Community Question Research Articles

The Israeli queue with a capacitated server: modeling and approximations

AbstractThe Israeli Queue is a batch service polling system where a single server attends to multiple queues based on seniority. Each arriving customer belongs to one of several classes. Upon arrival, a customer either joins an existing queue for their class or initiates a new queue if they are the first of their class to arrive. Customers from the class with the most senior member are served together as a batch, with the service time remaining constant regardless of the batch size. This service model is found in applications like advanced elevator systems and on-demand shared mobility, where passengers heading to the same destination can share a ride. However, in many real-world scenarios, the vehicle capacities are small and constraining, which calls for a deeper exploration of the Israeli queue with a capacitated server (IQCS). In this paper, we formally define the IQCS and address the challenges of creating a mathematically tractable model to represent it. To approximate the IQCS, we develop a quasi-birth-death process and derive approximations for key performance measures. To validate our approach, we implement a simulation model and use it to compare the IQCS, the approximate model, and the original Israeli Queue. Our results across various scenarios demonstrate the accuracy of the approximate model. Nonetheless, the presence of a remaining gap underscores the ongoing challenge of precisely and efficiently modeling the IQCS, posing an open question for the research community.

Beyond Empathy; Love. Person and Otherness in the Thought of Edith Stein

Debating the nature of social cognition, there has been an upsurge in studies on empathy since the turn of the century. The contribution of Edith Stein’s doctoral dissertation On Empathy has also been brought to the forefront. In her philosophy, there is a continuous concern for the questions of social relations and human community, the explorations of the human person and its unfolding in the encounter with otherness being a leading thread in her work. Still, after her dissertation, the notion of empathy is no longer in use. What does this signify? I propose that the notion of ‘empathy’ proved to be too restrained for what Stein discovers and wants to phenomenologically describe. After her conversion to the Christian faith, working in the intersection of philosophy and theology, she expands and transforms her notion of what it means to be a person and, correspondingly, her intuition of the social relation. In the interpretative readings of this article, I show how Edith Stein’s early intuitions on empathy, alterity, and personhood come to full development in her later writings, where the relation to otherness and the unfolding of the person are conceived as inseparable from the experience of a loving God.

Deciphering digital popularity from question quality: the preeminence of social attributes over textual dimensions in driving CQA platform traffic

ABSTRACT Community Question Answering (CQA) sites, now navigating through millions of daily user visits, hinge significantly on the quality of user-generated questions to bolster user satisfaction and, consequently, website traffic – crucial for platform sustainability. While the quality of questions ostensibly sets the stage for insightful answers, thereby attracting substantial traffic, existing research has predominantly sidelined the significance of questions, favoring a focus on traditional factors, such as answer quality, to elucidate traffic determinants. In this study we quantified the interrelation between question quality and traffic by extracting 15 relevant question-quality features and corresponding traffic (i.e. pageview) data from a leading Chinese CQA platform. Utilizing Grey Relational Analysis, we assessed these attributes’ relative influence on pageviews and created a ranking list of features of questions. Based on the rankings, our findings spotlight the paramount influence of a question's digital popularity and attributes tied to the questioner – central pillars of social features – surpassing the impact of textual elements. This study further delineates the underlying reasons for such patterns based on traffic sourced from diverse channels. Our revelations offer pivotal insights for optimizing CQA platform engagement and furnish avenues for prospective research.

Applying a Participatory Action Research Approach to Engage an Australian Culturally and Linguistically Diverse Community around Human Papillomavirus Vaccination: Lessons Learned.

Globally, migrant and culturally and linguistically diverse (CALD) communities are known to have inequitable access to HPV vaccination. One participatory research approach used to engage CALD communities around vaccination is participatory action research (PAR), but we know little about the use of PAR to engage priority migrant and CALD communities around school and HPV vaccination. To address this gap, we partnered with our local Multicultural Health Service to understand how the largest CALD group in our region of New South Wales, Australia, engages with their local school and HPV vaccination program. Through a long-standing PAR relationship, we used a participatory research method (World Café) approach to explore the level of awareness and engagement a multi-generational community member had concerning HPV vaccination. We acted by sharing a co-designed information website to answer the community's questions about HPV vaccination. We then evaluated these engagements with surveys, focus groups and online analytic platforms. Last, we reflected with project partners and health service stakeholders on the overall project outcomes and shared our learnings. In our discussion, we shared our lessons learned and contributed to a wider conversation about the benefits, challenges, and practicalities of using PAR to engage a priority CALD community around HPV vaccination.

PEPT: Expert Finding Meets Personalized Pre-training

Finding experts is essential in Community Question Answering (CQA) platforms as it enables the effective routing of questions to potential users who can provide relevant answers. The key is to personalized learning expert representations based on their historical answered questions, and accurately matching them with target questions. Recently, the application of Pre-trained Language Models (PLMs) have gained significant attraction due to their impressive capability to comprehend textual data, and are widespread used across various domains. There have been some preliminary works exploring the usability of PLMs in expert finding, such as pre-training expert or question representations. However, these models usually learn pure text representations of experts from histories, disregarding personalized and fine-grained expert modeling. For alleviating this, we present a personalized pre-training and fine-tuning paradigm, which could effectively learn expert interest and expertise simultaneously. Specifically, in our pre-training framework, we integrate historical answered questions of one expert with one target question, and regard it as a candidate aware expert-level input unit. Then, we fuse expert IDs into the pre-training for guiding the model to model personalized expert representations, which can help capture the unique characteristics and expertise of each individual expert. Additionally, in our pre-training task, we design: 1) a question-level masked language model task to learn the relatedness between histories, enabling the modeling of question-level expert interest; 2) a vote-oriented task to capture question-level expert expertise by predicting the vote score the expert would receive. Through our pre-training framework and tasks, our approach could holistically learn expert representations including interests and expertise. Our method has been extensively evaluated on six real-world CQA datasets, and the experimental results consistently demonstrate the superiority of our approach over competitive baseline methods. 1

Multi-dimensional feature fusion-based expert recommendation in community question answering

PurposeCommunity question answering (CQA) platforms play a significant role in knowledge dissemination and information retrieval. Expert recommendation can assist users by helping them find valuable answers efficiently. Existing works mainly use content and user behavioural features for expert recommendation, and fail to effectively leverage the correlation across multi-dimensional features.Design/methodology/approachTo address the above issue, this work proposes a multi-dimensional feature fusion-based method for expert recommendation, aiming to integrate features of question–answerer pairs from three dimensions, including network features, content features and user behaviour features. Specifically, network features are extracted by first learning user and tag representations using network representation learning methods and then calculating questioner–answerer similarities and answerer–tag similarities. Secondly, content features are extracted from textual contents of questions and answerer generated contents using text representation models. Thirdly, user behaviour features are extracted from user actions observed in CQA platforms, such as following and likes. Finally, given a question–answerer pair, the three dimensional features are fused and used to predict the probability of the candidate expert answering the given question.FindingsThe proposed method is evaluated on a data set collected from a publicly available CQA platform. Results show that the proposed method is effective compared with baseline methods. Ablation study shows that network features is the most important dimensional features among all three dimensional features.Practical implicationsThis work identifies three dimensional features for expert recommendation in CQA platforms and conducts a comprehensive investigation into the importance of features for the performance of expert recommendation. The results suggest that network features are the most important features among three-dimensional features, which indicates that the performance of expert recommendation in CQA platforms is likely to get improved by further mining network features using advanced techniques, such as graph neural networks. One broader implication is that it is always important to include multi-dimensional features for expert recommendation and conduct systematic investigation to identify the most important features for finding directions for improvement.Originality/valueThis work proposes three-dimensional features given that existing works mostly focus on one or two-dimensional features and demonstrate the effectiveness of the newly proposed features.

Predicting Question Popularity for Community Question Answering

In this paper, we study the problem of predicting popularities of questions in Community Question Answering (CQA). To address this problem, we propose a Posterior Attention Recurrent Point Process Model (PARPP) to take both the interaction of users and the Matthew effect into account for question popularity prediction. Our PARPP uses long short-term memory (LSTM) to encode the observed history and another LSTM network to record each step of decoding information. At each decoding step, it uses prior attention to capture answers that have a greater impact on the problem. When a new answer is observed, it uses Bayes’ rule to modify prior attention and obtain posterior attention. Then, the posterior attention is used to update the decoding status. We further introduce a convergence strategy to capture the Matthew effect in CQA. We conduct experiments on a Zhihu dataset crawled from a famous Chinese CQA forum. The experimental results show that our model outperforms several state-of-the-art methods. We further analyze the attention mechanism in our model. Our analysis shows that the proposed attention mechanism can better capture the impact of each answer on the future popularity of the question, which makes our model more interpretable. Our study would shed light on other similar studies such as answer ranking in response to the question and finding experts who have expertise on the topics of the questions.

Feature engineering in learning-to-rank for community question answering task

Community question answering (CQA) forums are Internet-based platforms where users ask questions about a topic and other expert users try to provide solutions. Many CQA forums such as Quora, Stackoverflow, Yahoo!Answer, StackExchange exist with a lot of user-generated data. These data are leveraged in automated CQA ranking systems where similar questions (and answers) are presented in response to the user's query. In this work, we empirically investigate a few aspects of this domain. First, in addition to traditional features like Term Frequency (TF), Inverse Document Frequency (IDF), Best Match 25 (BM25), etc., we introduce a Bidirectional Encoder Representations from Transformers (BERT)-based feature that captures the semantic similarity between the question and answer. Second, most of the existing research works have focus on features extracted only from the question part; features extracted from answers have not been explored extensively. We combine both types of features in a linear fashion. Third, using our proposed concepts, we conduct an empirical investigation with different rank-learning algorithms, some of which have not been used so far in CQA domain. On three standard CQA datasets, our proposed framework achieves state-of-the-art performance (0.56, 0.58 and 0.60 NDCG@10 values). We also analyze the importance of the features we use in our investigation. This work is expected to guide the practitioners to devise a better set of features for the CQA retrieval task.

Attention-based RNN with question-aware loss and multi-level copying mechanism for natural answer generation

Natural answer generation is in a very clear practical significance and strong application background, which can be widely used in the field of knowledge services such as community question answering and intelligent customer service. Traditional knowledge question answering is to provide precise answer entities and neglect the defects; namely, users hope to receive a complete natural answer. In this research, we propose a novel attention-based recurrent neural network for natural answer generation, which is enhanced with multi-level copying mechanisms and question-aware loss. To generate natural answers that conform to grammar, we leverage multi-level copying mechanisms and the prediction mechanism which can copy semantic units and predict common words. Moreover, considering the problem that the generated natural answer does not match the user question, question-aware loss is introduced to make the generated target answer sequences correspond to the question. Experiments on three response generation tasks show our model to be superior in quality while being more parallelizable and requiring significantly less time to train. Our model achieves 0.727 BLEU on the SimpleQuestions response generation task, improving over the existing best results by over 0.007 BLEU. Our model has scored a significant enhancement on naturalness with up to 0.05 more than best performing baseline. The simulation results show that our method can generate grammatical and contextual natural answers according to user needs.

Transforming the residential built environment: Land-use policies and the influence of the growth machine across the United States

What effects do growth machine actors have on transformation of the built environment in terms of localities' enacted land-use policies and actual development? To the best of our knowledge, this study is among the first to answer this question for communities across the United States. We draw on the growth machine theory and speak to some of its proponents’ puzzling findings that policies designed to limit growth often facilitate it. Our analysis is grounded in the urban-quantitative tradition of large-sample studies of U.S. localities. We find that involvement of growth machine business actors in local government is related to transformation of the built environment as indicated by the issuance of new residential building permits. Intriguingly, involvement of local growth actors is also associated with greater use of land-use control policies, which, in turn, have positive rather than negative effects on issuance of new residential building permits. Thus, it seems growth machine actors support policies that outwardly appear to stymie growth, although they in practice do not. These findings suggest land-use policies are often designed with enough leeway for continued growth. Supporting such policies allows growth machine actors to publicly signal support for managed growth and environmental protection, although the reality on the ground remains business as usual.

Fuzzy ensemble of fined tuned BERT models for domain-specific sentiment analysis of software engineering dataset.

Software engineers post their opinions about various topics on social media that can be collectively mined using Sentiment Analysis. Analyzing this opinion is useful because it can provide insight into developers' feedback about various tools and topics. General-purpose sentiment analysis tools do not work well in the software domain because most of these tools are trained on movies and review datasets. Therefore, efforts are underway to develop domain-specific sentiment analysis tools for the Software Engineering (SE) domain. However, existing domain-specific tools for SE struggle to compute negative and neutral sentiments and can not be used on all SE datasets. This work uses a hybrid technique based on deep learning and a fine-tuned BERT model, i.e., Bert-Base, Bert-Large, Bert-LSTM, Bert-GRU, and Bert-CNN presented that is adapted as a domain-specific sentiment analysis tool for Community Question Answering datasets (named as Fuzzy Ensemble). Five different variants of fine-tuned BERT on the SE dataset are developed, and an ensemble of these fine-tuned models is taken using fuzzy logic. The trained model is evaluated on four publicly available benchmark datasets, i.e., Stack Overflow, JavaLib, Jira, and Code Review, using various evaluation metrics. The fuzzy Ensemble model is also compared with the state-of-the-art sentiment analysis tools for the software engineering domain, i.e., SentiStrength-SE, Senti4SD, SentiCR, and Generative Pre-Training Transformer (GPT). GPT mode is fine-tuned by the authors for domain-specific sentiment analysis. The Fuzzy Ensemble model covers the limitation of existing tools and improve accuracy to predict neutral sentiments even on diverse dataset. The fuzzy Ensemble model performs superior to state-of-the-art tools by achieving a maximum F1-score of 0.883.

Improving the clarity of questions in Community Question Answering networks

Improving the clarity of questions in Community Question Answering networks

Celebrating Fifty Years of Jewish Pride: An Autoethnographic View on Queerness, Diaspora and Homeland in an American Gay Synagogue

Anthropologists of religion are preoccupied with questions of identity, community, performance and representation. One way they cope with these concerns is through a reflexive examination of their ethnographic positionality in the field. This provides an opportunity to engage not only with “the other”, but also to explore their own identities and background. This article presents an autoethnographic analysis of Pride Shabbat, a special service held in June to celebrate the intersection of Judaism and queerness. The service took place at Congregation Beit Simchat Torah (CBST) as part of their 50th-anniversary celebration. Since the 1970s, CBST has been known as the largest gay synagogue in the world and provided diverse religious and spiritual services to the Jewish LGBTQ+ community. Based on my participation in this specific event in June 2023, I draw distinct differences between the Israeli Jewish LGBTQ community and the American Jewish LGBTQ community, such as issues related to ageism and multigenerational perceptions within the gay community, the internal dynamic for gender dominance, as well as diverse trajectories of queerness, religiosity and nationality. Symbolically, contrary to the common perception that the diaspora looks to the state of Israel for symbolic and actual existence, this inquiry sheds light on the opposite perspective; the homeland (represented by the ethnographer) absorbs and learns from the queer Jewish practices and experiences taking place within the diaspora (the American Jewish LGBTQ community). This is an opposite movement which reveals the cracks in the perception of the gay community as a transnational community, as well as the tense power relations between Israel and American Jewry.

Engage Wider Audience or Facilitate Quality Answers? a Mixed-methods Analysis of Questioning Strategies for Research Sensemaking on a Community Q&A Site

Discussing research-sensemaking questions on Community Question and Answering (CQA) platforms has been an increasingly common practice for the public to participate in science communication. Nonetheless, how users strategically craft research-sensemaking questions to engage public participation and facilitate knowledge construction is a significant yet less understood problem. To fill this gap, we collected 837 science-related questions and 157,684 answers from Zhihu, and conducted a mixed-methods study to explore user-developed strategies in proposing research-sensemaking questions, and their potential effects on public engagement and knowledge construction. Through open coding, we captured a comprehensive taxonomy of question-crafting strategies, such as eyecatching narratives with counter-intuitive claims and rigorous descriptions with data use. Regression analysis indicated that these strategies correlated with user engagement and answer construction in different ways (e.g., emotional questions attracted more views and answers), yet there existed a general divergence between wide participation and quality knowledge establishment, when most questioning strategies could not ensure both. Based on log analysis, we further found that collaborative editing afforded unique values in refining research-sensemaking questions regarding accuracy, rigor, comprehensiveness and attractiveness. We propose design implications to facilitate accessible, accurate and engaging science communication on CQA platforms.

What Does a Downvote Do? Performing Complementary and Competing Knowledge Practices on an Online Platform

Large-scale online platforms powered by user-generated content are extensively researched as venues of learning and knowledge production. In this ethnographically oriented study, we examine knowledge practices on a community question answering platform for computer programmers in relation to the platform mechanics of voting. Grounded in the practice theoretical perspective and drawing on the analysis of online discussion threads and platform-related online materials, our study unpacks the dominant practice of crowd-based curation, the complementing practice of distributed moderation, and the more marginal practice of providing feedback to content producers. The practices co-exist in tension and consonance, which are embedded in the materiality of the platform and are continuously enacted through user discursive boundary work, sustaining the mentioned practices as intelligible for other users, and outlining what counts as legitimately participation on the platform. The study contributes to existing research on the roles voting plays on online platforms, as well as offers implications for research on social and material organization of users' online practices. The study also discusses that it is the ambiguity around the mechanics of voting that allows practices to co-exist. While this ambiguity is often discussed by users as problematic, we suggest as potential implication of our study that it may be productive to design platforms for workable forms of ambiguity allowing knowledge practices to co-exist in tension and to provide space for user negotiations of these practices.

Early prediction of promising expert users on community question answering sites

Early prediction of promising expert users on community question answering sites

Interpretable answer retrieval based on heterogeneous network embedding

Community question answering is a rising technology based on users' autonomous interactive behaviors, such as posting their issues, answering questions based on their experience, and commenting on existing questions. As a result of its use of natural language for communication and stimulation of user interest in information sharing, it has increasingly taken the place of other channels as the main way that people learn new things. Multi-type entity characteristics fusion and poor answer interpretability are the two major concerns that currently plague community answer prediction research. The Interpretable Answer Retrieval Method Based on Heterogeneous Network Embedding (IARHNE) is what we present in this work. It combines complex entity features and generates interpretable predicted answers. In order to incorporate the interactions of several kinds of individuals in answer social retrieval, we first build a heterogeneity graph. In order to acquire entity embeddings, we secondly use the heterogeneous graph neural network. We then adopt the vector distance to convert the entity matching problem in the heterogeneous information network into a homogeneous node similarity job. Finally, using entity correlation to predict answers, we provide a list of answers to the new query and interpret them using meta-paths. Comparative studies using three authentic datasets demonstrate the benefits of IARHNE for interpretative question-answering research.

The digital operating room : Chances and risks of artificial intelligence

At the central workplace of the surgeon the digitalization of the operating room has particular consequences for the surgical work. Starting with intraoperative cross-sectional imaging and sonography, through functional imaging, minimally invasive and robot-assisted surgery up to digital surgical and anesthesiological documentation, the vast majority of operating rooms are now at least partially digitalized. The increasing digitalization of the whole process chain enables not only for the collection but also the analysis of big data. Current research focuses on artificial intelligence for the analysis of intraoperative data as the prerequisite for assistance systems that support surgical decision making or warn of risks; however, these technologies raise new ethical questions for the surgical community that affect the core of surgical work.

Collusive spam detection from Chinese community question answering sites: A collective classification framework

With Community Question Answering (CQA) sites evolving into quite popular knowledge-sharing platforms on the Internet, they have also become ideal places for various spammers to spread fake or promotional information. Recently, with the rapid development of crowdsourcing systems, numerous malicious users have launched organized spam campaigns, conducting many spam accounts to carry out collusive spamming activities on CQA sites. In these campaigns, the spammers do not act independently but post deceptive questions and answers (Q&As) collaboratively, which makes the Q&As closely related to each other, but the spam clues of them are even less visible. Therefore, most existing spam detection works may fail to detect these carefully organized and posted collusive CQA spam. In this paper, taking Baidu Zhidao, a popular CQA platform in Chinese, as the study object, we propose a Collective Classification framework for community Question Answering spam detection (CCQA), which collectively identifies the collusive CQA spam using Q&A features and the correlations among Q&As. First, we define the Deceptive Pattern of Q&As, based on which the real Q&A groups are extracted. Then, we extract several highly discriminative Q&A features from both individual and group levels, and propose several types of correlations, which correlate the Q&As that are more likely to have the same labels. After uniformly modeling the Q&As, features, and correlations in the Attributed Heterogeneous Information Network (AHIN), a semi-supervised collective classification algorithm is proposed to detect the collusive Q&A spam. Experimental results on a real-life dataset demonstrate that CCQA can accurately detect the collusive CQA spam, and outperform a number of competitive baselines.

Layer‐specific imprints of traits within a plant–herbivore–predator network – complementary insights from complementary methods

Who interacts with whom is a key question in community and network ecology. The concept that these interactions may be driven by a match between the traits of consumer and resource species is known as trait‐matching. If trait‐matching would allow for general predictions of interaction structure based on sufficiently few and easily‐measurable traits, then this approach could replace the laborious description of each individual pairwise interaction. To resolve imprints of trait‐matching in a species‐rich tri‐trophic Salix–galler–parasitoid network, and to identify the most relevant traits, we applied five different methods, each approaching the same phenomenon from a different perspective. As traits, we used, body sizes, gall type (position on plant, structure of gall) and phenology, among others, as well as phylogenetic proxies. When jointly applied, the methods demonstrate distinctly different imprints of traits within the two bipartite network elements (Salix–galler versus galler–parasitoid interactions). Of the galler–parasitoid sub‐network's interactions, approximately half were explainable by the species traits used; of the Salix–galler sub‐network's interactions, traits explained at most two‐fifths. Gall type appeared to be the most important structuring trait in both networks. Phylogeny explained as much, or more than did our tested traits, suggesting that traits may be conserved and phylogeny therefore an effective proxy. Overall, the more specialized structure of the Salix–galler network versus the more nested structure of the galler–parasitoid network meant that different methods were more effective at capturing interactions and interaction structure in the different sub‐networks. Thus, our analysis reveals how structuring impacts may vary even between levels within the same multitrophic network, and calls for comparative analyses of trait matching across a wide set of systems and methods.