Search (18 results, page 1 of 1)

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Abstract

In the age of mobile commerce, users receive floods of commercial messages. How do users judge the relevance of such information? Is their relevance judgment affected by contextual factors, such as location and time? How do message content and contextual factors affect users' privacy concerns? With a focus on mobile ads, we propose a research model based on theories of relevance judgment and mobile marketing research. We suggest topicality, reliability, and economic value as key content factors and location and time as key contextual factors. We found mobile relevance judgment is affected mainly by content factors, whereas privacy concerns are affected by both content and contextual factors. Moreover, topicality and economic value have a synergetic effect that makes a message more relevant. Higher topicality and location precision exacerbate privacy concerns, whereas message reliability alleviates privacy concerns caused by location precision. These findings reveal an interesting intricacy in user relevance judgment and privacy concerns and provide nuanced guidance for the design and delivery of mobile commercial information.

Source

Journal of the Association for Information Science and Technology. 65(2014) no.7, S.1335-1348

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Abstract

With increasing digital information availability, semantic web technologies have been employed to construct semantic digital libraries in order to ease information comprehension. The use of semantic web enables users to search or visualize resources in a semantic fashion. Semantic web generation is a key process in semantic digital library construction, which converts metadata of digital resources into semantic web data. Many text mining technologies, such as keyword extraction and clustering, have been proposed to generate semantic web data. However, one important type of metadata in publications, called affiliation, is hard to convert into semantic web data precisely because different authors, who have the same affiliation, often express the affiliation in different ways. To address this issue, this paper proposes a clustering method based on normalized compression distance for the purpose of affiliation disambiguation. The experimental results show that our method is able to identify different affiliations that denote the same institutes. The clustering results outperform the well-known k-means clustering method in terms of average precision, F-measure, entropy, and purity.

Source

Journal of the American Society for Information Science and Technology. 62(2011) no.6, S.1029-1041

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Abstract

The spread of misinformation on social media has become a major societal issue during recent years. In this work, we used the ongoing COVID-19 pandemic as a case study to systematically investigate factors associated with the spread of multi-topic misinformation related to one event on social media based on the heuristic-systematic model. Among factors related to systematic processing of information, we discovered that the topics of a misinformation story matter, with conspiracy theories being the most likely to be retweeted. As for factors related to heuristic processing of information, such as when citizens look up to their leaders during such a crisis, our results demonstrated that behaviors of a political leader, former US President Donald J. Trump, may have nudged people's sharing of COVID-19 misinformation. Outcomes of this study help social media platform and users better understand and prevent the spread of misinformation on social media.

Source

Journal of the Association for Information Science and Technology. 73(2022) no.5, S.726-737

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Abstract

Data reuse has recently become significant in academia and is providing new impetus for academic research. This prompts two questions: What precisely is the data reuse process? What is the connection between each participating element? To address these issues, 42 studies were reviewed to identify the stages and primary data reuse elements. A meta-synthesis was used to locate and analyze the studies, and inductive coding was used to organize the analytical process. We identified three stages of data reuse-initiation, exploration and collection, and repurposing-and explored how they interact and form iterative characteristics. The results illuminated the data reuse at each stage, including issues of data trust, data sources, scaffolds, and barriers. The results indicated that multisource data and human scaffolds promote reuse behavior effectively. Further, two data and information search patterns were extracted: reticular centripetal patterns and decentralized centripetal patterns. Three paths with elements cooperating through flexible functions and motivated by different action items were identified: data centers, human scaffolds, and publications. This study supports improvements for data infrastructure construction, data reuse, and data reuse research by providing a new perspective on the effect of information behavior and clarifying the stages and contextual relationships between various elements.

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Journal of the Association for Information Science and Technology. 72(2021) no.9, S.1161-1182

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Abstract

Recent studies have found organizational learning capacity to be a key factor in influencing organizational assimilation and exploitation of knowledge-intensive innovations. Despite its increasing importance, the impact of organizational learning capacity an technology assimilation is not well understood. Distilling from extant works an organizational learning and technology assimilation, this study identifies four components of organizational learning capacity, namely, systems orientation, organizational climate for learning orientation, knowledge acquisition and utilization orientation, and information sharing and dissemination orientation. The authors subject these components to structural equation modeling analyses to better understand their structure and dimensionality. The analyses strongly support the proposed four major dimensions underlying organizational learning capacity. Organizational learning capacity, as a higher-order factor, has a significant impact an attitude towards organizational adoption of knowledge-intensive innovations. Implications for practice and research are discussed.

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Journal of the American Society for Information Science and Technology. 57(2006) no.2, S.264-279

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Abstract

The research provides (1) an account of the construction of a new Scientific Argument Ontology (SAO), (2) a statistical analysis of 40 articles from both fields of Library and Information Science and Biomedical Research, and (3) a summary of important differences between the article structures common to each respective field of study and characteristics of their contents as revealed by applying SAO to conduct qualitative analysis. Ontology coverage ratios as well as the ratios of different classes and evidence types were calculated in the analysis. The results show a comprehensive coverage of SAO, while also indicate that the ontological construction of scientific arguments should fully consider the characteristics of their disciplines and fields in order to better facilitate extraction, discovery and retrieval.

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Proceedings of the Association for Information Science and Technology 56(2019) no.1, S.855-857

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Abstract

An efficient and robust medical-image indexing procedure should be user-oriented. It is essential to index the images at the right level of description and ensure that the indexed levels match the user's interest level. This study examines 240 medical-image descriptions produced by three different groups of medical-image users (novices, intermediates, and experts) in the area of radiography. This article reports several important findings: First, the effect of domain knowledge has a significant relationship with the use of semantic image attributes in image-users' descriptions. We found that experts employ more high-level image attributes which require high-reasoning or diagnostic knowledge to search for a medical image (Abstract Objects and Scenes) than do novices; novices are more likely to describe some basic objects which do not require much radiological knowledge to search for an image they need (Generic Objects) than are experts. Second, all image users in this study prefer to use image attributes of the semantic levels to represent the image that they desired to find, especially using those specific-level and scene-related attributes. Third, image attributes generated by medical-image users can be mapped to all levels of the pyramid model that was developed to structure visual information. Therefore, the pyramid model could be considered a robust instrument for indexing medical imagery.

Source

Journal of the American Society for Information Science and Technology. 63(2012) no.2, S.225-241

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Abstract

One way to facilitate Multilingual Information Access (MLIA) for digital libraries is to generate multilingual metadata records by applying Machine Translation (MT) techniques. Current online MT services are available and affordable, but are not always effective for creating multilingual metadata records. In this study, we implemented 3 different MT strategies and evaluated their performance when translating English metadata records to Chinese and Spanish. These strategies included combining MT results from 3 online MT systems (Google, Bing, and Yahoo!) with and without additional linguistic resources, such as manually-generated parallel corpora, and metadata records in the two target languages obtained from international partners. The open-source statistical MT platform Moses was applied to design and implement the three translation strategies. Human evaluation of the MT results using adequacy and fluency demonstrated that two of the strategies produced higher quality translations than individual online MT systems for both languages. Especially, adding small, manually-generated parallel corpora of metadata records significantly improved translation performance. Our study suggested an effective and efficient MT approach for providing multilingual services for digital collections.

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Journal of the Association for Information Science and Technology. 69(2018) no.1, S.47-59

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Abstract

To provide some context for the potential engagement behavior of Twitter users around science, this article investigates how Bitly short links to scientific publications embedded in scholarly Twitter mentions are clicked on Twitter. Based on the click metrics of over 1.1 million Bitly short links referring to Web of Science (WoS) publications, our results show that around 49.5% of them were not clicked by Twitter users. For those Bitly short links with clicks from Twitter, the majority of their Twitter clicks accumulated within a short period of time after they were first tweeted. Bitly short links to the publications in the field of Social Sciences and Humanities tend to attract more clicks from Twitter over other subject fields. This article also assesses the extent to which Twitter clicks are correlated with some other impact indicators. Twitter clicks are weakly correlated with scholarly impact indicators (WoS citations and Mendeley readers), but moderately correlated to other Twitter engagement indicators (total retweets and total likes). In light of these results, we highlight the importance of paying more attention to the click metrics of URLs in scholarly Twitter mentions, to improve our understanding about the more effective dissemination and reception of science information on Twitter.

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Journal of the Association for Information Science and Technology. 72(2021) no.7, S.918-932

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Abstract

Scientific papers are essential manifestations of evolving scientific knowledge, and arguments are an important avenue to communicate research results. This study aims to understand how the argumentation process is represented in scientific papers, which is important for knowledge representation, discovery, and retrieval. First, based on fundamental argument theory and scientific discourse ontologies, a coding schema, including 17 categories was constructed. Thereafter, annotation experiments were conducted with 40 scientific articles randomly selected from two different research areas (library and information science and biomedical sciences). Statistical analysis and the sequential pattern mining method were then employed; the ratio of different argumentation units and evidence types were calculated, the argumentation semantics of figures and tables analyzed, and the argumentation structures extracted. A correlation analysis between argumentation and rhetorical structures was also performed to further reveal how argumentation was represented within scientific discourses. The results indicated a difference in the proportion of the argumentation units in the two types of scientific papers, as well as a similar linear construction with differences in the specific argument structures of each knowledge domain and a clear correlation between argumentation and rhetorical structure.

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Journal of the Association for Information Science and Technology. 73(2022) no.6, S.863-878

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Journal of the Association for Information Science and Technology. 65(2014) no.9, S.1820-1833

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Journal of the Association for Information Science and Technology. 72(2021) no.6, S.710-722

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Abstract

Digital images of cultural heritage (CH) contain rich semantic information. However, today's semantic representations of CH images fail to fully reveal the content entities and context within these vital surrogates. This paper draws on the fields of image research and digital humanities to propose a systematic methodology and a technical route for semantic enrichment of CH digital images. This new methodology systematically applies a series of procedures including: semantic annotation, entity-based enrichment, establishing internal relations, event-centric enrichment, defining hierarchy relations between properties text annotation, and finally, named entity recognition in order to ultimately provide fine-grained contextual semantic content disclosure. The feasibility and advantages of the proposed semantic enrichment methods for semantic representation are demonstrated via a visual display platform for digital images of CH built to represent the Wutai Mountain Map, a typical Dunhuang mural. This study proves that semantic enrichment offers a promising new model for exposing content at a fine-grained level, and establishing a rich semantic network centered on the content of digital images of CH.

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Journal of the Association for Information Science and Technology. 69(2018) no.9, S.1122-1133

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Journal of the Association for Information Science and Technology. 73(2022) no.2, S.188-203

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Journal of the Association for Information Science and Technology. 74(2023) no.1, S.115-127

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Journal of the Association for Information Science and Technology. 74(2023) no.3, S.339-353

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Journal of the Association for Information Science and Technology. 75(2023) no.1, S.3-23