Investigating the Role of Problem Severity and Channel Publicness on the Sophistication of Support-Seeking Messages on Social Network Sites: A Replication Study

Oh and LaRose (2016) contended that problem severity and channel publicness shape support seekers’ goals, time spent composing messages, and the sophistication of those messages. The current study seeks to replicate and extend Oh and LaRose’s study to produce a more accurate understanding of the factors that influence the sophistication of support-seeking messages online. Support seekers’ goals varied according to the severity of a stressor and the publicness of a channel, and the amount of time people spent composing a message influenced its sophistication. We extend the original study by examining associations that were not initially tested. Time spent composing messages mediated the influence of problem severity on the quality of people’s messages, and this effect was moderated by channel publicness.     

The Dynamic Landscape of Marketing Children’s Books: Publishers Find Consistent Success Through a Combination of Online and Traditional Marketing to Adults and Children

There are a number of challenges unique to the children’s book sector faced by publishers in today’s digitally-dominant business market, making it more crucial than ever for children’s book marketers to create multifaceted marketing plans that include differing but complementary approaches. Among the common marketing challenges specific to children’s books are overabundance of content, competition, access to the library market, the digital versus print dichotomy, difficulty of marketing directly to young readers on the Internet, and the dual (child and adult) target audience. While some marketing tactics must effectively attract the attention of the child reader, it is of fundamental importance (with some exceptions) for publishers, authors, and marketers of children’s books to also ensure their marketing techniques target the adult gatekeepers who are likely to purchase those books. Depending upon various factors, including age range of the reader and subject matter, marketers of children’s books are most successful when applying a mix of traditional and digital marketing techniques.     

Features of Islamic Children’s Books in English: A Case Study of Books Published in Malaysia

The purpose of this study is to investigate the elements of Islamic children’s books that are written in English by focusing on books that are available in the Malaysian market and published in Malaysia. This study applies the content analysis method, by examining the selected books of their subject matter as well as the language. The results of the study reveal that Islamic books written for children in English are distinctive in terms of the content and language features.     

Using word embeddings in Twitter election classification

Word embeddings and convolutional neural networks (CNN) have attracted extensive attention in various classification tasks for Twitter, e.g. sentiment classification. However, the effect of the configuration used to generate the word embeddings on the classification performance has not been studied in the existing literature. In this paper, using a Twitter election classification task that aims to detect election-related tweets, we investigate the impact of the background dataset used to train the embedding models, as well as the parameters of the word embedding training process, namely the context window size, the dimensionality and the number of negative samples, on the attained classification performance. By comparing the classification results of word embedding models that have been trained using different background corpora (e.g. Wikipedia articles and Twitter microposts), we show that the background data should align with the Twitter classification dataset both in data type and time period to achieve significantly better performance compared to baselines such as SVM with TF-IDF. Moreover, by evaluating the results of word embedding models trained using various context window sizes and dimensionalities, we find that large context window and dimension sizes are preferable to improve the performance. However, the number of negative samples parameter does not significantly affect the performance of the CNN classifiers. Our experimental results also show that choosing the correct word embedding model for use with CNN leads to statistically significant improvements over various baselines such as random, SVM with TF-IDF and SVM with word embeddings. Finally, for out-of-vocabulary (OOV) words that are not available in the learned word embedding models, we show that a simple OOV strategy to randomly initialise the OOV words without any prior knowledge is sufficient to attain a good classification performance among the current OOV strategies (e.g. a random initialisation using statistics of the pre-trained word embedding models).     

Clustering small-sized collections of short texts

The need to cluster small text corpora composed of a few hundreds of short texts rises in various applications; e.g., clustering top-retrieved documents based on their snippets. This clustering task is challenging due to the vocabulary mismatch between short texts and the insufficient corpus-based statistics (e.g., term co-occurrence statistics) due to the corpus size. We address this clustering challenge using a framework that utilizes a set of external knowledge resources that provide information about term relations. Specifically, we use information induced from the resources to estimate similarity between terms and produce term clusters. We also utilize the resources to expand the vocabulary used in the given corpus and thus enhance term clustering. We then project the texts in the corpus onto the term clusters to cluster the texts. We evaluate various instantiations of the proposed framework by varying the term clustering method used, the approach of projecting the texts onto the term clusters, and the way of applying external knowledge resources. Extensive empirical evaluation demonstrates the merits of our approach with respect to applying clustering algorithms directly on the text corpus, and using state-of-the-art co-clustering and topic modeling methods.     

Imagining transformative spaces: the personal–political sites of community archives

Although much has been written about formal archival spaces, little scholarship has addressed the physical spaces of community archives. This paper asks: How do community members imagine the physical spaces that steward identity-based community archives? Based on focus groups with more than 54 community archives users at five different community archives sites across Southern California, this paper examines how members of marginalized communities conceive of the physical space inhabited by community archives representing their communities. The sites explored range from a prominent location on a university campus, to storefronts, strip malls, and small cinderblock buildings. Yet across sites, users spoke about community archives spaces as symbolic and affectively moving locations. Many users described their community archives site as a “home-away-from-home,” marked by intergenerational dialog and a profound sense of belonging. For other users, community archives sites were described as “politically generative spaces” which foster dialog and debate about identity, representation, and activism and enable the community to envision its future. And yet, while the very existence of community archives is political, many participants felt that the full political potential of these sites is not yet realized. By listening to the voices of the communities represented and served by community archives, our research both indicates that a shift is warranted in archival metaphors of space and reveals how community archives are personally and politically transformative spaces for the communities they represent and serve.     

Traveling through: exploring doctoral demographics in archival studies

The growth of archival studies programs has prompted archival scholars to establish an international network for supporting collaborative research, curriculum development, and pedagogy. Doctoral education is key to the sustainability of such programs and the continuation of the network over time. We carried out longitudinal research to survey the population of doctoral students attending one or more Archival Education and Research Institutes (AERI), an annual meeting first held in 2009. Building on prior research on graduate archival education, we gathered demographic and qualitative data about doctoral students specializing in archival studies who are based in several countries including the USA. We sought to assess attendee motivations, guide conference planning, and help advance overall AERI objectives. Our study provides a baseline understanding of the disciplinary backgrounds, research directions, and specific professional development activities that doctoral students in archival studies pursued around the globe from 2013 to 2015. This paper argues that doctoral education should continue to be a particular subject of archival research and indicates how archival students’ range of academic interests is diversifying and strengthening the scholarly community.