The LIS Blogosphere Contains Tags that Can Be Categorized and It Disseminates Professional Content
Notice bibliographique
Résumé
A Review of: Aharony, N. (2009). Librarians and information scientists in the blogosphere: An exploratory analysis. Library & Information Science Research, 31(3), 174-181. Objective – This study analyzes library and information studies (LIS) oriented blogs to determine the content, and looks at tags and folksonomies of these blogs to determine whether they form a consistent, coherent scheme or whether they are lacking in internal logic. Design – A qualitative content analysis of tags assigned to 30 LIS blogs. Setting – The research took place on the internet from May to July, 2008. Subjects – Thirty LIS blogs were examined, each of which was written by a librarian or an information scientist. Methods – The researcher reviewed 100 blogs that were found by browsing the Top 25 Librarian Bloggers as published by the Online Education Database in 2007 and by searching Technorati, one of the main search engines for blogs, using the term “library and information science.” Thirty blogs were chosen for analysis based on two criteria: the blog had to be written by a librarian or an information scientist, and the blog had to be active during the period studied (May-July, 2008). A content analysis was undertaken on the tags assigned to the 30 blogs by categorizing the tags that appeared as tag clouds (visual representations of user-generated tags in which the tags used more frequently are depicted in larger, bolder font) in Technorati. In order to validate the Technorati tags, the researcher’s coders read and analyzed all the blog posts over the given time period. The categorization consists of five major categories, each with several subcategories. The categories were developed using a clustering approach, with new categories coming into being when a tag did not fit into an already established category. Main Results – The tag categorization resulted in five broad categories, each with several sub-categories (a few of which are listed here): 1. General (Nouns, Disciplines, Place Names) 2. Library-related (Web 2.0, Librarians’ Activities, Catalogues) 3. Technology-related Products, Technology – Types, People) 4. Information-related (Access to Information, Information Sources) 5. Social web-related (Names of Blogs, Names of Social Networks) The tag analysis resulted in the following percentages of distribution: • 33.62% of the tags associated with LIS blogs were general in nature • 20.21% of the tags were technology-related • 19.12% of the tags were library-related • 14.60% of the tags were information-related • 12.90% of the tags were related to the social web These percentages add up to 100.45%. The author makes no mention of this oddity and it is assumed to be an error. The researcher attempted to determine if tags and folksonomies form a consistent scheme. In reporting her findings, she concluded that four major categories of professional-related content were revealed, which reflect the blogger-librarians’ fields of interest. The prominence of the general category revealed that bloggers’ personal interests and experiences were written about more often. As well, it appears that although bloggers seem to assign non-related tags randomly, the analysis shows that tags still can be categorized. Conclusion – The researcher concludes that this study is helpful for librarians and information scientists because it can help them to navigate the LIS blogosphere. She reports that the categories of tags beyond the general category, which mainly contains tags related to bloggers’ personal interests and experiences, shows that blogs can contribute to professional development. Although more informal in nature, the research has shown that LIS blogs do contain professional information, and it behooves professionals to become familiar with the tag scheme in topic oriented blogs, and to try to work within the scheme to make use of the content within. The researcher suggests further ideas for research, including the differences in LIS blogs written by a single blogger as compared with blogs written by multiple authors, as well as gender differences between male and female authored blogs. The author also suggests further research on multimedia blogs such as photoblogs, and audio and video blogs.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,003 | 0,011 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,015 | 0,018 |
| Études des sciences et des technologies | 0,003 | 0,003 |
| Communication savante | 0,007 | 0,009 |
| Science ouverte | 0,000 | 0,003 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,009 | 0,004 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».