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The article reviews various social media platforms used for delivering content about Nazi persecution and the Holocaust, focusing on educational efforts.

There are several initiatives underway to improve the learning of software developers. These attempts include the integration of GitHub into software engineering classes, the creation of learning management systems, gamification approaches, and collaborative learning platforms. These initiatives have demonstrated promise in boosting students' collaborative growth and cooperation abilities, emphasizing their potential influence on improving learning experiences in practical areas. Books, on the other hand, remain basic in education, but their physical size limits their ability to explore all practical elements of a topic in depth. This limitation requires more research and application of theoretical information in real‐world circumstances. In this work, we address the issue of limited space in traditional books that frequently prevents complete presentation of practical elements of a topic. To address this issue, we propose an application that improves the reading experience and accelerates the learning process. To anticipate themes, we use a combination of latent Dirichlet allocation (LDA) algorithms and a generative pre‐trained transformer. First, utilizing LDA to find potential topic keywords inside the text and then leveraging generative pretrained transformer to predict topic names based on the LDA produced keywords. In addition, a query builder module produces and executes queries depending on the current page's topic, obtaining real‐world issues from Stack Overflow. The system classifies results by query‐title similarity, question‐answer ranking, and content quality before displaying them to users. This bridges the gap between theoretical knowledge and practical application. We illustrate the usefulness of suggested tool using simulations, comparison with existing tools and user studies. The majority of users provide favorable comments and find it interesting and helpful for improving the learning process. [ABSTRACT FROM AUTHOR]

In order to enhance the shopping experience of customers and retain them, thereby increasing sales volume, the author proposes the research topic of an intelligent recommendation system for product information on e-commerce platforms based on robot customer service automatic question answering. Firstly, starting directly from the question itself, the system can provide feedback to customers by simply segmenting the questions submitted by customers and matching them with semantic templates; Secondly, the system automatically builds and updates the user's personalized knowledge base, using this to predict user purchasing tendencies and achieve the function of recommending products to customers. The implementation of the intelligent shopping robot system has passed 365 question and answer tests on 5G mobile phone sales terms, and is feasible in the professional field. The experimental results indicate that, when the number of training corpora increased to 300, the accuracy of the system was 0.85, 0.90, and 0.98 using 100, 2003, and 300 tests respectively. Such a system is perfect for natural language processing, so we can improve the system by expanding and improving the knowledge base. The intelligent shopping robot recommendation system studied by the author is still in the analysis and demonstration stage, sincerely hope that the processing method used in this project can have reference significance for similar recommendation systems in the near future. [ABSTRACT FROM AUTHOR]

Community question answering sites (CQAs) are often flooded with questions that are never answered. To cope with the problem, experienced users of Stack Exchange are now allowed to mark newly posted questions as closed if they are of poor quality. Once closed, a question is no longer eligible to receive answers. However, identifying and closing subpar questions takes time. Therefore, the purpose of this article is to develop a supervised machine learning system that predicts question closibility, the possibility of a newly posted question to be eventually closed. Building on extant research on CQA question quality, the supervised machine learning system uses 17 features that were grouped into four categories, namely, asker features, community features, question content features and textual features. The performance of the developed system was tested on questions posted on Stack Exchange from 11 randomly chosen topics. The classification performance was generally promising and outperformed the baseline. Most of the measures of precision, recall, F1-score and area under the receiver operating characteristic curve (AUC) were above 0.90 irrespective of the topic of questions. By conceptualising question closibility, the article extends previous CQA research on question quality. Unlike previous studies, which were mostly limited to programming-related questions from Stack Overflow, this one empirically tests question closibility on questions from 11 randomly selected topics. The set of features used for classification offers a framework of question closibility that is not only more comprehensive but also more parsimonious compared with prior works. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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Based on motivational theory, this study investigated the effect of users' knowledge seeking on users' knowledge contribution in question-and-answer (Q&A) communities. We collected 4643 samples from the largest social Q&A platform in China (Zhihu) and applied a mediation effect test to the data. The results showed that knowledge seeking affects intrinsic motivations (altruism and self-efficacy) and extrinsic motivations (social support, group identity, and reputation), further affecting knowledge contribution. Our findings indicated that Q&A communities should be concerned with users' intrinsic and extrinsic motivations to ensure balanced knowledge exchange on social Q&A platforms, ultimately fostering long-term stability and growth. Existing research has mainly focused on a single behavioral state, such as knowledge seeking or knowledge contribution, and has paid little attention to the connection between these two types of user information behaviors. This study aimed to fill this gap by revealing the mechanisms through which users' knowledge seeking affects their knowledge contribution. [ABSTRACT FROM AUTHOR]

Purpose: Users' knowledge contribution behaviors are critical for online Q&A communities to thrive. Well-organized question threads in online Q&A communities enable users to clearly read existing answers and their evaluations before contributing. Based on the social comparison and peer influence literature, the authors examine peer influence on the informativeness of knowledge contributions in competitive settings. The authors also consider three levels of moderating factors concerning individuals' perception of competitiveness: question level, thread level and contributor level. Design/methodology/approach: The authors collected data from one of the largest online Q&A communities in China. The hypotheses were validated using hierarchical linear models with cross-classified random effects. The generalized propensity score weighting method was employed for the robustness check. Findings: The authors demonstrate the peer influence due to social comparison concerns among knowledge contribution behaviors in the same question thread. If more prior knowledge contributors choose to contribute long answers in the question thread, the subsequent contributions are more informative. This peer influence is stronger for factual questions and questions with higher popularity of answering but weaker in recommendation-type and well-answered questions and for contributors with higher social status. Originality/value: This research provides a new cue of peer influence on online UGC contributions in competitive settings initiated by social comparison concerns. Additionally, the authors identify three levels of moderating factors (question level, thread level and contributor level) that are specific to online Q&A settings and are related to a contributor's perception of competitiveness, which affect the direct effect of peer influence on knowledge contributions. Rather than focus on motivation and quality evaluation, the authors concentrate on the specific content of online knowledge contributions. Peer influence here is not based on an actual acquaintance or a following relationship but on answering the same question. The authors also illustrate the competitive peer influence in subjective and personalized behaviors in online UGC communities. [ABSTRACT FROM AUTHOR]

Online knowledge communities (OKCs), such as question-and-answer sites, have become increasingly popular venues for knowledge sharing. Accordingly, it is necessary for researchers and practitioners to develop effective and efficient text analysis tools to understand the massive amount of user-generated content (UGC) on OKCs. Unsupervised topic modeling has been widely adopted to extract humaninterpretable latent topics embedded in texts. These identified topics can be further used in subsequent analysis and managerial practices. However, existing generic topic models that assume documents are independent are inappropriate for analyzing OKCs where structural relationships exist between questions and answers. Thus, a new method is needed to fill this research gap. In this study, we propose a new topic model specifically designed for the text in OKCs. We make three primary contributions to the research on topic modeling in this context. First, we build a general and flexible Bayesian framework to explicitly model structural and temporal dependencies among texts. Second, we statistically demonstrate the approximate model inference using mean-field and coordinate ascent algorithms. Third, we showcase the practical value and relative merit of our method via a specific downstream task (i.e., user profiling). The proposed model is illustrated using two real-world datasets from well-known OKCs (i.e., Stack Exchange and Quora), and extensive experiments demonstrate its superiority over several cutting-edge benchmarks. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

This paper investigates the ramifications of information feed integration on user engagements and contributions in online content-sharing platforms by exploiting a natural experiment occurred in a leading knowledge-sharing platform that integrated informal social posts with professional knowledge content in one feed. Our results show that the juxtaposition of incongruous types of content increased mindset switching and cognitive strain, thus hurting user engagements. We also reveal a novel crowding-out effect, viz., the integration heightened concerns that posting informal social posts would dilute the contributor's professional image, thus inhibiting user contributions. Our findings hold important practical implications for all platforms that host (or are considering hosting) diverse types of user-generated content (UGC). Additional content curation tools can potentially enhance user engagement and retention, but their effectiveness hinges on a foundational and crucial element—the presentation format of heterogeneous content types. Essentially, the value of curating informal social posts in a knowledge-sharing platform would diminish when those content intrudes upon and conflict with the professional domain. This insight underscores that any UGC platforms, when adopting a diversity-oriented strategy, should pay close attention to heterogeneity between different content types for the purpose of optimizing user experiences and promoting user contributions. Many online communities that rely on effortful, voluntary content contributions offer additional content curation tools to facilitate social interactions and encourage user contributions. Any platform that offers two or more heterogeneous content types (e.g., expert knowledge and social posts) faces a choice about the presentation format: whether to display the content types separately or in an integrated information feed. We leverage a natural experiment on Zhihu, a Q&A platform that offers a social-interaction-oriented functionality called Ideas. Zhihu initially presented answers (expert knowledge content) and ideas (social posts) in two different information feeds, but the platform integrated ideas into the same information feed as answers in June 2019. We find that information feed integration significantly decreased user engagement with and contribution of both ideas and answers. We hypothesize that users decreased their engagement because the juxtaposition of incongruous types of content increased mindset switching and cognitive strain. This hypothesis is supported by an additional laboratory experiment. We also present evidence showing that contributions decreased both because of the decrease in engagement (weaker social recognition incentives) and because integration heightened concerns that posting ideas would dilute the contributor's professional image. Our findings have important theoretical and practical implications for any platform that hosts heterogeneous content. History: Xiaoquan (Michael) Zhang served as the senior editor and Yili (Kevin) Hong served as associate editor for this article. Funding: Z. Cao acknowledges this research was funded by National Natural Science Foundation of China [Grants 72201238, 72192823]. G. Li acknowledges this research was funded by National Natural Science Foundation of China [Grant 72102047] and Shanghai Pujiang Program [Grant 21PJC006]. Supplemental Material: The e-companion is available at https://doi.org/10.1287/isre.2022.0043. [ABSTRACT FROM AUTHOR]

The need for collective intelligence in technology means that online Q&A platforms, such as Stack Overflow and Reddit, have become invaluable in building the global knowledge ecosystem. Despite literature demonstrating a prevalence of inclusion and contribution disparities in online communities, studies investigating the underlying reasons behind such fluctuations remain scarce. The current study examines Stack Overflow users' contribution profiles, both in isolation and relative to various diversity metrics, including GDP and access to electricity. This study also examines whether such profiles propagate to the city and state levels, supplemented by granular data such as per capita income and education, before validating quantitative findings using content analysis. We selected 143 countries and compared the profiles of their respective users to assess implicit diversity-related complications that impact how users contribute. Results show that countries with high GDP, prominent R&D presence, less wealth inequality and sufficient access to infrastructure tend to have more users, regardless of their development status. Similarly, cities and states where technology is more prevalent (e.g., San Francisco and New York) have more users who tend to contribute more often. Qualitative analysis reveals distinct communication styles based on users' locations. Urban users exhibited assertive, solution-oriented behaviour, actively sharing information. Conversely, rural users engaged through inquiries and discussions, incorporating personal anecdotes, gratitude and conciliatory language. Findings from this study may benefit scholars and practitioners, allowing them to develop sustainable mechanisms to bridge the inclusion and diversity gaps. [ABSTRACT FROM AUTHOR]

Community based question and answer websites; such as, Yahoo Answers, Stack Overflow, Stack Exchange and Quora are briskly generating content in the form of questions and answers posted by users. These online discussion forums allow the users to append and edit the content easily. Therefore, a huge amount of content has accumulated over the time, which is beneficial for the search engine users for addressing their queries to the related content. On the other hand, the task of maintaining the answer quality and ranking of retrieved results has always been a challenging task for the websites that accumulate user's generated data. Here, the problem of ranking the answers based on the user's satisfaction is investigated. The best answers based on their ranking are identified. Three techniques are proposed, which are Relevance Features (REL_FS), Readability Features (RED_FS), and Weighted Answer Rank WEIGHTED ANS_RANK. REL_FS performs a summation of relevance features, RES_FS sums the readability features and WEIGHTED ANS_RANK performs a summation of REL_FS and RED_FS to deal with the task. Proposed methods compute the rank of answers according to both the relevance and readability of answers. Experimental results show proposed methods ability of providing effective ranking of answers for the selection of best answers. Moreover, it also measures the consistency of ranked answers with the help of a detailed correlational analysis. [ABSTRACT FROM AUTHOR]

As an emerging application, generative AI has attracted many users to conduct question and answer (Q&A), which may lead to their defection from social Q&A communities. Based on the push-pull-mooring (PPM) model, this research examined user migration intention from social Q&A communities to generative AI. Data were analyzed using a mixed method of SEM and fsQCA. The results revealed that migration intention is influenced by a combination of push factors (information overload and community fatigue), pull factors (perceived anthropomorphism, perceived accuracy, perceived trustworthiness, and flow experience), and mooring factor (social influence). The fsQCA results identified three main paths leading to migration intention. These results imply that Q&A communities need to reduce information overload and mitigate users' fatigue in order to retain them and achieve a sustainable development. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

A policy subsystem is a system of relations between actors within the context of a specific, territorially bounded policy issue. Mature policy subsystems feature well‐established, easily distinguishable, confrontational coalitions. Recent literature has explored the behavior of nascent subsystems, which emerge in response to novel policy challenges and feature developing, rather than fully fledged, coalitions. However, as yet, we lack an empirical approach to identify and analyze the structural characteristics of nascent subsystems and assess their implications for theoretical and subsystem development. How do we recognize a nascent policy subsystem when we see one? What are the drivers of its nascent coalitional structure? We answer these questions using social network analysis in the empirical case of the governance network of adaptation to sea‐level rise in the San Francisco Bay Area, using original data collected in 2018. We find that the network portrays a nascent subsystem developing out of pre‐existing coalitions focused on two facets of environmental advocacy: environmental protection and environmental justice. We conclude with recommendations for future research on policy subsystem development. [ABSTRACT FROM AUTHOR]

This paper argues for generation to be incorporated into the analysis of social reproduction to open new ways of thinking about the significance of children's unpaid work in and for their families. This paper situates its argument in relation to social reproduction theory and the conceptualization of generation in childhood studies and development studies. It draws on a longitudinal study of girls growing up in contemporary Benin and Togo conducted by Plan Benin and Plan Togo. This paper shows how the work of social reproduction is distributed across the household with children, especially girls, playing a large part in these activities. Trading and farming are the main economic activities of women, and girls gradually extend their knowledge of how to farm and trade as they get older. This paper concludes that placing generation into the centre of social reproduction theory will not only make visible the work that children do in subsistence economies but is also important for answering the perennial question of social reproduction theory in capitalist economies: who pays for that 'strange commodity', 'living labour' to be reproduced. [ABSTRACT FROM AUTHOR]

Research on how people look for and interact with information has a long history in the information field. The current literature has been repeatedly reviewed in earlier volumes of Annual Review of Information Science and Technology. In this review, we offer an overview of the research published in this area in the years 2016–2022 with a focus on the trends that have emerged in this period. We use the term “information behavior” as an umbrella for the research area interested in how people become informed and engage with information in diverse manners acknowledging that different researchers and subfields prefer other terms and frameworks, including information practices, information experience, and health information seeking, to name a few. We reviewed 1270 articles in the field published in the years 2016–2022 and identified seven emerging trends: The CoVID‐19 Pandemic, Diversity and Inclusion, Embodiment, Misinformation and Trust, Social Q&A Websites, Collaboration, and Information Creation. The reviewed literature and trends are discussed in relation to their significance for information, earlier review of information behavior research, and the long‐debated issue of theory‐driven versus atheoretical research in the field. [ABSTRACT FROM AUTHOR]

As a popular application, social question-and-answer (Q&A) platforms have similar functions and it is relatively easy for users to switch from a platform to another one. This may lead to users' defection. From a push–pull-mooring perspective, this work examined the determinants of user switching between social Q&A platforms. We found that both push factors (dissatisfaction and fatigue) and pull factors (content quality and user experience) positively affect switching intention, which leads to switching behavior. The mooring factor (switching cost) has a negative moderation effect on switching intention. These results suggest that social Q&A platforms can improve content quality and users' experience, and reduce their dissatisfaction and fatigue, thus preventing users' switching and retaining them. [ABSTRACT FROM AUTHOR]

Dealing with many of the problems related to the quality of textual content online involves identifying similar content. Algorithmic solutions for duplicate content classification typically rely on text vector representation, which maps textual information into a set of features. Ideally, this representation would capture all aspects of the underlying text, including length, word frequencies, syntax, and semantics. While recent advancements in text representation have led to improved performance, a comprehensive approach that explicitly incorporates all text features has not yet been proposed. In this study, we present the INCEPT framework that utilizes multiple representation methods to detect duplicate text pairs, taking advantage of their individual strengths. The core of our approach involves using a stacking ensemble of pairwise vector distance measurements that are computed from multiple text representation methods. A stacking classifier then utilizes these distance scores as input and learns to identify duplicate posts. We assess the proposed framework's effectiveness in identifying duplicate posts in an online Question and Answer platform. By combining several text representation methods, INCEPT performs well in the duplicate posts classification task. Our experiments demonstrate that specific framework configurations outperform the accuracy scores obtained from individual text representation methods. Therefore, we also infer that no single text representation method can independently capture a text's features. [ABSTRACT FROM AUTHOR]

Purpose: Based on the perspectives of social capital, image motivation and motivation affordances, this paper explores the direct and moderation effects of different kinds of motivations (i.e. relationship-based motivation, community-based motivation and individual-based motivation) on users' continuous knowledge contributions in social question and answer (Q&A) communities. Design/methodology/approach: The authors collect the panel data of 10,193 users from a popular social Q&A community in China. Then, a negative binomial regression model is adopted to analyze the collected data. Findings: The paper demonstrates that social learning, peer recognition and knowledge seeking positively affect users' continuous contribution behaviors. However, the results also show that social exposure has the opposite effect. In addition, self-presentation is found to moderate the influence of social factors on users' continuous use behaviors, while the moderation effect of motivation affordances has no significance. Originality/value: First, this study develops a comprehensive motivation framework that helps gain deeper insights into the underlying mechanism of knowledge contribution in social Q&A communities. Second, this study conducts panel data analysis to capture the impacts of motivations over time, rather than intentions at a fixed time point. Third, the findings can help operators of social Q&A communities to optimize community norms and incentive mechanisms. [ABSTRACT FROM AUTHOR]

Background: Risk aversion due to depression is common among older adults, and social participation is associated with improved mental health and a lower risk of late-life depression. However, little is known about the connection between participation in social activities and risky financial decisions among adults with depression. Thus, we aim to examine the connection between participation in social activities and taking financial risks and investing in risky financial assets (with high-return potential) in such individuals, differentiated by age and gender. The study also focuses on analyzing the percentage of investors within each social activity, their attendance frequency, and motivation. Methods: The data was obtained from the Survey of Health, Ageing and Retirement in Europe (SHARE) database Wave 2 (2006–2010). The study included 8,769 individuals aged 50 + with depression caseness, from 15 European countries and Israel who answered the question on participation in social activities and reported financial risk-taking intentions or behaviors (investing in stocks or shares, mutual funds or managed investment accounts, and both). The study utilized Pearson chi-square, odds ratios, Z, and hierarchical logistic regression tests. Results: The odds for taking financial risks and investing in risky financial assets were higher for those participating in social activities compared to those who did not, on both intentional (by 173%) and behavioral (by 240–397%) levels. Such social activities (attended at least once a week, without financial motivation) have been shown to be primarily represented by educational or training courses — where 33% of participants invested in risky financial assets. The connection persisted after controlling for gender, age, marital status, children, income. Conclusions: By overcoming the subjects' financial risk aversion, participation in social activities may help improve mental health in individuals aged 50 + with depression caseness. This has important implications for policymakers in healthcare, who by updating healthcare policies can fund and facilitate participation in social activities. As a result, the national healthcare system may benefit from lower hospitalization-related expenses, and generate higher cash flows into the country's economy using the population's renewed interest in investing available funds. These results are relevant in the wake of COVID-19 that increased loneliness and depression rates. [ABSTRACT FROM AUTHOR]

Apart from the well-recognized popularity and importance of online question-and-answer (Q&A) communities, the sustainable growth of these platforms is a serious concern faced by online knowledge-sharing platforms. Personality traits play a significant role in shaping human behavior and decision-making. Numerous studies have attempted to understand the volunteer knowledge contribution and provide solutions to improve low participation. However, the role of personality traits of online knowledge-sharing community users in their volunteer knowledge contribution is still unexplored. Based on the big five personality traits, we have proposed a model to understand the role of personality features of online users. We have collected a cross-sectional dataset from online Q&A community users and applied the SEM-ANN model to conclude our model results that will provide insight into online community users' contribution behavior. Findings show that agreeableness, openness, extrovert, and conscientiousness positively and neuroticism negatively influence online participation to contribute knowledge. We have also observed that the influence of personality traits significantly differs for both genders, and negativity bias is stronger in male users. Study results provide new insight into understanding online users' behavior and provide a distinct angle to understand and add value to previous studies' findings on online Q&A community users. [ABSTRACT FROM AUTHOR]

Background ChatGPT is an online language-based platform designed to answer questions in a human-like way, using deep learning -technology. Objectives To examine the diagnostic capabilities of ChatGPT using real-world anonymized medical dermatology cases. Methods Clinical information from 90 consecutive patients referred to a single dermatology emergency clinic between June and December 2022 were examined. Thirty-six patients were included. Anonymized clinical information was transcribed and input into ChatGPT 4.0 followed by the question 'What is the most likely diagnosis?' The suggested diagnosis made by ChatGPT was then compared with the diagnosis made by dermatology. Results After inputting clinical history and examination data obtained by a dermatologist, ChatGPT made a correct primary diagnosis 56% of the time (n = 20). Using the clinical history and cutaneous signs recorded by nonspecialists, it was able to make a correct diagnosis 39% of the time (n = 14). This was similar to the diagnostic rate of nonspecialists (36%; n = 13), but it was much lower than that of dermatologists (83%; n = 30). There was no differential offered by referring sources 28% of the time (n = 10), unlike ChatGPT, which provided a differential diagnosis 100% of the time. Qualitative analysis showed that ChatGPT offered responses with caution, often justifying its reasoning. Conclusions This study illustrates that while ChatGPT has a diagnostic capability, in its current form it does not significantly improve the diagnostic yield in primary or secondary care. [ABSTRACT FROM AUTHOR]

Ecological communities are inherently dynamic: species constantly turn over within years, months, weeks or even days. These temporal shifts in community composition determine essential aspects of species interactions and how energy, nutrients, information, diseases and perturbations 'flow' through systems. Yet, our understanding of community structure has relied heavily on static analyses not designed to capture critical features of this dynamic temporal dimension of communities. Here, we propose a conceptual and methodological framework for quantifying and analysing this temporal dimension. Conceptually, we split the temporal structure into two definitive features, sequence and duration, and review how they are linked to key concepts in ecology. We then outline how we can capture these definitive features using perspectives and tools from temporal graph theory. We demonstrate how we can easily integrate ongoing research on phenology into this framework and highlight what new opportunities arise from this approach to answer fundamental questions in community ecology. As climate change reshuffles ecological communities worldwide, quantifying the temporal organization of communities is imperative to resolve the fundamental processes that shape natural ecosystems and predict how these systems may change in the future. [ABSTRACT FROM AUTHOR]

Purpose: The current study aims to investigate the factors that impact the feedback received on answers to questions in social Q&A communities and whether the expertise-required question influences the role of these factors on the feedback. Design/methodology/approach: To 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. Findings: The 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/value: This 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&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&A communities. [ABSTRACT FROM AUTHOR]

Background and Objectives: Community question-answering (CQA) websites have become increasingly popular as platforms for individuals to seek and share knowledge. Identifying users with a special shape of expertise on CQA websites is a beneficial task for both companies and individuals. Specifically, finding those who have a general understanding of certain areas but lack expertise in other fields is crucial for companies who are planning internship programs. These users, called dash-shaped users, are willing to work for low wages and have the potential to quickly develop into skilled professionals, thus minimizing the risk of unsuccessful recruitment. Due to the vast number of users on CQA websites, they provide valuable resources for finding individuals with various levels of expertise. This study is the first of its kind to directly classify CQA users based solely on the textual content of their posts. Methods: To achieve this objective, we propose an ensemble of advanced deep learning algorithms and traditional machine learning methods for the binary classification of CQA users into two categories: those with dash-shaped expertise and those without. In the proposed method, we used the stack generalization to fuse the results of the dep and machine learning methods. To evaluate the effectiveness of our approach, we conducted an extensive experiment on three large datasets focused on Android, C#, and Java topics extracted from the Stack Overflow website. Results: The results on four datasets of the Stack Overflow, demonstrate that our ensemble method not only outperforms baseline methods including seven traditional machine learning and six deep models, but it achieves higher performance than state-of-the-art deep models by an average of 10% accuracy and F1-measure. Conclusion: The proposed model showed promising results in confirming that by using only their textual content of questions, we can classify the users in CQA websites. Specifically, the results showed that using the contextual content of the questions, the proposed model can be used for detecting the dash-shaped users precisely. Moreover, the proposed model is not limited to detecting dash-shaped users. It can also classify other shapes of expertise, such as T- and C-shaped users, which are valuable for forming agile software teams. Additionally, our model can be used as a filter method for downstream applications, like intern recommendations. [ABSTRACT FROM AUTHOR]

Graphs are widely used for representing pairwise interactions in complex systems. Since such real-world graphs are large and often evergrowing, sampling subgraphs is useful for various purposes, including simulation, visualization, stream processing, representation learning, and crawling. However, many complex systems consist of group interactions (e.g., collaborations of researchers and discussions on online Q&A platforms) and thus are represented more naturally and accurately by hypergraphs than by ordinary graphs. Motivated by the prevalence of large-scale hypergraphs, we study the problem of sampling from real-world hypergraphs, aiming at answering (Q1) how can we measure the goodness of sub-hypergraphs, and (Q2) how can we efficiently find a "good" sub-hypergraph. Regarding Q1, we distinguish between two goals: (a) representative sampling, which aims at capturing the characteristics of the input hypergraph, and (b) back-in-time sampling, which aims at closely approximating a past snapshot of the input time-evolving hypergraph. To evaluate the similarity of the sampled sub-hypergraph to the target (i.e., the input hypergraph or its past snapshot), we consider 10 graph-level, hyperedge-level, and node-level statistics. Regarding Q2, we first conduct a thorough analysis of various intuitive approaches using 11 real-world hypergraphs. Then, based on this analysis, we propose MiDaS and MiDaS-B, designed for representative sampling and back-in-time sampling, respectively. Regarding representative sampling, we demonstrate through extensive experiments that MiDaS, which employs a sampling bias toward high-degree nodes in hyperedge selection, is (a) Representative: finding overall the most representative samples among 15 considered approaches, (b) Fast: several orders of magnitude faster than the strongest competitors, and (c) Automatic: automatically tuning the degree of sampling bias. Regarding back-in-time sampling, we demonstrate that MiDaS-B inherits the strengths of MiDaS despite an additional challenge—the unavailability of the target (i.e., past snapshot). It effectively handles this challenge by focusing on replicating universal evolutionary patterns, rather than directly replicating the target. [ABSTRACT FROM AUTHOR]

Studies on representations of feminism have seldom addressed the variance across social media platforms, leaving the underlying dynamics that shape feminist portrayals within this important digital context unexamined. This research contrasts representations of feminism on Weibo and Zhihu, China's leading microblogging and question-and-answer platforms, using a corpus-based method. Employing Sketch Engine, I extracted and analyzed noun bigrams containing feminism from self-compiled corpora to explore the similarities and distinctions in representation. The results indicate that both platforms predominantly portray feminism as seeking female privilege via radical and aggressive approaches, despite Weibo's emphasis on methods and Zhihu's on purposes. In addition, the Weibo discourse more frequently presents feminism as an emerging social movement and employs a greater variety of distinguishing attributes to describe the movement. The findings reveal a shared public perception of feminism, while also highlighting the influence of distinct user demographics and platform architectures. This research augments the current literature by identifying the varied representations of feminism on different social media platforms and providing novel insights into its perception within the Chinese context. [ABSTRACT FROM AUTHOR]

Despite the proliferation of tools available to search for information online, such as search engines and generative artificial intelligence (AI), many people still actively use social question-and-answer (Q&A) platforms. This study examines the best answerers on Korean social Q&A platforms as key content producers and potential entrepreneurs. The characteristics of the best answerers, who are the core contributors to social Q&A platforms, were analysed based on the 903 best answerers from the social Q&A platform Naver Knowledge-iN, a leading social Q&A platform in South Korea. Research hypotheses were formulated based on three key dimensions: the intrinsic characteristics of the answers (length, immediacy, and similarity with the question), attributes of the answerer (self-introduction, credentials beyond the social Q&A platform), and level of engagement of the answerer on the platform (variety in the field of answers, diversity in role activities on the platform). An in-depth analysis using a multiple regression model was conducted to test the hypotheses. The findings of the analysis results indicate that enhanced user performance is positively correlated with several factors. Specifically, longer answers, high similarity between questions and answers, additional credentials beyond the social Q&A platform, a broader range of fields answered, and active participation as both asker and answerer all contribute to improved user performance. These findings highlight what sets social Q&A platforms apart from other search tools such as search engines or generative AI chatbots. This study has practical implications for social Q&A platforms seeking to identify the best contributors who are likely to produce quality content and are potential entrepreneurs. [ABSTRACT FROM AUTHOR]

We Published a step–up approach for robotic training in hepato-pancreato-biliary (HPB) surgery has been previously. The approach was mostly based on personal experience and communications between experts and needed appraisal and validation by the HPB surgical community. At the Great Britain and Ireland HPB Association (GBIHPBA) robotic HPB meeting held in Coventry, UK in October 2022, the authors sought consensus from the live audience, with an open forum for answering key questions. The aim of this exercise was to appraise the step-up approach, and in turn, lay the foundation for a more substantial UK robotic HPB surgical curriculum. The study was conducted using VEVOX online polling platform at the October 2022 GBIHPBA robotic HPB meeting in Coventry, UK. The questionnaire was designed based on a literature search and was externally validated. The data were collated and analysed to assess patterns of response. A median (IQR) of 104 (96–117) responses were generated for each question. 93 consultants and 61 trainees were present Over 90% were in favour of a standardised training pathway. 93.6% were in favour of the proposed step-up approach, with a significant number (67.3%; p < 0.001) considering three levels of case complexity. The survey shows a favourable outlook on adopting step-up training in robotic HPB surgery. Ongoing monitoring of progress, clinical outcomes, and collaboration among surgeons and units will bolster this evidence, potentially leading to an official UK robotic HPB curriculum. [ABSTRACT FROM AUTHOR]

• Multi-entity fusion and low interpretability of answers are urgent issues • We employ heterogeneous graphs for entity fusion of community question answering • The heterogeneous graph neural network is used for entity relation embedding • Meta-paths represent user intention and are pioneered for answer interpretability • Experiments on real datasets verify the interpretable question-answering performance 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. [ABSTRACT FROM AUTHOR]

The global pandemic motivated the emergence of grassroots celebrities in healthcare who led online opinion through their professional insights and sharing of health and medical-related knowledge in China. A certain number of practitioners introduced their professional guidance to general netizens through pay-for-answer subscription with the help of Chinese social media. This article interrogates grassroots celebrities via Zhihu, China's largest community for questions and answers (CQA). It extends Western-focused scholarship on microcelebrities. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Catfishing is a form of online deception where an individual presents themselves as an identity that is not their own. The study reported in this article explored the motivations for catfish perpetrators and the impacts on those who had been catfished in terms of the emotions and feelings expressed by victims. Data was collected using the crowd-sourced question and answer website Quora [1] and analysis was conducted on a pre-existing corpus of data which contained participant's answers to questions on catfishing. An automated linguistic analysis using Linguistic Inquiry and Word Count (LIWC-2022 [2]) and a thematic analysis (Braun and Clarke in Qual Res Psychol 3:77–101, 2006 [38]) were conducted on participant's descriptions of their perceptions of the motivations of catfishes and their catfishing experiences. The thematic analysis indicated that the motivations of catfish perpetrators can be linked to entertainment, emulating an ideal self, desiring meaningful interaction, and financial gain. Six emotions and feelings emerged from the accounts of catfishing victims: suspicion, love, depression, anger, embarrassment, and stupidity. These findings contribute to an understanding of what motivates individuals to catfish and suggests further research to explore specific emotions and feelings that catfish victims experience. The LIWC analysis and language style matching analysis showed that the data collected was mostly personal to each individual and that there were similarities in how victims write about their experiences. Therefore, we suggest that LIWC has promise as a method of providing added context to qualitative data analysis methods. [ABSTRACT FROM AUTHOR]

In this, the second iteration of our continuing 'Harbingers of Change' project, over 160 early career researchers (ECRs) from eight countries were questioned about their scholarly communications. Three repeat in‐depth interviews were conducted over 2 years of the pandemic to chart changes in attitude and behaviour. This paper provides interview findings (and an extensive literature review) regarding the role played by social media platforms. It was prompted by an initial review of the data that highlighted significant changes in the attitudes and behaviour of Malaysian ECRs, especially in respect to WhatsApp. Data were provided from several open‐ended interview questions about social media, a few interviewer prompts for individual platform names, and, indirectly, from 'mentions' of individual platforms in answers to a range of questions about scholarly communications. Findings were: (1) 15 platforms were mentioned in respect to a range of scholarly activities, most notably visibility and reach; (2) Twitter (now X) was the most mentioned, by half the ECRs; (3) 'churn' is a notable observation, with large gains and losses in mentions occurring between interviews; (4) large differences between countries, with Malaysian ECRs very much leading on the adoption of social media and largely responsible for the rise of WhatsApp, French ECRs showing little interest, and China a different space where WeChat reigns supreme and foreign platforms are blocked. [ABSTRACT FROM AUTHOR]

Objective: On October 7, 2023, the Hamas group launched a multi-pronged attack from the Gaza Strip to the south of Israel. This action, which was the beginning of armed conflicts between Israel and Palestinian militant groups led by Hamas, has been named the “Al-Aqsa storm operation” by Hamas and other armed Palestinian groups and “Black Saturday” or “Simchat Torah Massacre” by Israel. In today's world, the media, an integral part of human life, are the main tools people use to make sense of the world. The media's importance in war conditions and crises has doubled, and they have become the most essential sources of reliable information and awareness of the world and its changes. The start of the Hamas attacks on Israel quickly made headlines around the world, and based on their news policies, the media took specific approaches to reflecting this event. The present study investigates the media representation of the Israel-Gaza war in the IRNA news agency, BBC Persian website, Al-Arabiya Persian website, and Deutsche Welle Persian website. For this purpose, Theon Van Dijk's critical discourse analysis method was used in this article to investigate the media representation of the Israel-Gaza war in the IRNA news agency, BBC Persian website, Al-Arabiya Persian website, and Deutsche Welle Persian website and to answer the research questions including "1. How is the media representation of the IsraelGaza war in different media outlets including IRNA news agency, BBC Persian website, AlArabiya Persian website, and Deutsche Welle Persian website?” and “2. What are the differences and similarities between the discourses of these media outlets in the construction of the Israel-Gaza war?” Methods: Based on the presented definitions, discourse can be considered an intellectual system consisting of expressions and cognitive and epistemological attitudes that creates a specific way of thinking that serves the interests of a specific group. Some sociologists also consider discourse to be synonymous with ideology. Theon Van Dijk believes that ideology is the basis of common social representations of a group and influences attitudes, judgments, and what members of the group consider to be right or wrong. Van Dijk considers ideologies as cognitive symmetries of power and states that they should work best to serve the interests of a particular group. Critical discourse analysis seeks to answer the question of how discourses reproduce the involved macrostructures. Results: The results of this research showed that the mentioned media have presented different discourses on the Israel-Gaza war, each expressing the ideological position from which they operate. Therefore, the media seek to construct reality rather than act as a mirror that reflects the developing events. IRNA news agency’s discourse emphasized that the return of the issue of Palestine to its rightful place in the world’s public opinion was the primary goal of carrying out the Al-Aqsa storm operation. At the same time, this issue is the consequence of the actions of the international community, especially some countries in the Islamic world that sought to normalize relations with the Zionist regime. IRNA news agency sees breaking the silence of the international community in response to the Palestinian issue and Israel's actions, disrupting the process of normalizing relations between the Arab governments and the Zionist regime, returning the Palestinian issue to its rightful place, and dealing a solid blow to the information and propaganda power of the Zionist regime among the achievements of this operation. In BBC Persian’s discourse, the actions of Hamas were considered to be the leading cause of the war, and the subsequent decision of Israel to start the war was seen as a retaliatory action. BBC Persian describes Hamas fighters as violent and merciless people who have attacked Israeli settlements without any warning and have committed terrible crimes in these areas; therefore, the international community should support the Israeli people and soldiers who are united to fight Hamas. Conclusions: The result of this discourse-making method that was presented in the early days of the war was that when Israel attacked Gaza in the following months, BBC Persian was biased in reporting the fate of the people of Gaza and representing the crimes committed by Israel. In the review of Deutsche Welle Persian news website, a point of view similar to that of BBC Persian was observed; however, Deutsche Welle tried to show Islam as the main cause of these actions, placing Islam in opposition to the West. In this medium's discourse, Hamas is introduced as an ideology and political idea formed on the axis of religion. This issue has caused Hamas to emerge as a social movement that has many followers in Palestine, and the Palestinian people participate in what this group does by supporting Hamas's ideology. In this regard, Deutsche Welle refers to the battles and struggles of the Western allies of Israel with other Islamic extremist groups, such as Al-Qaeda and ISIS, to show Hamas as a part of the Islamic world, which past experiences have shown to be the birthplace of the most extremist ideas. Al-Arabiya Persian's discourse emphasized Iran's role as a negotiator in preventing Hezbollah's involvement in this war. In addition, in the early days of this operation, AlArabiya Persian reduced it to the level of an individual reaction, seeking to minimize the disruption of Israel’s relations with Arab countries. What is important to Al-Arabiya Persian is not to create tension in Arab-Israeli relations. [ABSTRACT FROM AUTHOR]

Hiring knowledgeable and cost-effective individuals, who use their knowledge and expertise to boost the organization, is extremely important for organizations as they are the most valuable assets. T-shaped experts are the best option based on agile methodology. The T-shaped professional has a deep understanding of one topic and broad knowledge of several others. Compared to other types of professionals, T-shaped professionals are better communicators and cheaper to hire. Finding T-shaped experts in a given skill area requires determining each candidate's depth of knowledge and shape of expertise. To estimate each candidate's depth of knowledge in a given skill area, we propose a translation-based method that utilizes two attention-based skill translation models to overcome the vocabulary mismatch between skills and user documents. We also propose two new approaches based on binary cross-entropy and focal loss to determine whether each user is T-shaped. Our experiments on three collections of the StackOverflow dataset demonstrate the efficiency of our proposed method compared to the state-of-the-art approaches. [ABSTRACT FROM AUTHOR]

This document is a tutorial on multiple linear regression, which explores the use of additional predictor variables in the modeling process. It provides an Excel file with raw data and figures, and addresses key questions such as how to use multiple predictor variables, the importance of community intelligence, and methods for selecting predictor variables. The tutorial also discusses the basics of multivariate linear statistical models and computational methods for estimating coefficients. It emphasizes the concepts of redundant information, overfitting, and workload when using multiple predictor variables. The text further discusses the application of linear regression and other machine learning methods in analyzing and predicting umbrella sales. It explains the process of rescaling variables and calculating correlation coefficients to determine the relationships between rainfall, advertising cost, and umbrella sales. The text also explores the concept of community intelligence and the importance of harnessing diverse perspectives in understanding these relationships. Additionally, it discusses different methods for selecting predictor variables and estimating future demand using causal forecasting and time series forecasting. The text emphasizes the need for caution and the integration of statistical models with domain knowledge. [Extracted from the article]

The growth of digital platforms has led to the proliferation of Online Communities, providing individuals with opportunities to seek help and share knowledge. A key challenge of help-related platforms that address technical questions (i.e., utilitarian, rather than opinion or supportive) is to ensure the contributions address seekers' specific information needs. Despite growing academic interest in such platforms, research has mainly focused on factors that influence the quantity of contributions, ignoring whether these contributions effectively helped the seekers. To fill this research gap, this study draws upon theories of self-determination and motivation crowding to examine contributing behaviors that result in successful helping. By analyzing a rich dataset collected from an online Q&A platform, we find that gains in a help provider's past rewards positively influence the success of contribution. Further, while previous studies suggest that external rewards result in a high quantity of contribution, our findings show that an inflated frequency of contribution leads to a crowding-out effect. Specifically, the contribution frequency has a curvilinear relationship with the success of the contribution. Taken together, these findings demonstrate there is a need to revisit the gamification mechanism on help-related platforms to ensure the success of knowledge contribution. This is crucial for the sustainability of these platforms as low-quality answers can lead users to mistrust and eventually leave the platform. [ABSTRACT FROM AUTHOR]

Virtual collaborative Q&A communities generate shared knowledge through the interaction of people and content. This knowledge is often fragmented, and its value as a collective, collaboratively formed product, is largely overlooked. Inspired by work on individual mental semantic networks, the current study explores the networks formed by user-added associative links as reflecting an aspect of self-organization within the communities' collaborative knowledge sharing. Using eight Q&A topic-centered discussions from the Stack Exchange platform, it investigated how associative links form internal structures within the networks. Network analysis tools were used to derive topological indicator metrics of complex structures from associatively-linked networks. Similar metrics extracted from 1000 simulated randomly linked networks of comparable sizes and growth patterns were used to generate estimated sampling distributions through bootstrap resampling, and 99% confidence intervals were constructed for each metric. The discussion-network indicators were compared against these. Results showed that participant-added associative links largely led to networks that were more clustered, integrated, and included posts with more connections than those that would be expected in random networks of similar size and growth pattern. The differences were observed to increase over time. Also, the largest connected subgraphs within the discussion networks were found to be modular. Limited qualitative observations have also pointed to the impacts of external content-related events on the network structures. The findings strengthen the notion that the networks emerging from associative link sharing resemble other information networks that are characterized by internal structures suggesting self-organization, laying the ground for further exploration of collaborative linking as a form of collective knowledge organization. It underscores the importance of recognizing and leveraging this latent mechanism in both theory and practice. [ABSTRACT FROM AUTHOR]

Purpose: This study employed self-determination theory (SDT) and the "Motivational affordance–Psychological outcomes–Behavioral outcomes" framework to investigate the relationship between gamification features and knowledge-sharing behavior in online communities. Design/methodology/approach: A theoretical model was tested with 281 Chinese users from an online social question and answer (Q&A) community. Partial least square structural equation modeling was applied to analyze the data. Findings: The empirical results revealed that competence mediated the effects of immersion and achievement-related gamification features on knowledge sharing. Moreover, relatedness mediated the effects of immersion, achievement and social-related gamification features on knowledge sharing. Research limitations/implications: This study was conducted on a Chinese Q&A platform, and the results may not be generalizable to other cultures or service providers with different goals. Practical implications: The study's findings indicate that gamification could serve as an effective toolkit for incentivizing and promoting knowledge sharing in online communities. The findings thus provide strategic insights for administrators of online communities seeking to leverage gamification designs to encourage user participation in knowledge-sharing activities. Originality/value: Research on the role of gamification in promoting knowledge sharing has been limited in scope and has focused on tourism comment communities. Little evidence exists on the effect of gamification within social Q&A communities. Further, the finding of gamification's positive role in motivating knowledge sharing indicates the need for the knowledge-sharing field to focus on contextual factors. [ABSTRACT FROM AUTHOR]

How do forensic examiners know if they have altered an image stream when converting a digital image from one codec or file container to another for analysis? Forensic standards and best practices recommend avoiding alteration or degradation of multimedia data during transcoding. An image stream hashing method was recently introduced to the forensic science community to answer the question above. This paper offers an initial validation study of image stream hashing method that may answer the question above. The first half of the study's experiments tested the image stream hashing method to measure fitness for use in forensic science while identifying errors and limitations. The study's second phase analyzed the systematic errors detected in initial tests to discover error causation. Causation analysis identified four method limitations subsequently used to develop proposed standard controls of method operations. The final study phase repeated the initial experiments used in the first phase while implementing the proposed standard controls of method operations. Initial test results indicated the method had significant error rates, limiting the effectiveness of the method to only three of the five file types used in the study. The final testing phase revealed that implementing proposed standard controls of method operations reduced the potential systematic errors to a negligible level when using the image stream hashing method for content verification. The validation study concluded that examiners could use the image stream hashing method for forensic science only by implementing error mitigation techniques that utilize the proposed standard controls of method operations. [ABSTRACT FROM AUTHOR]

The semantic-based method for question retrieval is an important method for searching similar questions in community question answering (CQA). The major challenges in question retrieval lie in polysemy and lexical gaps between questions, and the quality of retrieved similar questions by semantic retrieval model might not be high enough to effectively solve one's doubts. In order to address these challenges, a high-quality and multi-level semantic analysis-based similar question retrieval framework named HQML-QR is proposed, which consists of semantic representation from tag-level and sentence-level semantics for question retrieval (TS-QR) and multi-dimensional quality analysis (MDQQ). Firstly, TS-QR extracts multi-level semantic features of the question contents, where graph embedding model is utilized to learn coarse-grained semantics of questions from the scope of the tag. Meanwhile, in order to effectively identify polysemy and extract fine-grained sentence semantic of questions, TS-QR integrates the pre-trained language model based on self-attention mechanism to ensure the accuracy of question retrieval. Secondly, based on the quality factors in CQA (i.e., popularity, question, answer and user), MDQQ constructs a multi-dimensional quality evaluation model to provide a reasonable quality measurement standard for questions. Under the guidance of the quality of questions, the similarity score obtained by semantic vector matching is updated to retrieve high-quality and semantically similar questions. Finally, experiments are executed on CQADupStack dataset from Stack Overflow and the experimental results show that the P@N of HQML-QR has an average increase of 5.65%, 4.44% and 4.34% compared with LDA-VSM-SEM, WET-QR, RCM-QR, respectively. [ABSTRACT FROM AUTHOR]

Different community question-answering systems have been evolving as online fora for knowledge sharing among users across the world. However, finding experts on the questions is a key issue in the community question-answering system. To address this issue, a novel approach is proposed in this paper for finding experts from the community question-answering website using an exact string-matching algorithm along with domain knowledge. The proposed system is designed with three phases: i) User Profile Modeling, ii) Question Preprocessing, and iii) Expert Recommendation. Initially, the matrix factorization model is adopted for user profile modeling where the tags and the answerer's information are represented in the form of a matrix. Then, the domain-wise grouping of tags is done to minimize the search time of the tags. The question preprocessing is done using a keyword extraction algorithm to extract the keywords. Finally, the expert recommendation is accomplished using the trie string matching algorithm and key-value mapping process. For doing experiments, a stack overflow community question-answering website is utilized in this work. The performance of the system is measured and the results section proved that the system achieved 91.2% accuracy. [ABSTRACT FROM AUTHOR]

This study addresses the scarcity of research examining technology's impact on learners of English as a foreign language in English for specific purposes (ESP) courses within English-medium instruction (EMI) programs. Focusing on a business communication course using a social question-answering platform, this study explores the interplay among English proficiency, motivational orientations, and online engagement. Surveying 126 university students via a mixed-method approach, this research identifies distinct motivational orientations (intrinsic, extrinsic, and self-efficacy) existing during class activities, influenced by proficiency levels, with notable differences in online participation between beginner and advanced learners. The findings suggest that while online activities enhance peer interaction and teacher feedback, they may trigger anxiety in beginners, potentially diminishing motivation and self-efficacy. These results emphasize the need to tailor technology integration to proficiency levels and motivational orientations in ESP/EMI settings. Practical recommendations are offered for educators aiming to implement effective online activities and provide targeted support, particularly for beginner learners, thereby bolstering their motivation and selfefficacy. [ABSTRACT FROM AUTHOR]