Exploring Big Data Analytic Approaches to Cancer Blog Text Analysis

In this article, the authors explore the potential of a big data analytics approach to unstructured text analytics of cancer blogs. The application is developed using Cloudera platform's Hadoop MapReduce framework. It uses several text analytics algorithms, including word count, word association, clustering, and classification, to identify and analyze the patterns and keywords in cancer blog postings. This article establishes an exploratory approach to involving big data analytics methods in developing text analytics applications for the analysis of cancer blogs. Additional insights are extracted through various means, including the development of categories or keywords contained in the blogs, the development of a taxonomy, and the examination of relationships among the categories. The application has the potential for generalizability and implementation with health content in other blogs and social media. It can provide insight and decision support for cancer management and facilitate efficient and relevant searches for information related to cancer.

Learning Morality with Siblings: The Untold Tale of a Mid-Twentieth Century Taiwanese Family

This article uses a new theoretical and methodological framework to reconstruct a story of two children from fieldnotes collected by anthropologists Arthur and Margery Wolf in rural Taiwan (1958 to 1960). Through the case of a brother–sister dyad, it examines the moral life of young children and provides a rare glimpse into sibling relationship in peer and family contexts. First, combining social network analysis and NLP text-analytics, this article introduces a general picture of these siblings’ life in the peer community. Moreover, drawing from naturalistic observations and projective tests, it offers an ethnographic analysis of how children support each other and assert themselves. It emphasizes the role of child-to-child ties in moral learning, in contrast to the predominant focus of parent–child ties in the study of Chinese families. It challenges assumptions of the Chinese “child training” model and invites us to take children's moral psychology seriously and re-discover their agency.

TextQ—A User Friendly Tool for Exploratory Text Analysis

As the amount of textual data available on the Internet grows substantially each year, there is a need for tools to assist with exploratory data analysis. Furthermore, to democratize the process of text analytics, tools must be usable for those with a non-technical background and those who do not have the financial resources to outsource their data analysis needs. To that end, we developed TextQ, which provides a simple, intuitive interface for exploratory analysis of textual data. We also tested the efficacy of TextQ using two case studies performed by subject matter experts—one related to a project on the detection of cyberbullying communication and another related to the user of Twitter for influence operations. TextQ was able to efficiently process over a million social media messages and provide valuable insights that directly assisted in our research efforts on these topics. TextQ is built using an open access platform and object-oriented architecture for ease of use and installation. Additional features will continue to be added to TextQ, based on the needs and interests of the installed base.