The LIS Blogosphere Contains Tags that Can Be Categorized and It Disseminates Professional Content
Bibliographic record
Abstract
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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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.202 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".