Community Question Research Articles

Multimodal representative answer extraction in community question answering

To solve the information overload problem of multimodal answers in community question answering (CQA), this paper proposes a multimodal representative answer extraction method. First, the method of similarity calculation between multimodal answers is constructed, and multimodal clustering is used to cluster answers. Then, a binary multi-objective optimization model with three objective functions including multimodal answer coverage, multimodal answer redundancy, and multimodal answer consistency is constructed to extract a representative subset of answers. The improved Beluga whale optimization algorithm (MTRL-BWO), based on tent mapping, reinforcement learning, and multiple swarm strategy, is designed to increase the diversity of the population while avoiding local optima to improve the search capability and solution accuracy of the algorithm. Experimental results show the feasibility and superior performance of the proposed method.

Deep Semantic Understanding and Sequence Relevance Learning for Question Routing in Community Question Answering

Question routing (QR) aims to route newly submitted questions to the potential experts most likely to provide answers. Many previous works formalize the question routing task as a text matching and ranking problem between questions and user profiles, focusing on text representation and semantic similarity computation. However, these works often fail to extract matching features efficiently and lack deep contextual textual understanding. Moreover, we argue that in addition to the semantic similarity between terms, the interactive relationship between question sequences and user profile sequences also plays an important role in matching. In this paper, we proposed two BERT-based models called QR-BERTrep and QR-tBERTint to address these issues from different perspectives. QR-BERTrep is a representation-based feature ensemble model in which we integrated a weighted sum of BERT layer outputs as an extra feature into a Siamese deep matching network, aiming to address the non-context-aware word embedding and limited semantic understanding. QR-tBERTint is an interaction-based model that explores the interactive relationships between sequences as well as the semantic similarity of terms through a topic-enhanced BERT model. Specifically, it fuses a short-text-friendly topic model to capture corpus-level semantic information. Experimental results on real-world data demonstrate that QR-BERTrep significantly outperforms other traditional representation-based models. Meanwhile, QR-tBERTint exceeds QR-BERTrep and QR-BERTint with a maximum increase of 17.26% and 11.52% in MAP, respectively, showing that combining global topic information and exploring interactive relationships between sequences is quite effective for question routing tasks.

MATER: Bi-level matching-aggregation model for time-aware expert recommendation

Community question answering (CQA) websites have grown rapidly, but they face a gap between questions and answerers. This gap causes delays in getting answers and overwhelms potential answerers. Expert recommendation (ER) can bridge this gap by providing suitable answerers for the newly posted questions in time. However, most ER methods have some limitations. First, they focus on the representation of a user’s interest, but they ignore their ability to give high-quality answers (user expertise). Second, they do not consider that the answerers’ interests and expertise may change over time. Third, they only use the accepted answer as a good example, but they ignore other good answers that are not accepted. To address these issues, we propose MATER, a Bi-level Matching-Aggregation Model for Time-Aware Expert Recommendation to address these issues. MATER utilizes a matching-aggregation framework at two levels of sentence and question. MATER first matches the target question with each question in the user’s profile by a sentence-level matching-aggregation model. Next, the question matching results are aggregated by a proposed multi-perspective time-aware aggregation layer that considers both time-aware interest and expertise of a user. We propose to employ a listwise loss function to train our model, which takes into consideration all potential experts in the ranking strategy. Experimental results on six real-world CQA datasets from different domains demonstrate that MATER significantly outperforms state-of-the-art ER methods.

Blockchain from the information systems perspective: Literature review, synthesis, and directions for future research

This paper profiled blockchain studies in information systems (IS) journals from 2016 to 2022. Drawing on the 443 selected articles from 77 IS journals, we proposed a classification scheme from the IS perspective. Current blockchain articles are highly skewed, focusing on research agendas and system design. We proposed a theoretical framework by summarizing the current status of the blockchain literature and highlighting 15 future research questions for the IS research community. We are optimistic that the proposed framework and future research questions can guide blockchain research and advance its scale and impact.

Content-Driven Answer Selection in Community Question Answering Using Clustering and Fuzzy Ranking

Content-Driven Answer Selection in Community Question Answering Using Clustering and Fuzzy Ranking

Memory-Aware Attentive Control for Community Question Answering With Knowledge-Based Dual Refinement

The question answering system in open domain enables a machine to automatically select and generate the answer for questions posed by humans in a natural language form on the website. Previous approaches seek effective ways of extracting the semantic features between question and answer, but the contextual information effects in semantic matching are still limited by short-term memory. As an alternative, we propose an internal knowledge-based end-to-end model, enhanced by an attentive memory network for both answer selection and answer generation tasks by considering the full advantages of the semantics and multifacts (i.e., timescales, topics, and context). In detail, we design a long-term memory to learn the top- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k$</tex-math> </inline-formula> fine-grained similarity representations, where two memory-aware mechanisms aggregate the series of semantic word-level and sentence-level similarities to support the coarse contextual information. Furthermore, we propose a novel memory refinement mechanism with the two-dimensional of writing heads that offer an efficient approach to multiview selection of the salient word pairs. In the training stage, we adopt the transformer-based transfer learning skill to effectively pretrain the model. Experimentally, we compare the state-of-the-art approaches on four public datasets, the experimental results show that the proposed model achieves competitive performance.

Community‐driven science and science education: Living in and navigating the edges of equity, justice, and science learning

Community‐driven science and science education: Living in and navigating the edges of equity, justice, and science learning

Co‐created environmental health science: Identifying community questions and co‐generating knowledge to support science learning

AbstractSocial, political, and cultural complexities observed in environmental justice (EJ) communities require new forms of investigation, science teaching, and communication. Defined broadly, participatory approaches can challenge and change inequity and mistrust in science. Here, we describe Project Harvest and the partnership building and co‐generation of knowledge alongside four EJ communities in Arizona. From 2017 to 2021, Project Harvest centered learning around these communities and the participant experience drove the data sharing practice. The framework of sense‐making is used to analyze how community scientists (CS) are learning within the context of environmental pollution and (in)justice. The environmental health literacy (EHL) framework is applied to document the acquisition of skills that enable protective decision‐making and the capacity of CS to move along the EHL continuum. Using data from surveys, focus groups, and semi‐structured interviews, we are asking how did: (1) Personal connections and local relevancy fuel sense‐making? (2) Data sharing make pollution visible and connect to historical knowledge to either reinforce or modify their existing mental map around pollution? and (3) The co‐creation process build data literacy and a relationship science? Results indicate that due to the program framing, CS personally connected with, and made sense of their data based on use and experience. CS synthesized and connected their pollution history and lived experiences with their data and evaluated contaminant transport. CS saw themselves as part of the process, are taking what they learned and the evidence they helped produce to adopt protective environmental health measures and are applying these skills to new contexts. Here, co‐created science nurtured a new/renewed relationship with science. This science culture rooted in co‐creation, fosters action, trust, and supports ongoing science engagement. The science learning that stems from co‐created efforts can set the pace for social transformation and provide the foundation for structural change.

Building a deep learning-based QA system from a CQA dataset

A man-made machine-reading comprehension (MRC) dataset is necessary to train the answer extraction part of existing Question Answering (QA) systems. However, a high-quality and well-structured dataset with question-paragraph-answer pairs is not usually found in the real world. Furthermore, updating or building an MRC dataset is a challenging and costly affair. To address these shortcomings, we propose a QA system that uses a large-scale English Community Question Answering (CQA) dataset (i.e., Stack Exchange) composed of 3,081,834 question-answer pairs. The QA system adopts a classifier-retriever-summarizer structure design. The question classifier and the answer retriever part are based on a Bidirectional Encoder Representations from Transformers (BERT) Natural Language Processing (NLP) model by Google, and the summarizer part introduces a deep learning-based Text-to-Text Transfer Transformer (T5) model to summarize the long answers. We instantiated the proposed QA system with 140 topics from the CQA dataset (including topics such as biology, law, politics, etc.) and conducted human and automatic evaluations. Our system presented encouraging results, considering that it provides high-quality answers to the questions in the test set and satisfied the requirements to develop a QA system without MRC datasets. Our results show the potential of building automatic and high-performance QA systems without being limited by man-made datasets, a significant step forward in the research of open-domain or specific-domain QA systems.

SISTEM TATA RUANG DESA TRADISIONAL BATAK TOBA DAN RELEVANSINYA DI KAWASAN DANAU TOBA (ANALISIS SOSIOLOGIS)

&lt;p&gt;&lt;em&gt;The main purpose of establishing a village is to meet the needs of the community that founded the village. In order for the community's needs to be met, it is necessary to manage the village potential properly. The purpose of this study was to determine the spatial system of the traditional Batak Toba village which includes the physical and social components of the traditional Batak Toba village and how the social system organizes the physical and social components of a village. This study uses qualitative methods to understand social phenomena. The results of the study indicate that a village for the Toba Batak community is a social order and system in which the community can interact socially, both between individuals and between families. The traditional Batak Toba village is autonomous and has authority. In order for the autonomy and authority of a village to continue to exist, a traditional Batak Toba village lives in a legal order. The running of the legal system is organized by a collective leadership in a village, this collective leadership creates a legal order and a place for community questions. In the event of a violation or dispute, the collective leadership is asked for consideration of the sanctions or penalties that will be given.&lt;/em&gt;&lt;em&gt;&lt;/em&gt;&lt;/p&gt;

Context-aware Answer Selection in Community Question Answering Exploiting Spatial Temporal Bidirectional Long Short-Term Memory

Community Question Answering (CQA) sites provide knowledge sharing facility as the users can post questions and other users can share their answers. The selection of top-quality answers from the set of answers in a thread is a significant and challenging task in Natural Language Processing (NLP). To address this issue, we propose a deep learning based spatial temporal Bidirectional Long Short-Term Memory (Bi-LSTM) algorithm. The existing studies mainly focus only computing semantic similarity between questions and answers using votes given by the users. The proposed hybrid approach, based on both forward and backward, consider question to answer and answer to answer similarity. The forward LSTM captures the spatial impact of the answer to estimate the relevancy, whereas the backward LSTM learns temporal features with the answer to predict the best quality answer. Moreover, spatial Bi-LSTM captures past and future dependencies for a better understanding of context and to improve the effectiveness of answer selection. For extracting meaningful information from noisy text data, data is preprocessed following standard steps such as tokenization, parsing, lemmatization, stop words removal, part of speech tagging and entities extraction. Word embeddings-based Paragraph to vector (par2vec) has additional input nodes to represent paragraph information in vector for context understanding. The empirical analysis carried out on the SemEval CQA dataset shows that the proposed model outperforms state-of-art answer selection approaches.

Ask and Ye shall be Answered: Bayesian tag-based collaborative recommendation of trustworthy experts over time in community question answering

Several challenging issues have yet to be jointly addressed in the recommendation of experts for community question answering, including dynamicity, comprehensive profiling, the incorporation of auxiliary data, and the manipulation of heterogeneous information. We argue that a unified treatment of these issues is beneficial in improving recommendation effectiveness.In this paper, we introduce and formalize a new and more thorough instance of the expert-recommendation task for community question answering, which is conceived to suitably account for the connections among the targeted issues. Moreover, in order to carry out the devised task, we present an innovative Bayesian tag-based approach that systematically handles all of the targeted issues in a coherent and seamlessly unified manner. At the heart of the proposed approach is an unprecedented principled fusion of various types of information. The integrated information enables a peculiar characterization of community members in terms of three inherent properties, i.e., their tag-based temporally-discounted interest, expertise, and willingness to respond. The first property is determined by looking into questions, while the last two are determined from answers. Within a generic question answering community, the three properties of its members allow for selectively routing any question to a specifically addressed set of responders. These are recommended as trustworthy repliers, who are not only actually experts in the themes of the routed question as per its tags, but also still interested in such themes and willing to answer at routing time.An extensive empirical assessment involving real-world benchmark data from heterogeneous domains reveals the higher recommendation effectiveness of the presented approach compared to state-of-the-art competitors.

CHQ- SocioEmo: Identifying Social and Emotional Support Needs in Consumer-Health Questions

General public, often called consumers, are increasingly seeking health information online. To be satisfactory, answers to health-related questions often have to go beyond informational needs. Automated approaches to consumer health question answering should be able to recognize the need for social and emotional support. Recently, large scale datasets have addressed the issue of medical question answering and highlighted the challenges associated with question classification from the standpoint of informational needs. However, there is a lack of annotated datasets for the non-informational needs. We introduce a new dataset for non-informational support needs, called CHQ-SocioEmo. The Dataset of Consumer Health Questions was collected from a community question answering forum and annotated with basic emotions and social support needs. This is the first publicly available resource for understanding non-informational support needs in consumer health-related questions online. We benchmark the corpus against multiple state-of-the-art classification models to demonstrate the dataset’s effectiveness.

How do we elicit more user feedback in the social Q&amp;A community? A consideration of the expertise-required question

PurposeThe current study aims to investigate the factors that impact the feedback received on answers to questions in social Q&amp;A communities and whether the expertise-required question influences the role of these factors on the feedback.Design/methodology/approachTo understand the antecedents and consequences that influence the feedback received on answers to online community questions, the elaboration likelihood model (ELM) is applied in this study. The authors use web data crawling methods and a combination of quantitative analyses. The data for this study came from Zhihu; in total, 353,775 responses were obtained to 1,531 questions, ranging from 49 to 23,681 responses per question. Each answer received 0 to 113,892 likes and 0 to 6,250 comments.FindingsThe answers' cognitive and emotional components and the answerer's influence positively affect user feedback behavior. In addition, the expertise-required question moderates the effects of the answer's cognitive component and emotional component on the user feedback, moderating the effects of the answerer's influence on the user approval feedback.Originality/valueThis study builds upon a limited yet growing body of literature on a theme of great relevance to scholars, practitioners and social media users concerning the effects of the connotation of answers (i.e. their cognitive and emotional components) and the answerer's influence on user feedback (i.e. approval and collaborative feedback) in social Q&amp;A communities. The authors further consider the moderating role of the domain expertise required by the question (expertise-required question). The ELM model is applied to explore the relationships between questions, answers and feedback. The findings of this study add a new perspective to the research on user feedback and have implications for the management of social Q&amp;A communities.

Feature-Alignment-Based Cross-Platform Question Answering Expert Recommendation

Community question answering (CQA), with its flexible user interaction characteristics, is gradually becoming a new knowledge-sharing platform that allows people to acquire knowledge and share experiences. The number of questions is rapidly increasing with the open registration of communities and the massive influx of users, which makes it impossible to match many questions to suitable question answering experts (noted as experts) in a timely manner. Therefore, it is of great importance to perform expert recommendation in CQA. Existing expert recommendation algorithms only use data from a single platform, which is not ideal for new CQA platforms with sparse historical interaction and a small number of questions and users. Considering that many mature CQA platforms (source platforms) have rich historical interaction data and a large amount of questions and experts, this paper will fully mine the information and transfer it to new platforms with sparse data (target platform), which can effectively alleviate the data sparsity problem. However, the feature composition of questions and experts in different platforms is inconsistent, so the data from the source platform cannot be directly transferred for training in the target platform. Therefore, this paper proposes feature-alignment-based cross-platform question answering expert recommendation (FA-CPQAER), which can align expert and question features while transferring data. First, we use the rating predictor composed by the BP network for expert recommendation within the domains, and then the feature matching of questions and experts between two domains by similarity calculation is achieved for the purpose of using the information in the source platform to assist expert recommendation in the target platform. Meanwhile, we train a stacked denoising autoencoder (SDAE) in both domains, which can map user and question features to the same dimension and align the data distributions. Extensive experiments are conducted on two real CQA datasets, Toutiao and Zhihu datasets, and the results show that compared to the other advanced expert recommendation algorithms, this paper’s method achieves better results in the evaluation metrics of MAE, RMSE, Accuracy, and Recall, which fully demonstrates the effectiveness of the method in this paper to solve the data sparsity problem in expert recommendation.

I Know What You Are Searching for: Code Snippet Recommendation from Stack Overflow Posts

Stack Overflow has been heavily used by software developers to seek programming-related information. More and more developers use Community Question and Answer forums, such as Stack Overflow, to search for code examples of how to accomplish a certain coding task. This is often considered to be more efficient than working from source documentation, tutorials, or full worked examples. However, due to the complexity of these online Question and Answer forums and the very large volume of information they contain, developers can be overwhelmed by the sheer volume of available information. This makes it hard to find and/or even be aware of the most relevant code examples to meet their needs. To alleviate this issue, in this work, we present a query-driven code recommendation tool, named Que2Code , that identifies the best code snippets for a user query from Stack Overflow posts. Our approach has two main stages: (i) semantically equivalent question retrieval and (ii) best code snippet recommendation. During the first stage, for a given query question formulated by a developer, we first generate paraphrase questions for the input query as a way of query boosting and then retrieve the relevant Stack Overflow posted questions based on these generated questions. In the second stage, we collect all of the code snippets within questions retrieved in the first stage and develop a novel scheme to rank code snippet candidates from Stack Overflow posts via pairwise comparisons. To evaluate the performance of our proposed model, we conduct a large-scale experiment to evaluate the effectiveness of the semantically equivalent question retrieval task and best code snippet recommendation task separately on Python and Java datasets in Stack Overflow. We also perform a human study to measure how real-world developers perceive the results generated by our model. Both the automatic and human evaluation results demonstrate the promising performance of our model, and we have released our code and data to assist other researchers.

Finding an absolute maximum theoretical power coefficient for ducted wind turbines

This study investigates power coefficients for ducted wind turbines to determine an absolute maximum value corresponding to a maximum ideal power output. A maximum power coefficient using the rotor area in the available kinetic energy is still an open question in the research community. Here, the available kinetic energy of a free stream uses the largest device area, diffuser outlet, funnel inlet, and virtual diffuser outlet areas in the power coefficient calculation. The virtual diffuser outlet is defined as the downstream location at which the static pressure of the fluid inside the stream tube recovers to the pressure of the free stream flow. This study performs a one-dimensional analysis based on the continuity, momentum, and energy principles and the Euler Turbomachinery equation. The results show the absolute maximum power coefficient of 0.384 using the virtual diffuser outlet area in the available kinetic energy calculation. Another finding is that both the maximum ideal power output and the cp = 0.384 happen at the same value of the rotor thrust coefficient, CT,R≈ 2/3. The power coefficient based on the rotor area is more convenient for idealized bare turbines; therefore, the power coefficient based on the virtual diffuser outlet area applies only to ducted wind turbines.

Current issues of the implementation of the right to protect state symbols in sports activities

This article is devoted to current problems of protecting the right of the state to use state symbols (anthem, flag) within the framework of international sports and other events. The subject of the research is to consider the right to perform the national anthem, display the national flag and other symbols through the prism of the state’s intellectual property. The current position of the Russian Federation on the international sports and political arena raises the question for the legal community of the need for legislative regulation of the state's intellectual property rights to state symbols. The authors analyzed the current positions of international organizations in the field of sports regarding the participation of athletes from the Russian Federation in international sports events. The subject of the study is also international legal acts related to the holding of international sporting events and regulations of international sports organizations and federations (IOC, FIFA, UEFA). The legal system, both at the national and international levels, should be structured in such a way that international sports organizations do not have the right to ban the performance of the national anthem and the using of the national flag by a country participating in international sports competitions, because such a ban is a violation of the state's right to identity. The bans imposed by foreign organizations and judicial institutions on the Russian Federation as sanctions are presented by the international community as a compromise for the admission of Russian athletes to participate in international sports competitions in a neutral status, however, from a legal point of view, this approach requires reconsideration. The authors proposed amendments to a number of regulatory legal acts of the Russian Federation: the Constitution of the Russian Federation, the Civil Code of the Russian Federation.

Telmisartan and losartan: The marked differences between their chemical and pharmacological properties may explain the difference in therapeutic efficacy in hospitalized patients with COVID‐19

Telmisartan and losartan: The marked differences between their chemical and pharmacological properties may explain the difference in therapeutic efficacy in hospitalized patients with COVID‐19

Challenges in Forecasting Antimicrobial Resistance.

Antimicrobial resistance is a major threat to human health. Since the 2000s, computational tools for predicting infectious diseases have been greatly advanced; however, efforts to develop real-time forecasting models for antimicrobial-resistant organisms (AMROs) have been absent. In this perspective, we discuss the utility of AMRO forecasting at different scales, highlight the challenges in this field, and suggest future research priorities. We also discuss challenges in scientific understanding, access to high-quality data, model calibration, and implementation and evaluation of forecasting models. We further highlight the need to initiate research on AMRO forecasting using currently available data and resources to galvanize the research community and address initial practical questions.