks. (2) All text blocks are classified as 10 categories according to vision features, keyword features and text content features. The initial classification results have 75% precision and 67% recall. (3) The context information of text blocks are employed to repair and refine initial classification results, which are improved to 96% precision and 98% recall. Finally, academic information is extracted from classified text blocks. Our experimental results on real-world datasets show that the proposed method is effective and efficient for extracting academic information from conference Web pages.

Conference Web pages are the main platforms to share the conference information and organize conference events. To discover the academic knowledge from such Web pages for building academic ontologies or social networks, it is necessary to extract academic information from conference Web pages. This paper proposes an approach to extract academic information from conference Web pages. Firstly, Web pages are segmented into text blocks by analyzing the visual feature and DOM structure. Then Bayes Network is used to classify these text blocks into predefined categories, and the quality of initial classification results are improved after post-processing. Finally, the academic information is extracted from the classified text blocks. Our experimental results on the real world datasets show that the proposed method is highly effective and efficient for extracting academic information from conference Web pages, and it has average 90% precision and 89% recall.