The threshold model as a classical paradigm for studying information spreading processes has been well studied. The main focuses are on how the underlying social network structure or the size of initial seeds can affect the cascading dynamics. However, the influence of node characteristics has been largely ignored. Here, inspired by empirical observations, we extend the threshold model by taking into account lurking nodes, who rarely interact with their neighbors. In particular, we consider two different scenarios: (i) Lurkers are absolutely silent and never interact with others and (ii) lurkers intermittently interact with their neighborhood with an activity rate p. In the first case, we demonstrate that lurkers may reduce the effective average degree of the underlying network, playing a dual role in spreading dynamics. In the latter case, we find that the stochastic dynamic behavior of lurkers could significantly promote the spread of information. Concretely, slightly raising the activity rate p of lurkers may result in a remarkable increase in the final cascade size. Further increasing p could make nodes become more stable on average, while it is still easy to observe global cascades due to the fluctuations of the effective degree of nodes.
Frequency Fg of global cascades as a function of node threshold ϕ and average degree z for different values of f: (a) f=0, corresponding to the original threshold model, and (b) f=0.5. The red lines show the boundary of the global cascade regime according to Eq. (7). Simulations correspond to an ER network with size N=5000 and are averaged over 104 realizations.
In order to gain a deeper understanding of social media, we analyzed relevant abstracts that were downloaded from the Web of Science (WOS) database. Our search termsFootnote 1 yielded a total of 13,177 records, out of which 12,597 unique abstracts were obtained. The analysis of these records was undertaken in two steps. First, we used VOSviewer (Van Eck and Waltman 2011) to perform a co-citation analysis of first authors in the downloaded corpus. VOSviewer allows visualization of similarities in publications and authors through an examination of bibliometric networks. Furthermore, we used VOSviewer to analyze words derived from titles and abstracts. Second, we used Latent Dirichlet Allocation (LDA) (see Blei 2012) to extract key thematic areas latent in the literature on social media. Further details about these analyses and results are presented in section 3.
Relevant articles were then identified and downloaded from each of the target journals by going through their archives. Specifically, all volumes and issues published in these journals between 1997 and 2017 were considered in our analysis. Articles, research notes, introductions, research commentaries, and editorial overviews relevant to social media were downloaded and numbered to prepare an APA style reference list. The first literature search resulted in 181 articles that had some relevance to the social media domain. A closer examination of individual abstracts and full articles led to the elimination of 49 irrelevant articles, thus giving us a total of 132 articles pertinent to the domain of interest (i.e., social media).
VOSviewer was used to analyze terms (i.e., words) in the titles and abstracts of our corpus to obtain a two-dimensional map showing proximities of words that are likely to be related based on their co-occurrences. Specifically, VOSviewer relies on the Apache OpenNLP Toolkit to identify noun phrases, and then compares their overall co-occurrence distribution with their distribution across other noun phrases to compute a relevance score (Van Eck and Waltman 2011). The intuition is that frequently co-occurring noun phrases with high relevance are likely to unravel a topic or theme that is latent in the corpus. The term map from VOSviewer is shown in Fig. 2. Only terms that occurred 50 times or more were included. Furthermore, relevance scores computed by VOSviewer for every term were used to select the top 80% that met the threshold.
In this issue, readers will get a first taste of what our presenters have been working on since last July. Whitver and Riesen address the complexities of transfer learning within the course-embedded library instruction environment; Jankowski, Russo, Beene and Townsend approach student (non-expert) struggles to evaluate the trustworthiness of information sources through the threshold concepts of format and authority; and Beatty and Hernandez Jr bring visual literacy and critical information literacy together and highlight the value students place on information literacy when it comes to social justice topics. Maxson et al. explore peer teaching implementation strategies and student feedback on different approaches; Martinez and Forrey foray into the psychological elements of imposter syndrome within academic library instruction work; and Murphy investigates the potential for collaborative research assignment design between academic librarians and new graduate teaching assistants toward the establishment of shared-goals and better-leveraged expertise. I hope you enjoy the amuse-bouche of LIW 2018 presented in this issue and relish the opportunity to have a second helping of LIW 2018-inspired articles when the next issue comes out later this year. 2b1af7f3a8