Search (661 results, page 1 of 34)

0.11369465 = product of:
  0.17054197 = sum of:
    0.01886051 = weight(_text_:on in 1209) [ClassicSimilarity], result of:
      0.01886051 = score(doc=1209,freq=4.0), product of:
        0.109763056 = queryWeight, product of:
          2.199415 = idf(docFreq=13325, maxDocs=44218)
          0.04990557 = queryNorm
        0.1718293 = fieldWeight in 1209, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          2.199415 = idf(docFreq=13325, maxDocs=44218)
          0.0390625 = fieldNorm(doc=1209)
    0.15168147 = product of:
      0.30336294 = sum of:
        0.30336294 = weight(_text_:demand in 1209) [ClassicSimilarity], result of:
          0.30336294 = score(doc=1209,freq=16.0), product of:
            0.31127608 = queryWeight, product of:
              6.237302 = idf(docFreq=234, maxDocs=44218)
              0.04990557 = queryNorm
            0.9745784 = fieldWeight in 1209, product of:
              4.0 = tf(freq=16.0), with freq of:
                16.0 = termFreq=16.0
              6.237302 = idf(docFreq=234, maxDocs=44218)
              0.0390625 = fieldNorm(doc=1209)
      0.5 = coord(1/2)
  0.6666667 = coord(2/3)

Abstract

Improving health literacy through health information dissemination is one of the most economical and effective mechanisms for improving population health. This process needs to fully accommodate the thematic suitability of health information supply and demand and reduce the impact of information overload and supply-demand mismatch on the enthusiasm of health information acquisition. We propose a health information topic modeling analysis framework that integrates deep learning methods and clustering techniques to model the supply-side and demand-side topics of health information and to quantify the thematic alignment of supply and demand. To validate the effectiveness of the framework, we have conducted an empirical analysis on a dataset with 90,418 pieces of textual data from two prominent social networking platforms. The results show that the supply of health information in general has not yet met the demand, the demand for health information has not yet been met to a considerable extent, especially for disease-related topics, and there is clear inconsistency between the supply and demand sides for the same health topics. Public health policy-making departments and content producers can adjust their information selection and dissemination strategies according to the distribution of identified health topics, thereby improving the effectiveness of public health information dissemination.

0.10348095 = product of:
  0.15522142 = sum of:
    0.013336393 = weight(_text_:on in 1039) [ClassicSimilarity], result of:
      0.013336393 = score(doc=1039,freq=2.0), product of:
        0.109763056 = queryWeight, product of:
          2.199415 = idf(docFreq=13325, maxDocs=44218)
          0.04990557 = queryNorm
        0.121501654 = fieldWeight in 1039, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          2.199415 = idf(docFreq=13325, maxDocs=44218)
          0.0390625 = fieldNorm(doc=1039)
    0.14188503 = product of:
      0.28377005 = sum of:
        0.28377005 = weight(_text_:demand in 1039) [ClassicSimilarity], result of:
          0.28377005 = score(doc=1039,freq=14.0), product of:
            0.31127608 = queryWeight, product of:
              6.237302 = idf(docFreq=234, maxDocs=44218)
              0.04990557 = queryNorm
            0.9116346 = fieldWeight in 1039, product of:
              3.7416575 = tf(freq=14.0), with freq of:
                14.0 = termFreq=14.0
              6.237302 = idf(docFreq=234, maxDocs=44218)
              0.0390625 = fieldNorm(doc=1039)
      0.5 = coord(1/2)
  0.6666667 = coord(2/3)

Abstract

Accurate demand forecasting plays a critical role in hotel revenue management. Online reviews have emerged as a viable information source for hotel demand forecasting. However, existing hotel demand forecasting studies leverage only sentiment information from online reviews, leading to capturing insufficient information. Furthermore, prevailing hotel demand forecasting methods either lack explainability or fail to capture local correlations within sequences. In this study, we (1) propose a comprehensive framework consisting of four components: expertise, sentiment, popularity, and novelty (ESPN framework), to investigate the impact of online reviews on hotel demand forecasting; (2) propose a novel dual attention-based long short-term memory convolutional neural network (DA-LSTM-CNN) model to optimize the model effectiveness. We collected online review data from Ctrip.com to evaluate our proposed ESPN framework and DA-LSTM-CNN model. The empirical results show that incorporating features derived from the ESPN improves forecasting accuracy and our DA-LSTM-CNN significantly outperforms the state-of-the-art models. Further, we use a case study to illustrate the explainability of the DA-LSTM-CNN, which could guide future setups for hotel demand forecasting systems. We discuss how stakeholders can benefit from our proposed ESPN framework and DA-LSTM-CNN model.

0.06424023 = product of:
  0.09636035 = sum of:
    0.032007344 = weight(_text_:on in 1212) [ClassicSimilarity], result of:
      0.032007344 = score(doc=1212,freq=8.0), product of:
        0.109763056 = queryWeight, product of:
          2.199415 = idf(docFreq=13325, maxDocs=44218)
          0.04990557 = queryNorm
        0.29160398 = fieldWeight in 1212, product of:
          2.828427 = tf(freq=8.0), with freq of:
            8.0 = termFreq=8.0
          2.199415 = idf(docFreq=13325, maxDocs=44218)
          0.046875 = fieldNorm(doc=1212)
    0.064353004 = product of:
      0.12870601 = sum of:
        0.12870601 = weight(_text_:demand in 1212) [ClassicSimilarity], result of:
          0.12870601 = score(doc=1212,freq=2.0), product of:
            0.31127608 = queryWeight, product of:
              6.237302 = idf(docFreq=234, maxDocs=44218)
              0.04990557 = queryNorm
            0.4134786 = fieldWeight in 1212, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              6.237302 = idf(docFreq=234, maxDocs=44218)
              0.046875 = fieldNorm(doc=1212)
      0.5 = coord(1/2)
  0.6666667 = coord(2/3)

Abstract

This paper has a critical and a constructive part. The first part formulates a political demand, based on ethical considerations: Until 2050, there should be a global moratorium on synthetic phenomenology, strictly banning all research that directly aims at or knowingly risks the emergence of artificial consciousness on post-biotic carrier systems. The second part lays the first conceptual foundations for an open-ended process with the aim of gradually refining the original moratorium, tying it to an ever more fine-grained, rational, evidence-based, and hopefully ethically convincing set of constraints. The systematic research program defined by this process could lead to an incremental reformulation of the original moratorium. It might result in a moratorium repeal even before 2050, in the continuation of a strict ban beyond the year 2050, or a gradually evolving, more substantial, and ethically refined view of which if any kinds of conscious experience we want to implement in AI systems.

0.04832534 = product of:
  0.07248801 = sum of:
    0.01886051 = weight(_text_:on in 5982) [ClassicSimilarity], result of:
      0.01886051 = score(doc=5982,freq=4.0), product of:
        0.109763056 = queryWeight, product of:
          2.199415 = idf(docFreq=13325, maxDocs=44218)
          0.04990557 = queryNorm
        0.1718293 = fieldWeight in 5982, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          2.199415 = idf(docFreq=13325, maxDocs=44218)
          0.0390625 = fieldNorm(doc=5982)
    0.053627502 = product of:
      0.107255004 = sum of:
        0.107255004 = weight(_text_:demand in 5982) [ClassicSimilarity], result of:
          0.107255004 = score(doc=5982,freq=2.0), product of:
            0.31127608 = queryWeight, product of:
              6.237302 = idf(docFreq=234, maxDocs=44218)
              0.04990557 = queryNorm
            0.3445655 = fieldWeight in 5982, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              6.237302 = idf(docFreq=234, maxDocs=44218)
              0.0390625 = fieldNorm(doc=5982)
      0.5 = coord(1/2)
  0.6666667 = coord(2/3)

Abstract

As the humanities develop in the realm of increasingly more pronounced digital scholarship, it is important to provide quality subject access to a vast range of heterogeneous information objects in digital services. The study aims to paint a representative picture of the current state of affairs of the use of subject index terms in humanities journal articles with particular reference to the well-established subject access needs of humanities researchers, with the purpose of identifying which improvements are needed in this context. Design/methodology/approach The comparison of subject metadata on a sample of 649 peer-reviewed journal articles from across the humanities is conducted in a university repository, against Scopus, the former reflecting local and national policies and the latter being the most comprehensive international abstract and citation database of research output. Findings The study shows that established bibliographic objectives to ensure subject access for humanities journal articles are not supported in either the world's largest commercial abstract and citation database Scopus or the local repository of a public university in Sweden. The indexing policies in the two services do not seem to address the needs of humanities scholars for highly granular subject index terms with appropriate facets; no controlled vocabularies for any humanities discipline are used whatsoever. Originality/value In all, not much has changed since 1990s when indexing for the humanities was shown to lag behind the sciences. The community of researchers and information professionals, today working together on digital humanities projects, as well as interdisciplinary research teams, should demand that their subject access needs be fulfilled, especially in commercial services like Scopus and discovery services.

0.035714075 = product of:
  0.053571112 = sum of:
    0.010669114 = weight(_text_:on in 1107) [ClassicSimilarity], result of:
      0.010669114 = score(doc=1107,freq=2.0), product of:
        0.109763056 = queryWeight, product of:
          2.199415 = idf(docFreq=13325, maxDocs=44218)
          0.04990557 = queryNorm
        0.097201325 = fieldWeight in 1107, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          2.199415 = idf(docFreq=13325, maxDocs=44218)
          0.03125 = fieldNorm(doc=1107)
    0.042902 = product of:
      0.085804 = sum of:
        0.085804 = weight(_text_:demand in 1107) [ClassicSimilarity], result of:
          0.085804 = score(doc=1107,freq=2.0), product of:
            0.31127608 = queryWeight, product of:
              6.237302 = idf(docFreq=234, maxDocs=44218)
              0.04990557 = queryNorm
            0.2756524 = fieldWeight in 1107, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              6.237302 = idf(docFreq=234, maxDocs=44218)
              0.03125 = fieldNorm(doc=1107)
      0.5 = coord(1/2)
  0.6666667 = coord(2/3)

Abstract

In recent years, many school librarians have been scrambling to build and expand their graphic novel collections to meet the large and growing demand for these materials. For the purposes of this study, the term graphic novels refers to volumes in which the content is provided through sequential art, including fiction, nonfiction, and biographical material. As the library field has not yet arrived at a set of best practices or guidelines for institutions working to classify and catalog graphic novels, this study seeks to record the ways in which school librarians are handling these materials as well as issues and questions at the forefront of their minds. A survey of school librarians in the United States revealed that almost all of them collect fiction and nonfiction graphic novels, while 67% collect manga. Most respondents indicated that they are partly or solely responsible for the cataloging and classification decisions made in their media centers. For classification purposes, most have elected to create separate graphic novel collections to house their fictional graphic novels. Some include nonfiction graphic novels in this section, while others create a nonfiction graphic novel collection nearby or shelve nonfiction graphic novels with other items that deal with similar subject matter. Many school librarians express uncertainty about how best to catalog and classify longer series, adapted classics, superhero stories, and the increasing number and variety of inventive titles that defy categorization. They also struggle with inconsistent vendor records and past practices and suffer from a lack of full confidence in their knowledge of how to best classify and catalog graphic novels so that they are both searchable in the library catalog and easily accessible on the shelves.

0.034792386 = product of:
  0.052188575 = sum of:
    0.035284784 = weight(_text_:on in 5835) [ClassicSimilarity], result of:
      0.035284784 = score(doc=5835,freq=14.0), product of:
        0.109763056 = queryWeight, product of:
          2.199415 = idf(docFreq=13325, maxDocs=44218)
          0.04990557 = queryNorm
        0.3214632 = fieldWeight in 5835, product of:
          3.7416575 = tf(freq=14.0), with freq of:
            14.0 = termFreq=14.0
          2.199415 = idf(docFreq=13325, maxDocs=44218)
          0.0390625 = fieldNorm(doc=5835)
    0.01690379 = product of:
      0.03380758 = sum of:
        0.03380758 = weight(_text_:22 in 5835) [ClassicSimilarity], result of:
          0.03380758 = score(doc=5835,freq=2.0), product of:
            0.1747608 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.04990557 = queryNorm
            0.19345059 = fieldWeight in 5835, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.0390625 = fieldNorm(doc=5835)
      0.5 = coord(1/2)
  0.6666667 = coord(2/3)

Abstract

Purpose The purpose of this paper is to identify key countries and their focal research fields on the information divide. Design/methodology/approach Literature was retrieved to identify key countries and their primary focus. The literature research method was adopted to identify aspects of the primary focus in each key country. Findings The key countries with literature on the information divide are the USA, China, the UK and India. The problem of health is prominent in the USA, and solutions include providing information, distinguishing users' profiles and improving eHealth literacy. Economic and political factors led to the urban-rural information divide in China, and policy is the most powerful solution. Under the influence of humanism, research on the information divide in the UK focuses on all age groups, and solutions differ according to age. Deep-rooted patriarchal concepts and traditional marriage customs make the gender information divide prominent in India, and increasing women's information consciousness is a feasible way to reduce this divide. Originality/value This paper is an extensive review study on the information divide, which clarifies the key countries and their focal fields in research on this topic. More important, the paper innovatively analyzes and summarizes existing literature from a country perspective.

Date

13. 2.2020 18:22:13

0.033380013 = product of:
  0.050070018 = sum of:
    0.026404712 = weight(_text_:on in 896) [ClassicSimilarity], result of:
      0.026404712 = score(doc=896,freq=4.0), product of:
        0.109763056 = queryWeight, product of:
          2.199415 = idf(docFreq=13325, maxDocs=44218)
          0.04990557 = queryNorm
        0.24056101 = fieldWeight in 896, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          2.199415 = idf(docFreq=13325, maxDocs=44218)
          0.0546875 = fieldNorm(doc=896)
    0.023665305 = product of:
      0.04733061 = sum of:
        0.04733061 = weight(_text_:22 in 896) [ClassicSimilarity], result of:
          0.04733061 = score(doc=896,freq=2.0), product of:
            0.1747608 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.04990557 = queryNorm
            0.2708308 = fieldWeight in 896, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.0546875 = fieldNorm(doc=896)
      0.5 = coord(1/2)
  0.6666667 = coord(2/3)

Abstract

JASIST has in recent years received many submissions reporting data analytics based on "Big Data" of online reviews scraped from various platforms. By outlining major issues in this type of scape-and-report scholarship and providing a set of recommendations, this essay encourages online reviews researchers to look at Big Data with a critical eye and treat online reviews as a sociotechnical "thing" produced within the fabric of sociomaterial life.

Date

22. 1.2023 18:33:53

0.031149916 = product of:
  0.046724875 = sum of:
    0.029821085 = weight(_text_:on in 5844) [ClassicSimilarity], result of:
      0.029821085 = score(doc=5844,freq=10.0), product of:
        0.109763056 = queryWeight, product of:
          2.199415 = idf(docFreq=13325, maxDocs=44218)
          0.04990557 = queryNorm
        0.271686 = fieldWeight in 5844, product of:
          3.1622777 = tf(freq=10.0), with freq of:
            10.0 = termFreq=10.0
          2.199415 = idf(docFreq=13325, maxDocs=44218)
          0.0390625 = fieldNorm(doc=5844)
    0.01690379 = product of:
      0.03380758 = sum of:
        0.03380758 = weight(_text_:22 in 5844) [ClassicSimilarity], result of:
          0.03380758 = score(doc=5844,freq=2.0), product of:
            0.1747608 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.04990557 = queryNorm
            0.19345059 = fieldWeight in 5844, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.0390625 = fieldNorm(doc=5844)
      0.5 = coord(1/2)
  0.6666667 = coord(2/3)

Abstract

Purpose This paper investigates how school teachers look for informational texts for their classrooms. Access to current, varied and authentic informational texts improves learning outcomes for K-12 students, but many teachers lack resources to expand and update readings. The Web offers freely available resources, but finding suitable ones is time-consuming. This research lays the groundwork for building tools to ease that burden. Design/methodology/approach This paper reports qualitative findings from a study in two stages: (1) a set of semistructured interviews, based on the critical incident technique, eliciting teachers' information-seeking practices and challenges; and (2) observations of teachers using a prototype teaching-oriented news search tool under a think-aloud protocol. Findings Teachers articulated different objectives and ways of using readings in their classrooms, goals and self-reported practices varied by experience level. Teachers struggled to formulate queries that are likely to return readings on specific course topics, instead searching directly for abstract topics. Experience differences did not translate into observable differences in search skill or success in the lab study. Originality/value There is limited work on teachers' information-seeking practices, particularly on how teachers look for texts for classroom use. This paper describes how teachers look for information in this context, setting the stage for future development and research on how to support this use case. Understanding and supporting teachers looking for information is a rich area for future research, due to the complexity of the information need and the fact that teachers are not looking for information for themselves.

Date

20. 1.2015 18:30:22

0.031149916 = product of:
  0.046724875 = sum of:
    0.029821085 = weight(_text_:on in 174) [ClassicSimilarity], result of:
      0.029821085 = score(doc=174,freq=10.0), product of:
        0.109763056 = queryWeight, product of:
          2.199415 = idf(docFreq=13325, maxDocs=44218)
          0.04990557 = queryNorm
        0.271686 = fieldWeight in 174, product of:
          3.1622777 = tf(freq=10.0), with freq of:
            10.0 = termFreq=10.0
          2.199415 = idf(docFreq=13325, maxDocs=44218)
          0.0390625 = fieldNorm(doc=174)
    0.01690379 = product of:
      0.03380758 = sum of:
        0.03380758 = weight(_text_:22 in 174) [ClassicSimilarity], result of:
          0.03380758 = score(doc=174,freq=2.0), product of:
            0.1747608 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.04990557 = queryNorm
            0.19345059 = fieldWeight in 174, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.0390625 = fieldNorm(doc=174)
      0.5 = coord(1/2)
  0.6666667 = coord(2/3)

Abstract

Drawing from social cognitive theory, the purpose of this study is to examine how personal, environmental and behavioral factors can interplay to influence people's use of YouTube as a learning resource. Design/methodology/approach This study proposed a conceptual model, which was then tested with data collected from a survey with 150 participants who had the experience of using YouTube for learning. The bootstrap method was employed to test the direct and mediation hypotheses in the model. Findings The results revealed that personal factors, i.e. learning outcome expectations and attitude, had direct effects on using YouTube as a learning resource (person ? behavior). The environmental factor, i.e. the sociability of YouTube, influenced the attitude (environment ? person), while the behavioral factor, i.e. prior experience of learning on YouTube, affected learning outcome expectations (behavior ? person). Moreover, the two personal factors fully mediated the influences of sociability and prior experience on YouTube usage for learning. Practical implications The factors and their relationships identified in this study provide important implications for individual learners, platform designers, educators and other stakeholders who encourage the use of YouTube as a learning resource. Originality/value This study draws on a comprehensive theoretical perspective (i.e. social cognitive theory) to investigate the interplay of critical components (i.e. individual, environment and behavior) in YouTube's learning ecosystem. Personal factors not only directly influenced the extent to which people use YouTube as a learning resource but also mediated the effects of environmental and behavioral factors on the usage behavior.

Date

20. 1.2015 18:30:22

0.031149916 = product of:
  0.046724875 = sum of:
    0.029821085 = weight(_text_:on in 882) [ClassicSimilarity], result of:
      0.029821085 = score(doc=882,freq=10.0), product of:
        0.109763056 = queryWeight, product of:
          2.199415 = idf(docFreq=13325, maxDocs=44218)
          0.04990557 = queryNorm
        0.271686 = fieldWeight in 882, product of:
          3.1622777 = tf(freq=10.0), with freq of:
            10.0 = termFreq=10.0
          2.199415 = idf(docFreq=13325, maxDocs=44218)
          0.0390625 = fieldNorm(doc=882)
    0.01690379 = product of:
      0.03380758 = sum of:
        0.03380758 = weight(_text_:22 in 882) [ClassicSimilarity], result of:
          0.03380758 = score(doc=882,freq=2.0), product of:
            0.1747608 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.04990557 = queryNorm
            0.19345059 = fieldWeight in 882, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.0390625 = fieldNorm(doc=882)
      0.5 = coord(1/2)
  0.6666667 = coord(2/3)

Abstract

Compared to previous studies that generally detect scientific breakthroughs based on citation patterns, this article proposes a knowledge entity-based disruption indicator by quantifying the change of knowledge directly created and inspired by scientific breakthroughs to their evolutionary trajectories. Two groups of analytic units, including MeSH terms and their co-occurrences, are employed independently by the indicator to measure the change of knowledge. The effectiveness of the proposed indicators was evaluated against the four datasets of scientific breakthroughs derived from four recognition trials. In terms of identifying scientific breakthroughs, the proposed disruption indicator based on MeSH co-occurrences outperforms that based on MeSH terms and three earlier citation-based disruption indicators. It is also shown that in our indicator, measuring the change of knowledge inspired by the focal paper in its evolutionary trajectory is a larger contributor than measuring the change created by the focal paper. Our study not only offers empirical insights into conceptual understanding of scientific breakthroughs but also provides practical disruption indicator for scientists and science management agencies searching for valuable research.

Date

22. 1.2023 18:37:33

0.02905105 = product of:
  0.043576576 = sum of:
    0.026672786 = weight(_text_:on in 263) [ClassicSimilarity], result of:
      0.026672786 = score(doc=263,freq=8.0), product of:
        0.109763056 = queryWeight, product of:
          2.199415 = idf(docFreq=13325, maxDocs=44218)
          0.04990557 = queryNorm
        0.24300331 = fieldWeight in 263, product of:
          2.828427 = tf(freq=8.0), with freq of:
            8.0 = termFreq=8.0
          2.199415 = idf(docFreq=13325, maxDocs=44218)
          0.0390625 = fieldNorm(doc=263)
    0.01690379 = product of:
      0.03380758 = sum of:
        0.03380758 = weight(_text_:22 in 263) [ClassicSimilarity], result of:
          0.03380758 = score(doc=263,freq=2.0), product of:
            0.1747608 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.04990557 = queryNorm
            0.19345059 = fieldWeight in 263, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.0390625 = fieldNorm(doc=263)
      0.5 = coord(1/2)
  0.6666667 = coord(2/3)

Abstract

When people experience major life changes, this often impacts their self-presentation, networks, and online behavior in substantial ways. To effectively study major life transitions and events, we surveyed a large U.S. sample (n = 554) to create the Major Life Events Taxonomy, a list of 121 life events in 12 categories. We then applied this taxonomy to a second large U.S. survey sample (n = 775) to understand on average how much social readjustment each event required, how likely each event was to be shared on social media with different types of audiences, and how much online network separation each involved. We found that social readjustment is positively correlated with sharing on social media, with both broad audiences and close ties as well as in online spaces separate from one's network of known ties. Some life transitions involve high levels of sharing with both separate audiences and broad audiences on social media, providing evidence for what previous research has called social media as social transition machinery. Researchers can use the Major Life Events Taxonomy to examine how people's life transition experiences relate to their behaviors, technology use, and health and well-being outcomes.

Date

10. 6.2021 19:22:47

0.02905105 = product of:
  0.043576576 = sum of:
    0.026672786 = weight(_text_:on in 563) [ClassicSimilarity], result of:
      0.026672786 = score(doc=563,freq=8.0), product of:
        0.109763056 = queryWeight, product of:
          2.199415 = idf(docFreq=13325, maxDocs=44218)
          0.04990557 = queryNorm
        0.24300331 = fieldWeight in 563, product of:
          2.828427 = tf(freq=8.0), with freq of:
            8.0 = termFreq=8.0
          2.199415 = idf(docFreq=13325, maxDocs=44218)
          0.0390625 = fieldNorm(doc=563)
    0.01690379 = product of:
      0.03380758 = sum of:
        0.03380758 = weight(_text_:22 in 563) [ClassicSimilarity], result of:
          0.03380758 = score(doc=563,freq=2.0), product of:
            0.1747608 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.04990557 = queryNorm
            0.19345059 = fieldWeight in 563, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.0390625 = fieldNorm(doc=563)
      0.5 = coord(1/2)
  0.6666667 = coord(2/3)

Abstract

While it is clear that using a mobile device can interrupt real-world activities such as walking or driving, the effects of interruptions on mobile device use have been under-studied. We are particularly interested in how the ambient distraction of walking while using a mobile device, combined with the occurrence of simulated interruptions of different levels of cognitive complexity, affect web search activities. We have established an experimental design to study how the degree of cognitive complexity of simulated interruptions influences both objective and subjective search task performance. In a controlled laboratory study (n = 27), quantitative and qualitative data were collected on mobile search performance, perceptions of the interruptions, and how participants reacted to the interruptions, using a custom mobile eye-tracking app, a questionnaire, and observations. As expected, more cognitively complex interruptions resulted in increased overall task completion times and higher perceived impacts. Interestingly, the effect on the resumption lag or the actual search performance was not significant, showing the resiliency of people to resume their tasks after an interruption. Implications from this study enhance our understanding of how interruptions objectively and subjectively affect search task performance, motivating the need for providing explicit mobile search support to enable recovery from interruptions.

Date

3. 5.2022 13:22:33

0.02905105 = product of:
  0.043576576 = sum of:
    0.026672786 = weight(_text_:on in 890) [ClassicSimilarity], result of:
      0.026672786 = score(doc=890,freq=8.0), product of:
        0.109763056 = queryWeight, product of:
          2.199415 = idf(docFreq=13325, maxDocs=44218)
          0.04990557 = queryNorm
        0.24300331 = fieldWeight in 890, product of:
          2.828427 = tf(freq=8.0), with freq of:
            8.0 = termFreq=8.0
          2.199415 = idf(docFreq=13325, maxDocs=44218)
          0.0390625 = fieldNorm(doc=890)
    0.01690379 = product of:
      0.03380758 = sum of:
        0.03380758 = weight(_text_:22 in 890) [ClassicSimilarity], result of:
          0.03380758 = score(doc=890,freq=2.0), product of:
            0.1747608 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.04990557 = queryNorm
            0.19345059 = fieldWeight in 890, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.0390625 = fieldNorm(doc=890)
      0.5 = coord(1/2)
  0.6666667 = coord(2/3)

Abstract

Knowledge-enhanced short-text matching has been a significant task attracting much attention in recent years. However, the existing approaches cannot effectively balance effect and efficiency. Effective models usually consist of complex network structures leading to slow inference speed and the difficulties of applications in actual practice. In addition, most knowledge-enhanced models try to link the mentions in the text to the entities of the knowledge graphs-the difficulties of entity linking decrease the generalizability among different datasets. To address these problems, we propose a lightweight Semantic-Enhanced Interactive Network (SEIN) model for efficient short-text matching. Unlike most current research, SEIN employs an unsupervised method to select WordNet's most appropriate paraphrase description as the external semantic knowledge. It focuses on integrating semantic information and interactive information of text while simplifying the structure of other modules. We conduct intensive experiments on four real-world datasets, that is, Quora, Twitter-URL, SciTail, and SICK-E. Compared with state-of-the-art methods, SEIN achieves the best performance on most datasets. The experimental results proved that introducing external knowledge could effectively improve the performance of the short-text matching models. The research sheds light on the role of lightweight models in leveraging external knowledge to improve the effect of short-text matching.

Date

22. 1.2023 19:05:27

0.02905105 = product of:
  0.043576576 = sum of:
    0.026672786 = weight(_text_:on in 996) [ClassicSimilarity], result of:
      0.026672786 = score(doc=996,freq=8.0), product of:
        0.109763056 = queryWeight, product of:
          2.199415 = idf(docFreq=13325, maxDocs=44218)
          0.04990557 = queryNorm
        0.24300331 = fieldWeight in 996, product of:
          2.828427 = tf(freq=8.0), with freq of:
            8.0 = termFreq=8.0
          2.199415 = idf(docFreq=13325, maxDocs=44218)
          0.0390625 = fieldNorm(doc=996)
    0.01690379 = product of:
      0.03380758 = sum of:
        0.03380758 = weight(_text_:22 in 996) [ClassicSimilarity], result of:
          0.03380758 = score(doc=996,freq=2.0), product of:
            0.1747608 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.04990557 = queryNorm
            0.19345059 = fieldWeight in 996, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.0390625 = fieldNorm(doc=996)
      0.5 = coord(1/2)
  0.6666667 = coord(2/3)

Abstract

The COVID-19 crisis provided an opportunity for information professionals to rethink the role of information in individuals' decision making such as vaccine uptake. Unlike previous studies, which often considered information as a single factor among others, this study examined the impact of the quantity and trustworthiness of information on people's adoption of information for vaccination decisions based on the information adoption model. We analyzed COVID-19 Preventive Behavior Survey data collected by the Massachusetts Institute of Technology from Facebook users (N = 82,213) in 15 countries between October 2020 and March 2021. The results of logistic regression analyses indicate that reasonable quantity and trustworthiness of information were positively related to COVID-19 vaccination intent. But excessive and less than the desired amount of information was more likely to have negative impacts on vaccination intent. The degrees of trust in the mediums and in the sources were associated with the level of vaccine acceptance. But the effects of trustworthiness accorded to information sources showed variations across sources and mediums. Implications for information professionals and suggestions for policies are discussed.

Date

22. 6.2023 18:20:47

0.02905105 = product of:
  0.043576576 = sum of:
    0.026672786 = weight(_text_:on in 1054) [ClassicSimilarity], result of:
      0.026672786 = score(doc=1054,freq=8.0), product of:
        0.109763056 = queryWeight, product of:
          2.199415 = idf(docFreq=13325, maxDocs=44218)
          0.04990557 = queryNorm
        0.24300331 = fieldWeight in 1054, product of:
          2.828427 = tf(freq=8.0), with freq of:
            8.0 = termFreq=8.0
          2.199415 = idf(docFreq=13325, maxDocs=44218)
          0.0390625 = fieldNorm(doc=1054)
    0.01690379 = product of:
      0.03380758 = sum of:
        0.03380758 = weight(_text_:22 in 1054) [ClassicSimilarity], result of:
          0.03380758 = score(doc=1054,freq=2.0), product of:
            0.1747608 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.04990557 = queryNorm
            0.19345059 = fieldWeight in 1054, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.0390625 = fieldNorm(doc=1054)
      0.5 = coord(1/2)
  0.6666667 = coord(2/3)

Abstract

To effectively manage a crisis, most decisions made by governments, organizations, communities, and individuals are based on "shared situational awareness" (SSA) derived from multiple information sources. Developing SSA depends on the alignment of mental models, which "represent our shared version of truth and reality on which we can act." Social media has facilitated public sensemaking during a crisis; however, it has also encouraged mental model dissonance, resulting in the digital destruction of mental models and undermining adequate SSA. The study is concerned with the challenges of creating SSA during the COVID-19 pandemic in Australia. This paper documents a netnography of Australian public health agencies' Facebook communication, exploring the initial impact of COVID-19 on SSA creation. Chaos theory is used as a theoretical lens to examine information perception, meaning, and assumptions relating to SSA from pre to post-pandemic periods. Our study highlights how the initial COVID-19 "butterfly effect" swamped the public health communication channel, leaving little space for other important health issues. This research contributes to information systems, information science, and communications by illustrating how the emergence of a crisis impacts social media communication, the creation of SSA, and what this means for social media adoption for crisis communication purposes.

Date

22. 9.2023 16:02:26

0.028611436 = product of:
  0.042917155 = sum of:
    0.02263261 = weight(_text_:on in 5996) [ClassicSimilarity], result of:
      0.02263261 = score(doc=5996,freq=4.0), product of:
        0.109763056 = queryWeight, product of:
          2.199415 = idf(docFreq=13325, maxDocs=44218)
          0.04990557 = queryNorm
        0.20619515 = fieldWeight in 5996, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          2.199415 = idf(docFreq=13325, maxDocs=44218)
          0.046875 = fieldNorm(doc=5996)
    0.020284547 = product of:
      0.040569093 = sum of:
        0.040569093 = weight(_text_:22 in 5996) [ClassicSimilarity], result of:
          0.040569093 = score(doc=5996,freq=2.0), product of:
            0.1747608 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.04990557 = queryNorm
            0.23214069 = fieldWeight in 5996, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.046875 = fieldNorm(doc=5996)
      0.5 = coord(1/2)
  0.6666667 = coord(2/3)

Abstract

This paper contains a report of two interdependent knowledge organization (KO) projects for an LGBT2QIA+ library. The authors, in the context of volunteer library work for an independent library, redesigned the classification system and subject cataloguing guidelines to centre LGBT2QIA+ subjects. We discuss the priorities of creating and maintaining knowledge organization systems for a historically marginalized community and address the challenge that queer subjectivity poses to the goals of KO. The classification system features a focus on identity and physically reorganizes the library space in a way that accounts for the multiple and overlapping labels that constitute the currently articulated boundaries of this community. The subject heading system focuses on making visible topics and elements of identity made invisible by universal systems and by the newly implemented classification system. We discuss how this project may inform KO for other marginalized subjects, particularly through process and documentation that prioritizes transparency and the acceptance of an unfinished endpoint for queer KO.

Date

6.10.2020 21:22:33

0.028611436 = product of:
  0.042917155 = sum of:
    0.02263261 = weight(_text_:on in 182) [ClassicSimilarity], result of:
      0.02263261 = score(doc=182,freq=4.0), product of:
        0.109763056 = queryWeight, product of:
          2.199415 = idf(docFreq=13325, maxDocs=44218)
          0.04990557 = queryNorm
        0.20619515 = fieldWeight in 182, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          2.199415 = idf(docFreq=13325, maxDocs=44218)
          0.046875 = fieldNorm(doc=182)
    0.020284547 = product of:
      0.040569093 = sum of:
        0.040569093 = weight(_text_:22 in 182) [ClassicSimilarity], result of:
          0.040569093 = score(doc=182,freq=2.0), product of:
            0.1747608 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.04990557 = queryNorm
            0.23214069 = fieldWeight in 182, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.046875 = fieldNorm(doc=182)
      0.5 = coord(1/2)
  0.6666667 = coord(2/3)

Abstract

Purpose The purpose of the paper is to analyse the interactions of bridging users in Twitter discussions about vaccination. Design/methodology/approach Conversational threads were collected through filtering the Twitter stream using keywords and the most active participants in the conversations. Following data collection and anonymisation of tweets and user profiles, a retweet network was created to find users bridging the main clusters. Four conversations were selected, ranging from 456 to 1,983 tweets long, and then analysed through content analysis. Findings Although different opinions met in the discussions, a consensus was rarely built. Many sub-threads involved insults and criticism, and participants seemed not interested in shifting their positions. However, examples of reasoned discussions were also found. Originality/value The study analyses conversations on Twitter, which is rarely studied. The focus on the interactions of bridging users adds to the uniqueness of the paper.

Date

20. 1.2015 18:30:22

0.028611436 = product of:
  0.042917155 = sum of:
    0.02263261 = weight(_text_:on in 184) [ClassicSimilarity], result of:
      0.02263261 = score(doc=184,freq=4.0), product of:
        0.109763056 = queryWeight, product of:
          2.199415 = idf(docFreq=13325, maxDocs=44218)
          0.04990557 = queryNorm
        0.20619515 = fieldWeight in 184, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          2.199415 = idf(docFreq=13325, maxDocs=44218)
          0.046875 = fieldNorm(doc=184)
    0.020284547 = product of:
      0.040569093 = sum of:
        0.040569093 = weight(_text_:22 in 184) [ClassicSimilarity], result of:
          0.040569093 = score(doc=184,freq=2.0), product of:
            0.1747608 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.04990557 = queryNorm
            0.23214069 = fieldWeight in 184, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.046875 = fieldNorm(doc=184)
      0.5 = coord(1/2)
  0.6666667 = coord(2/3)

Abstract

Purpose The purpose of the paper is to analyse the interactions of bridging users in Twitter discussions about vaccination. Design/methodology/approach Conversational threads were collected through filtering the Twitter stream using keywords and the most active participants in the conversations. Following data collection and anonymisation of tweets and user profiles, a retweet network was created to find users bridging the main clusters. Four conversations were selected, ranging from 456 to 1,983 tweets long, and then analysed through content analysis. Findings Although different opinions met in the discussions, a consensus was rarely built. Many sub-threads involved insults and criticism, and participants seemed not interested in shifting their positions. However, examples of reasoned discussions were also found. Originality/value The study analyses conversations on Twitter, which is rarely studied. The focus on the interactions of bridging users adds to the uniqueness of the paper.

Date

20. 1.2015 18:30:22

0.028611436 = product of:
  0.042917155 = sum of:
    0.02263261 = weight(_text_:on in 941) [ClassicSimilarity], result of:
      0.02263261 = score(doc=941,freq=4.0), product of:
        0.109763056 = queryWeight, product of:
          2.199415 = idf(docFreq=13325, maxDocs=44218)
          0.04990557 = queryNorm
        0.20619515 = fieldWeight in 941, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          2.199415 = idf(docFreq=13325, maxDocs=44218)
          0.046875 = fieldNorm(doc=941)
    0.020284547 = product of:
      0.040569093 = sum of:
        0.040569093 = weight(_text_:22 in 941) [ClassicSimilarity], result of:
          0.040569093 = score(doc=941,freq=2.0), product of:
            0.1747608 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.04990557 = queryNorm
            0.23214069 = fieldWeight in 941, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.046875 = fieldNorm(doc=941)
      0.5 = coord(1/2)
  0.6666667 = coord(2/3)

Abstract

Inferring the gender of named entities present in a text has several practical applications in information sciences. Existing approaches toward name gender identification rely exclusively on using the gender distributions from labeled data. In the absence of such labeled data, these methods fail. In this article, we propose a two-stage model that is able to infer the gender of names present in text without requiring explicit name-gender labels. We use coreference resolution as the backbone for our proposed model. To aid coreference resolution where the existing contextual information does not suffice, we use a retrieval-assisted context aggregation framework. We demonstrate that state-of-the-art name gender inference is possible without supervision. Our proposed method matches or outperforms several supervised approaches and commercially used methods on five English language datasets from different domains.

Date

22. 3.2023 12:00:14

0.028611436 = product of:
  0.042917155 = sum of:
    0.02263261 = weight(_text_:on in 1067) [ClassicSimilarity], result of:
      0.02263261 = score(doc=1067,freq=4.0), product of:
        0.109763056 = queryWeight, product of:
          2.199415 = idf(docFreq=13325, maxDocs=44218)
          0.04990557 = queryNorm
        0.20619515 = fieldWeight in 1067, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          2.199415 = idf(docFreq=13325, maxDocs=44218)
          0.046875 = fieldNorm(doc=1067)
    0.020284547 = product of:
      0.040569093 = sum of:
        0.040569093 = weight(_text_:22 in 1067) [ClassicSimilarity], result of:
          0.040569093 = score(doc=1067,freq=2.0), product of:
            0.1747608 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.04990557 = queryNorm
            0.23214069 = fieldWeight in 1067, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.046875 = fieldNorm(doc=1067)
      0.5 = coord(1/2)
  0.6666667 = coord(2/3)

Abstract

Social question-and-answer (Q&A) communities are becoming increasingly important for knowledge acquisition. However, some users withhold knowledge, which can hinder the effectiveness of these platforms. Based on social exchange theory, the study investigates how different types of questions influence knowledge withholding, with question difficulty and user anonymity as boundary conditions. Two experiments were conducted to test hypotheses. Results indicate that informational questions are more likely to lead to knowledge withholding than conversational ones, as they elicit more fear of negative evaluation and fear of exploitation. The study also examines the interplay of question difficulty and user anonymity with question type. Overall, this study significantly extends the existing literature on counterproductive knowledge behavior by exploring the antecedents of knowledge withholding in social Q&A communities.

Date

22. 9.2023 13:51:47