Research into the Impact of Facebook as a Library Marketing Tool is Inconclusive
Bibliographic record
Abstract
A Review of: Xia, D. Z. (2009). Marketing library services through Facebook groups. Library Management 30(6/7), 469-477. Objective – To investigate whether Facebook Groups are useful for library marketing. Design – Content analysis of membership and activity of university library-related Facebook Groups. Setting – Two global Facebook Groups, and the Facebook Groups of two academic libraries in the US (Rutgers University and Indiana University, both with populations in excess of 30 000 students). Subjects – A total of 28 Facebook Groups were analyzed. Methods – Facebook global Groups are open to all users, while Groups based in a network (e.g., a university) only allow access for those in the network. Therefore, to collect data, the author used personal connections to log on to members’ profiles within university networks. The 26 university Groups were selected by searching Facebook for Groups belonging to the two university networks, using the word “library.” Groups unrelated to library business were discarded. A total of 11 Groups within the Rutgers network were analyzed. Of these, only one was organized by a librarian; the rest were organized by students. From Indiana, 15 Groups were identified, three of which were organized by librarians. In Table 1 (p. 474), all Groups are listed: 2 global Groups and 26 Groups within the two university networks. The author then visited all Groups, read all posts, and recorded the total number of members; status of each member, divided into faculty, staff and students; dates of first and last post; and discussion activity. The author analyzed group activity by keeping a tally of how often each member participated in discussions, as there was no way to see the number of times a member returned. The author also paid special attention to Groups with a large number of staff and faculty members, to gain information about the efforts of librarians to support or start new Groups. Main Results – There were a total of 652 members in the 26 university Groups (mean number of members was 25, ranging from 2 - 176). The two global Groups had a total of 12,665 members. Students were most active at starting new Groups, but these were on average very small (around 20 members), with very little discussion. Most discussions focused on limited topics or were event-driven, and therefore failed to retain member participation. The most active Facebook Groups were the global Groups. These Groups had a high staff and faculty membership, and librarians played an important role in promoting and maintaining group discussions. Conclusion – According to the author, a successful Facebook Group should be managed by active organizers, and discuss a broad range of topics. Good examples of active Groups were the two global Groups. Group activity should be diverse, include discussion topics and wall posts, as well as messages sent to group members. The messages were found to be critical for library marketing as they appear as personal messages in members’ inboxes.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.021 | 0.087 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.030 | 0.004 |
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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".