以關鍵字使用分析探討社會標記者與索引專家的文獻標引心智模式
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2013
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隨著Web 2.0的廣泛應用，各式社會網路平台提供了社會標記的功能，讓社會標記者以自由形式的關鍵字組織各式資訊。在圖書資訊學界方面，資訊組織的權威控制與索引典控制係以一種控制詞彙的方式標引資訊的內容屬性。無論是社會標記者或是圖書資訊學界的索引專家皆將關鍵字視為一種觀念，且依其先備經驗與知識，經由關鍵字以表徵其對資訊內容所認知的理解與心智模式。現有的研究皆著重在以個別的關鍵字為研究對象，包括關鍵字的來源與使用情形，以及社會標記與控制詞彙間關鍵字的重複情形等，並未具體提出完整的文獻標引心智模式地圖，而是零散、片斷且沒有任何關聯關係的文獻標引心智模式。如果可以更瞭解社會標記者與索引專家的心智模式及其差異，即可選用更適當的關鍵字組織各項資訊資源，進而促成資源的發掘，導引使用者找到所需的資訊。
本研究旨在以關鍵字的使用分析，探討社會標記者與索引專家的文獻標引心智模式。在樣本資料方面，本研究選取13種圖資期刊中1,489篇文獻的社會標記與控制詞彙等關鍵字為樣本，包括CiteULike的3,972個社會標記（1,672個不重複標記）與LISA的6,708個控制詞彙（1,338個控制詞彙）。在研究方法則是計算關鍵字的使用情形外，還包括社會網路分析與頻繁樣式成長法（含頻繁樣式樹）等方法討論隱藏在關鍵字間的關聯關係結構與樣式。從關鍵字的使用情形、冪次定律分佈、社會標記與控制詞彙間的關鍵字比對、社會網路分析（包括：中心度、階層集叢、同等角色）及頻繁樣式成長等方面而言，結果顯示社會標記者與索引專家間的文獻標引心智模相似度並不高，主要的研究結果如下：
1.社會標記者的文獻標引心智模式比索引專家更為多元化。
2.社會標記者直接從文獻題名中選用關鍵字的傾向高於索引專家。
3.社會標記與控制詞彙間的相同程度不高且彼此互補。
4.社會標記者不經常單獨使用內容群組的關鍵字類別及其所擁有的關鍵字，卻經常與題名主題群組的關鍵字類別及其所擁有的關鍵字一起搭配使用。索引專家雖然不常單獨使用其他群組的關鍵字類別及其所擁有的關鍵字作為文獻標引之用，卻常與題名、主題與內容等群組及其所擁有的關鍵字一起搭配使用。
5.社會標記者傾向交替使用較多組的關鍵字類別及其擁有的關鍵字，而且每組幾乎是由兩種不同的關鍵字類別所組成。然而，索引專家則是傾向交替較少組的關鍵字類別及其擁有的關鍵字，且每組幾乎是由兩種以上不同的關鍵字類別所組成（即2、3與7種）。
6.社會標記者傾向使用較少的FP-tree規則且較少的關鍵字類別進行文獻標引，而索引專家則是傾向使用較多的FP-tree規則且較多的關鍵字類別，組織各式資訊資源。
基於前述的研究結果，本研究貢獻主要有五項：發展文獻標引的心智模式、分析文獻標引心智模式地圖及其結構與樣式、分析關鍵字的使用情形及其共同出現的關聯關係特質、從心智模式解釋社會標記與控制詞彙兩者互補現象的原因，及擴展標記類別模式（tag category model）的可行性驗證與應用解釋。研究結果亦可進一步應用在資訊系統的設計，包括關鍵字的推薦、使用者界面的設計及瀏覽分類架構的建立與運用。
With the wide application of Web 2.0, various social networking platforms allow taggers to use uncontrolled, free keywords (i.e., social tags) to organize information. In library and information science, professional indexers are guided by the principles of authority control and thesaurus control to organize information with controlled vocabularies. Both social taggers and professional indexers regard keywords as concepts that represent their cognitions and mental models of information content, according to their prior experience and knowledge. Existing studies have focused on examining the sources and usage of individual keywords, and comparing the similarity between tags and controlled vocabularies. However, the results of such studies only reflect scattered debris rather than a whole picture of the mental models used by social taggers and professional indexers for article indexing. A better understanding of the mental models of taggers and professional indexers and their usage gap may inspire better selection of appropriate keywords for organizing information, facilitating resource discovery, and guiding users to find the right information. This study explores the mental models used by taggers and professional indexers to designate keywords for article indexing. Using a dataset of 3,972 CiteULike tags and 6,708 Library and Information Science Abstracts (LISA) descriptors from 1,489 scholarly articles in 13 library and information science journals, this study attempts to analyze the keyword usage of taggers and professional indexers to capture and build up their mental models for article indexing, and generalize their structures and patterns. To achieve this end, in this study social network analysis and frequent-pattern growth methods were employed. When measured with respect to terms used, power law distribution, a comparison of terms used as tags and descriptors, social network analysis (including centrality, overall structure and role equivalence) and frequent-pattern growth analysis (including frequent-pattern tree), little similarity was found between the mental models of taggers and professional indexers in article indexing. The results of this study are summarized as follows: Taggers’ mental models for article indexing are more diverse than those of professional indexers. Social taggers have a higher preference than professional indexers to select terms for article indexing from title keywords. There is little similarity between social tags and controlled vocabularies and they complement each other. Keywords in content-related categories were not used independently by social taggers, but they were often used with those from topic-related categories. On the other hand, keywords of other-related categories were often co-used with those of title-, topic- or content-related categories by professional indexers. Social taggers may prefer to assign co-occurring keywords with more sets of fewer facets’ viewpoints (almost always two-facets); however, professional indexers may be inclined to offer keywords with fewer sets of more facets’ viewpoints (i.e., two-, three- and seven-facets). Social taggers may be inclined to assign keywords with fewer path-based rules comprising fewer keyword categories. Professional indexers may tend to offer keywords with more path-based rules comprising more keyword categories. According to the research results mentioned above, the key contributions of this study are as follows: Development of a generic model of mental models of social taggers and professional indexers for article indexing. Analysis of the structures and patterns embedded in maps of mental models of social taggers and professional indexers in article indexing. Analysis of the characteristics of keyword usage and co-occurring keywords’ associations. Presentation of a theoretical basis to explain the reason why social tags complement controlled vocabularies. Extension of the tag category model by feasibility examination and explanation. Furthermore, the results of this study also inform the design of information systems, including term recommendations and user interfaces for indexing, as well as frequent-pattern based classification trees for browsing and navigation.
With the wide application of Web 2.0, various social networking platforms allow taggers to use uncontrolled, free keywords (i.e., social tags) to organize information. In library and information science, professional indexers are guided by the principles of authority control and thesaurus control to organize information with controlled vocabularies. Both social taggers and professional indexers regard keywords as concepts that represent their cognitions and mental models of information content, according to their prior experience and knowledge. Existing studies have focused on examining the sources and usage of individual keywords, and comparing the similarity between tags and controlled vocabularies. However, the results of such studies only reflect scattered debris rather than a whole picture of the mental models used by social taggers and professional indexers for article indexing. A better understanding of the mental models of taggers and professional indexers and their usage gap may inspire better selection of appropriate keywords for organizing information, facilitating resource discovery, and guiding users to find the right information. This study explores the mental models used by taggers and professional indexers to designate keywords for article indexing. Using a dataset of 3,972 CiteULike tags and 6,708 Library and Information Science Abstracts (LISA) descriptors from 1,489 scholarly articles in 13 library and information science journals, this study attempts to analyze the keyword usage of taggers and professional indexers to capture and build up their mental models for article indexing, and generalize their structures and patterns. To achieve this end, in this study social network analysis and frequent-pattern growth methods were employed. When measured with respect to terms used, power law distribution, a comparison of terms used as tags and descriptors, social network analysis (including centrality, overall structure and role equivalence) and frequent-pattern growth analysis (including frequent-pattern tree), little similarity was found between the mental models of taggers and professional indexers in article indexing. The results of this study are summarized as follows: Taggers’ mental models for article indexing are more diverse than those of professional indexers. Social taggers have a higher preference than professional indexers to select terms for article indexing from title keywords. There is little similarity between social tags and controlled vocabularies and they complement each other. Keywords in content-related categories were not used independently by social taggers, but they were often used with those from topic-related categories. On the other hand, keywords of other-related categories were often co-used with those of title-, topic- or content-related categories by professional indexers. Social taggers may prefer to assign co-occurring keywords with more sets of fewer facets’ viewpoints (almost always two-facets); however, professional indexers may be inclined to offer keywords with fewer sets of more facets’ viewpoints (i.e., two-, three- and seven-facets). Social taggers may be inclined to assign keywords with fewer path-based rules comprising fewer keyword categories. Professional indexers may tend to offer keywords with more path-based rules comprising more keyword categories. According to the research results mentioned above, the key contributions of this study are as follows: Development of a generic model of mental models of social taggers and professional indexers for article indexing. Analysis of the structures and patterns embedded in maps of mental models of social taggers and professional indexers in article indexing. Analysis of the characteristics of keyword usage and co-occurring keywords’ associations. Presentation of a theoretical basis to explain the reason why social tags complement controlled vocabularies. Extension of the tag category model by feasibility examination and explanation. Furthermore, the results of this study also inform the design of information systems, including term recommendations and user interfaces for indexing, as well as frequent-pattern based classification trees for browsing and navigation.
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社會標記, 控制詞彙, 標引, 心智模式, 社會網路分析, 頻繁項目樣式成長, social tags, controlled vocabularies, indexing, mental models, social network analysis, frequent pattern growth