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Enregistrement W1952647053 · doi:10.18438/b89c9r

Research into the Impact of Facebook as a Library Marketing Tool is Inconclusive

2010· article· en· W1952647053 sur OpenAlexvenueno aff
Lotta Haglund, David Herron

Notice bibliographique

RevueEvidence Based Library and Information Practice · 2010
Typearticle
Langueen
DomaineComputer Science
ThématiqueWeb and Library Services
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésLibrary scienceSocial network (sociolinguistics)World Wide WebComputer scienceSociologyPsychologySocial media

Résumé

récupéré en direct d'OpenAlex

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.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,002
score de la tête « metaresearch » (Gemma)0,001
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesCommunication savante, Charge utile insuffisante (le modèle a refusé de juger)
Catégories consensuellesCommunication savante
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,824
Score d'incertitude au seuil0,999

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0020,001
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,001
Études des sciences et des technologies0,0000,000
Communication savante0,0020,426
Science ouverte0,0010,001
Intégrité de la recherche0,0000,001
Charge utile insuffisante (le modèle a refusé de juger)0,0020,000

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.

Tête enseignante Opus0,014
Tête enseignante GPT0,312
Écart entre enseignants0,298 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; les deux têtes enseignantes s’accordent sur ce qui est montré ici.

Devis d'étudeSans objet
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2010
Routes d'admission1
Résumé présentoui

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