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  1. Jiang, Z.; Gu, Q.; Yin, Y.; Wang, J.; Chen, D.: GRAW+ : a two-view graph propagation method with word coupling for readability assessment (2019) 0.07 
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    Date
    15. 4.2019 13:46:22
  2. Zhou, Q.; Lee, C.S.; Sin, S.-C.J.; Lin, S.; Hu, H.; Ismail, M.F.F. Bin: Understanding the use of YouTube as a learning resource : a social cognitive perspective (2020) 0.07 
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    Date
    20. 1.2015 18:30:22
  3. Zhao, Y.C.; Peng, X.; Liu, Z.; Song, S.; Hansen, P.: Factors that affect asker's pay intention in trilateral payment-based social Q&A platforms : from a benefit and cost perspective (2020) 0.06 
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    Abstract
    More and more social Q&A platforms are launching a new business model to monetize online knowledge. This monetizing process introduces a more complicated cost and benefit tradeoff to users, especially for askers' concerns. Much of the previous research was conducted in the context of free-based Q&A platform, which hardly explains the triggers that motivate askers' pay intention. Based on the theories of social exchange and social capital, this study aims to identify and examine the antecedents of askers' pay intention from the perspective of benefit and cost. We empirically test our predictions based on survey data collected from 322 actual askers in a well-known trilateral payment-based social Q&A platform in China. The results by partial least squares (PLS) analysis indicate that besides noneconomic benefits including self-enhancement, social support, and entertainment, financial factors such as cost and benefit have significant influences on the perceived value of using trilateral payment-based Q&A platforms. More important, we further identify that the effect of financial benefit is moderated by perceived reciprocity belief, and the effect of perceived value is moderated by perceived trust in answerers. Our findings contribute to the previous literature by proposing a theoretical model that explains askers' behavioral intention, and the practical implications for payment-based Q&A service providers and participants.
  4. Westbrook, L.: Intimate partner violence online : expectations and agency in question and answer websites (2015) 0.06 
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    Abstract
    This article presents the first situation-rooted typology of intimate partner violence (IPV) postings in social question and answer (Q&A) sites. Survivors as well as abusers post high-risk health, legal, and financial questions to Q&A sites; answers come from individuals who self-identify as lawyers, experts, survivors, and abusers. Using grounded theory this study examines 1,241 individual posts, each within its own context, raising issues of agency and expectations. Informed by Savolainen's everyday life information seeking (ELIS) and Nahl's affective load theory (ALT), the resultant Q&A typology suggests implications for IPV service design, policy development, and research priorities.
  5. Dubin, D.; Kwasnik, B.H.; Tangmanee, C.: Elicitation techniques for classification research : pt.1: ordered trees; pt.2: repertory grids; pt.3: q-methodology (1994) 0.06 
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  6. Budzik, J.; Hammond, K.: Q&A: a system for the capture, organization and reuse of expertise (1999) 0.06 
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    Abstract
    It is a time-consuming and difficult task for an individual, a group, or an organization to systematically express and organize their expertise so it can be captured and reused. Yet the expertise of individuals within an organization is perhaps its most valuable resource. Q&A attempts to address this tension by providing an environment in which textual representations of expertise are captured as a byproduct of using the system as a semiautomatic question answering intermediary. Q&A mediates interactions between an expert and a question-asking user. It uses its experience referring questions to expert users to answer new questions by retrieving previously answered ones. If a user's question is not found within the collection of previously answered questions, Q&A suggests the set of experts who are most likely to be able to answer the question. The system then gives the user the option of passing a question along to one or more of these experts. When an expert answers a user's question, the resulting question answer pair is captured and indexed under a topic of the expert's choice for later use, and the answer is sent to the user. Unlike previous work on question-answering systems of this sort, Q&A does not assume a fixed hierarchy of topics. Rather, experts build the hierarchy themselves, as their corpus of questions grows. One of the main contributions of this work is a set of techniques for managing the emerging organization of textual representations of expertise over time by mediating the negotiation of shared representations among multiple experts
  7. Shah, C.; Kitzie, V.: Social Q&A and virtual reference : comparing apples and oranges with the help of experts and users (2012) 0.06 
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    Abstract
    Online question-answering (Q&A) services are becoming increasingly popular among information seekers. We divide them into two categories, social Q&A (SQA) and virtual reference (VR), and examine how experts (librarians) and end users (students) evaluate information within both categories. To accomplish this, we first performed an extensive literature review and compiled a list of the aspects found to contribute to a "good" answer. These aspects were divided among three high-level concepts: relevance, quality, and satisfaction. We then interviewed both experts and users, asking them first to reflect on their online Q&A experiences and then comment on our list of aspects. These interviews uncovered two main disparities. One disparity was found between users' expectations with these services and how information was actually delivered among them, and the other disparity between the perceptions of users and experts with regard to the aforementioned three characteristics of relevance, quality, and satisfaction. Using qualitative analyses of both the interviews and relevant literature, we suggest ways to create better hybrid solutions for online Q&A and to bridge the gap between experts' and users' understandings of relevance, quality, and satisfaction, as well as the perceived importance of each in contributing to a good answer.
  8. Neue Suchmaschine von Q-Sensei ermöglicht mehrdimensionales Navigieren (2009) 0.06 
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    Content
    "Mit dem Ziel, wissenschaftliche Informationen auf eine neue, effizientere Art und Weise zugänglich zu machen, startet die neue Suchmaschine von Q-Sensei, die im Vergleich zu anderen Suchdiensten ein tiefergehendes, komfortableres und präziseres Finden ermöglicht. Die neue Suchmaschine bietet ein multilineares Interface, welches es den Nutzern erlaubt, jederzeit ihre Suche zu steuern, eigene Parameter zu definieren und einen umfassenden Überblick im Zugriff auf Wissen zu behalten. Q-Sensei bietet aktuell Zugang zu sieben Millionen wissenschaftlichen Artikeln, die mit großer Genauigkeit effektiv durchsucht werden können. Erreicht wird das durch die Analyse der Suchergebnisse, wodurch passend zu jeder Suchanfrage automatisch relevante Suchvorschläge angezeigt werden. Diese können wiederum selbst durchsucht werden, was den Nutzern größere Freiheiten bei der Suche bietet als dies bei anderen Suchmaschinen der Fall ist. Die Q-Sensei Technologie verbindet verschiedene Kategorien von Suchvorschlägen, wie z.B. Autor, Stichworte, Sprache und Jahr der Veröffentlichung miteinander, wodurch ein mehrdimensionales Navigieren möglich wird. Durch die Möglichkeit, Suchvorschläge beliebig miteinander zu kombinieren, hinzuzufügen und zu entfernen, können Nutzer ihre Suche jederzeit bequem erweitern und anpassen und so auch Literatur finden, die ihnen ansonsten entgangen wäre.
    Sobald Nutzer die gewünschten Ergebnisse gefunden haben, können sie auf weitere Informationen zu jedem Treffer zugreifen. Dazu zählen Zitate, Webseiten von Herausgebern oder verwandte Wikipedia-Artikel. Außerdem werden weitere verwandte Themen oder Einträge aus der Q-Sensei-Datenbank angezeigt, die als Ausgangspunkt für eine neue Suche dienen können. Ferner haben alle Nutzer die Möglichkeit, Einträge mit eigenen Daten anzureichern oder zu ändern, sowie weitere relevante Informationen wie Webseiten von Autoren oder Zitate im Wiki-Stil einzutragen. Die Q-Sensei Corp. wurde im April 2007 durch den Zusammenschluss der in Deutschland ansässigen Lalisio GmbH und der US-amerikanischen Gesellschaft QUASM Corporation gegründet. Q-Sensei hat seinen vorübergehenden Sitz in Melbourne, FL und betreibt in Erfurt die Tochterfirma Lalisio."
  9. Sieverts, E.G.; Hofstede, M.; Nieuwland, A.; Groeneveld, C.; Zwart, B. de: Software for information storage and retrieval tested, evaluated and compared : pt.6: various additional programs (1993) 0.05 
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    Abstract
    In this article, the sixth in a series on microcomputer software for information storage and retrieval, test results of nine programs are presented and various properties and qualities of these programs are discussed. We discuss additional programs for information storage and retrieval and for text retrieval from several of the various categories which have been looked at in previous instalments. On new (secondary) type of ISR software is defined as administrative software. The program review in this issue are BRS-Search, dtSearch, InfoBank, Micro-OPC, Q&A, STN-PFS, Strix, TINman and ZYIndex. All but dtSearch and ZYIndex can be regarded as primarily classical retrieval packages; Q&A boasts comprehensive administrative features as well; dtSearch and ZYIndex are indexing programs. For ZYIndex a new Windows version has been tested. All other programs run under MS-DOS. For each of the nine programs about 100 facts and test results are tabulated. All the programs are individually discussed as well
    Object
    Q&A
  10. Raban, D.R.: Self-presentation and the value of information in Q&A websites (2009) 0.05 
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    Abstract
    Prior research has shown that social interaction is important for continuation of question-and-answer (Q&A) activity online and that it also leads to monetary rewards. The present research focuses on the link between social interaction and the value of information. Expressions of self-presentation in the interaction between askers and answerers online are studied as antecedents for answer feedback which represents the value of the answer to the asker. This relationship is examined in a Q&A site, specifically, in Google Answers (GA). The results of content analysis performed on sets of questions and answers show that both explicit and implicit social cues are used by the site's participants; however, only implicit expressions of self-presentation are related to the provision of social and monetary feedback, ratings, and tips. This finding highlights the importance of implicit cues in textual communication and lends support to the notion of social capital where both monetary and social forms of feedback are the result of interaction online.
  11. Li, D.; Kwong, C.-P.: Understanding latent semantic indexing : a topological structure analysis using Q-analysis (2010) 0.05 
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    Abstract
    The method of latent semantic indexing (LSI) is well-known for tackling the synonymy and polysemy problems in information retrieval; however, its performance can be very different for various datasets, and the questions of what characteristics of a dataset and why these characteristics contribute to this difference have not been fully understood. In this article, we propose that the mathematical structure of simplexes can be attached to a term-document matrix in the vector space model (VSM) for information retrieval. The Q-analysis devised by R.H. Atkin ([1974]) may then be applied to effect an analysis of the topological structure of the simplexes and their corresponding dataset. Experimental results of this analysis reveal that there is a correlation between the effectiveness of LSI and the topological structure of the dataset. By using the information obtained from the topological analysis, we develop a new method to explore the semantic information in a dataset. Experimental results show that our method can enhance the performance of VSM for datasets over which LSI is not effective.
    Object
    Q-analysis
  12. Kantor, B.; Boros, E.; Melamed, B.; Menkov, V: ¬The information quest : a dynamic model of user's information needs (1999) 0.05 
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    Abstract
    In networked information environments, using server-browser architectures, nearly all information finding episodes become extended interactions between the user and the system. In this setting the system needs some way to "understand" what the user is seeking, as this goal adapts and is modified during a session or a series of sessions. We describe a formal model, in which the model of the user's quest is represented as a generalized abstract "response function" representing the user's response to the information delivered by the system. Representing this response as u(n) = Q(S(n - 1)) shows that the user's utterance u(n) at a time step n is determined according to the user's "response function" Q by the materials S(n - 1) that had been presented up through the previous time step n - 1. The entire history of materials presented thus plays a role in determining the user's response, providing a very rich probe into the precise nature of the user's information quest, here represented by the rule Q. We show how this gives rise naturally to a new model for assimilating relevance feedback information, and to the concept of itineraries in the information network. Finally the concept of an information quest Q, provides a natural framework for considering the time dependence of information about the user's needs, and for various models of information aging. The use and effectiveness of this concept are illustrated with data collected in the Ant World Project at Rutgers
  13. Liu, A.; Zou, Q.; Chu, W.W.: Configurable indexing and ranking for XML information retrieval (2004) 0.05 
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  14. Shen, D.; Chen, Z.; Yang, Q.; Zeng, H.J.; Zhang, B.; Lu, Y.; Ma, W.Y.: Web page classification through summarization (2004) 0.05 
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  15. Wu, Q.: ¬The w-index : a measure to assess scientific impact by focusing on widely cited papers (2010) 0.05 
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    Abstract
    Based on the principles of the h-index, I propose a new measure, the w-index, as a particularly simple and more useful way to assess the substantial impact of a researcher's work, especially regarding excellent papers. The w-index can be defined as follows: If w of a researcher's papers have at least 10w citations each and the other papers have fewer than 10(w+1) citations, that researcher's w-index is w. The results demonstrate that there are noticeable differences between the w-index and the h-index, because the w-index plays close attention to the more widely cited papers. These discrepancies can be measured by comparing the ranks of 20 astrophysicists, a few famous physical scientists, and 16 Price medalists. Furthermore, I put forward the w(q)-index to improve the discriminatory power of the w-index and to rank scientists with the same w. The factor q is the least number of citations a researcher with w needed to reach w+1. In terms of both simplicity and accuracy, the w-index or w(q)-index can be widely used for evaluation of scientists, journals, conferences, scientific topics, research institutions, and so on.
  16. Lou, J.; Fang, Y.; Lim, K.H.; Peng, J.Z.: Contributing high quantity and quality knowledge to online Q&A communities (2013) 0.05 
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    Abstract
    This study investigates the motivational factors affecting the quantity and quality of voluntary knowledge contribution in online Q&A communities. Although previous studies focus on knowledge contribution quantity, this study regards quantity and quality as two important, yet distinct, aspects of knowledge contribution. Drawing on self-determination theory, this study proposes that five motivational factors, categorized along the extrinsic-intrinsic spectrum of motivation, have differential effects on knowledge contribution quantity versus quality in the context of online Q&A communities. An online survey with 367 participants was conducted in a leading online Q&A community to test the research model. Results show that rewards in the reputation system, learning, knowledge self-efficacy, and enjoy helping stand out as important motivations. Furthermore, rewards in the reputation system, as a manifestation of the external regulation, is more effective in facilitating the knowledge contribution quantity than quality. Knowledge self-efficacy, as a manifestation of intrinsic motivation, is more strongly related to knowledge contribution quality, whereas the other intrinsic motivation, enjoy helping, is more strongly associated with knowledge contribution quantity. Both theoretical and practical implications are discussed.
  17. Savolainen, R.: Providing informational support in an online discussion group and a Q&A site : the case of travel planning (2015) 0.05 
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    Abstract
    This study examines the ways in which informational support based on user-generated content is provided for the needs of leisure-related travel planning in an online discussion group and a Q&A site. Attention is paid to the grounds by which the participants bolster the informational support. The findings draw on the analysis of 200 threads of a Finnish online discussion group and a Yahoo! Answers Q&A (question and answer) forum. Three main types of informational support were identified: providing factual information, providing advice, and providing personal opinion. The grounds used in the answers varied across the types of informational support. While providing factual information, the most popular ground was description of the attributes of an entity. In the context of providing advice, reference to external sources of information was employed most frequently. Finally, although providing personal opinions, the participants most often bolstered their views by articulating positive or negative evaluations of an entity. Overall, regarding the grounds, there were more similarities than differences between the discussion group and the Q&A site.
  18. Wu, Q.; Lee, C.S.; Goh, D.H.-L.: Understanding user-generated questions in social Q&A : a goal-framing approach (2023) 0.05 
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    Abstract
    In social Q&A, user-generated questions can be viewed as goal expressions shaping the responses. Several studies have identified askers' goals from questions. However, it remains unclear how questions set goals for responders. To fill this gap, this research applies goal-framing theory. Goal-frames influence responses by attracting responders' attention to different goals. Eight question cues are used to identify gain, hedonic and normative goal-frames. A total of 14,599 posts are collected. To investigate the influence of goal-frames, response networks are constructed. Results reveal that gain goal-frames attract interactions with questions, while hedonic, and normative goal-frames promote interactions among responses. Further, topic types influence the effects of goal-frames. Gain goal-frames increase interactions with questions in Science, Technology, Engineering, and Mathematics (STEM) topics while hedonic and normative goal-frames attract interactions in non-STEM topics. This research leverages responders' perspectives to explain responses to questions, which are influenced by the goals set up by question cues. Beyond that, our findings enrich the empirical knowledge of social Q&A topics, revealing that the influence of questions varies across STEM and non-STEM topics because the question cues for specifying goals are different in the two topics. Our research opens new directions to investigate questions from responders' perspectives.
  19. Miao, Q.; Li, Q.; Zeng, D.: Fine-grained opinion mining by integrating multiple review sources (2010) 0.05 
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  20. Zhou, J.-z.: ¬A new subclass for Library of Congress Classification, QF : Computer science (1998) 0.04 
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    Abstract
    The field of computer science has grown rapidly over the past 20 years. However, the LCC only gives computer science a very limited space (QA75-QA76), which is buried in the QA subclass for mathematics. Although LCC call numbers for computer science may become very long, it is still impossible to differentiate a C programming book from a C++ programming book, or a data structure book using C language from an algorithm book in C language. Analyzes the historical reason for the current LCC system for computer science. Based on sample data from Blackwell's North America's Approval Program Coverage and Cost Study, calculates current and all possible subdivisions within Q class, and reports that computer science has more books published in the last 6 years than in any other physical sciences, yet takes only 2 out of 11.000 current subdivisions in LCC class Q. Concludes with suggestions for creating a completely new subclass QF for computer science

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