Family Communication Patterns as Predictors of Organizational Dissent: A Replication Study

This study replicated a previous research project addressing connections between family-of-origin communication and organizational dissent expression. We predicted that family communication patterns (FCPs), specifically conversation and conformity orientations, would predict upward, lateral, and displaced dissent. As in the original study, significant findings emerged for upward dissent and conformity orientation. This replication study indicated family type as an inconsistent predictor of expressed dissent. Contrary to the original study, we found no evidence to support a connection between pluralistic family types and upward dissent. Finally, as an extension of the original study we analyzed effects of FCPs on displaced dissent, finding an inverse association with conversation orientation. Implications for future research are discussed.     

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.     

Setting a Non-Agenda: Effects of a Perceived Lack of Problems in Recent News or Twitter

The mere perception that news has given certain problems more coverage can lead the audience to assume that those problems are more important. Given that the news media, at times, obsesses over relatively trivial matters, and given that the audience is increasingly able to filter media exposure, it is worth asking what happens when audience members perceive that recent media coverage has not emphasized any very important problems. In such cases, audience members might assume that any problems facing the nation must not be particularly important. We explicate this attitude of political complacency, test whether perceived media agendas lacking important problems can influence it, and explore whether complacency helps explain political disengagement. We also explore whether these effects generalize beyond news, to new media gatekeepers such as Twitter. Two experiments tested effects of a perceived absence of important problems in recent news or Twitter content. In the case of news, but not Twitter, this increased complacency in both studies. Study 2 added a no-exposure control and found that effects on complacency were driven by the cueing of nonproblem stories, not by the absence of problem story cues. Both studies validated complacency as a predictor of political disengagement.     

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.