Leveraging the Power of a Twitter Network for Library Promotion

Abstract

The Twitter network of two academic libraries was analyzed to determine the influential accounts that connect to them. Such information can be exploited by libraries to create tailored social media outreach and information dissemination programs. Three network metrics, measuring different definitions of importance, were calculated for each account in the network. This allowed for the quantification and ranking of the accounts by influence/ importance, normally considered to be qualitative and subjective. By all measures, accounts associated with the institutions, and not faculty, staff, or students, were found to be the most influential players in the networks of both libraries, suggesting that this is a general feature of academic library Twitter networks. Furthermore, the library, as an institutional account itself, is also influential to the broader Twitter community of its home institution. This demonstrates that the library is in a key position to propagate information from sister accounts at the institution.

Integrating Text Mining and Network Analysis for Topic Detection from Published Articles on Banana Sensory Characteristics

“…Nodes with many ties like banana in this instance are said to be prominent, or have high prestige as many nodes seek to direct ties towards them, an indication of importance. The in-degree is a measure of popularity based on the number of connections to a node [10], representing the amount of attention the node receives. Hence, banana co-occurred more than other terms.…”

“…The topic of interest being sensory attributes of banana and its products. The limitation of using a node's degree to quantify its significance is that each connection is valued equally as it assumes that forming a connection with an important node counts as much as a connection to an unimportant one [10]. Practically, developing a connection with the chief executive of a company gives more influence than a connection with someone in an entry level position.…”

“…The base class consisting of 20.7% (green), class 1, 20.8% (red), class 2, 41.5% (purple) and class 3, 17.0% (blue) of the nodes. Betweenness centrality accounts for the nodes that lie on many short paths having considerable control over information diffusion in the network acting as gatekeepers of information [10]. Terms with high betweenness centrality are also high in in-degree and eigenvector centrality and are therefore connected to many highly connected terms.…”

From Handpress to Handheld: Rare Book and Special Collections Libraries Forming the Libraries of Instagram

“…Library and archival literature has a wide coverage of how libraries can and should use social media platforms such as Facebook and Twitter to market library events and services, but much less on Instagram or on the use of social media for community building. The idea has been that "[i]nformation dissemination is the primary goal of any Twitter program run by the library" [33], focusing the use of social media in library orientation, for example, to promotion rather than to engaging users. While it is important to share knowledge of what is in a collection, passive sharing is not the same as engaging and adding new members to the community of those passionate, knowledgeable or even curious about rare books and special collections.…”

Faculty Response to Deselection in Academic Libraries: A Psycholinguistic Analysis

“…"From the time of Freud's writings about slips of the tongue to the early days of computer-based text analysis, researchers began amassing increasingly compelling evidence that the words we use have tremendous psychological value" (Pennebaker et al 2015, "Development," 1). In the field of library science, text analysis of the type used in psycholinguistics has been employed to provide insights into the language used in academic libraries' social media feeds (Al-Daihani and Abrahams 2016;Sewell 2013;Shulman et al 2015). Other researchers have used this approach Faculty Response to Deselection 4 to analyze user communities directly by examining search queries (Papatheodorou et al 2003;Yang and Hung 2012).…”