Using Technology Enabled Qualitative Research to Develop Products for the Social Good, An Overview

This paper discusses the potential benefits of the convergence of three recent trends for the design of socially beneficial products and services: the increasing application of qualitative research techniques in a wide range of disciplines, the rapid mainstreaming of social media and mobile technologies, and the emergence of software as a service. Presented is a scenario facilitating the complex data collection, analysis, storage, and reporting required for the qualitative research recommended for the task of designing relevant solutions to address needs of the underserved. A pilot study is used as a basis for describing the infrastructure and services required to realize this scenario. Implications for innovation of enhanced forms of qualitative research are presented

An integrated framework to classify healthcare virtual communities

Healthcare (HC) strives to improve service quality through its cost-effective social computing strategy. However, sudden rise in the count of virtual community of practices (VCoPs) introduced many choices for physicians; As a result, it is not surprising to observe current literature reporting lack of study to investigate ideas integration within and between VCoPs. VCoPs need to be categorized for HC physicians so they will be able to pin-point effective a VC to attain assistance from. This paper is one of the first investigative studies, in HC sector, that proposed a framework to classify and pin-point appropriate VCoPs, for physicians, after it reviewed and analyzed traditional and up-to-date theoretical, empirical and case study literature in the area of social computing, knowledge management (KM) and VCoPs. The implementation of this framework pinpointed professional VCoPs as most appropriate for physicians based on strict requirements, i.e. closed physician communities holding many participants, which are older than 5 years with high boundary crossing. This framework is also a “one-size-fit-all” formula to build an organizational VCoP, utilizable by other business sectors

Social media analytics: a survey of techniques, tools and platforms

This paper is written for (social science) researchers seeking to analyze the wealth of social media now available. It presents a comprehensive review of software tools for social networking media, wikis, really simple syndication feeds, blogs, newsgroups, chat and news feeds. For completeness, it also includes introductions to social media scraping, storage, data cleaning and sentiment analysis. Although principally a review, the paper also provides a methodology and a critique of social media tools. Analyzing social media, in particular Twitter feeds for sentiment analysis, has become a major research and business activity due to the availability of web-based application programming interfaces (APIs) provided by Twitter, Facebook and News services. This has led to an ‘explosion’ of data services, software tools for scraping and analysis and social media analytics platforms. It is also a research area undergoing rapid change and evolution due to commercial pressures and the potential for using social media data for computational (social science) research. Using a simple taxonomy, this paper provides a review of leading software tools and how to use them to scrape, cleanse and analyze the spectrum of social media. In addition, it discussed the requirement of an experimental computational environment for social media research and presents as an illustration the system architecture of a social media (analytics) platform built by University College London. The principal contribution of this paper is to provide an overview (including code fragments) for scientists seeking to utilize social media scraping and analytics either in their research or business. The data retrieval techniques that are presented in this paper are valid at the time of writing this paper (June 2014), but they are subject to change since social media data scraping APIs are rapidly changing

Mining and Visualizing Research Networks using the Artefact-Actor-Network Approach

Reinhardt, W., Wilke, A., Moi, M., Drachsler, H., & Sloep, P. B. (2012). Mining and Visualizing Research Networks using the Artefact-Actor-Network Approach. In A. Abraham (Ed.), Computational Social Networks. Mining and Visualization (pp. 233-268). Springer. Also available at http://www.springer.com/computer/communication+networks/book/978-1-4471-4053-5Virtual communities are increasingly relying on technologies and tools of the so-called Web 2.0. In the context of scientific events and topical Research Networks, researchers use Social Media as one main communication channel. This raises the question, how to monitor and analyze such Research Networks. In this chapter we argue that Artefact-Actor-Networks (AANs) serve well for modeling, storing and mining the social interactions around digital learning resources originating from various learning services. In order to deepen the model of AANs and its application to Research Networks, a relevant theoretical background as well as clues for a prototypical reference implementation are provided. This is followed by the analysis of six Research Networks and a detailed inspection of the results. Moreover, selected networks are visualized. Research Networks of the same type show similar descriptive measures while different types are not directly comparable to each other. Further, our analysis shows that narrowness of a Research Network's subject area can be predicted using the connectedness of semantic similarity networks. Finally conclusions are drawn and implications for future research are discussed

News now: exploratory study of digital news story organization and structure

2017 Spring.Includes bibliographical references.Newspaper publication has expanded beyond the printed format to digital formats to attract readers using iPhone apps, Facebook, Twitter and other outlets. Some apps will open the full story and others link to the full story on the newspaper's website. My exploratory research sought to explore different digital platforms by investigating Washington Post headlines written for the iPhone Application, Facebook, and Twitter. While these platforms limit the information available before linking to the full story on the website, each digital platform provided enough information to identify organizational patterns and sequences of who, what, where, why and how — the key concepts in the journalistic inverted pyramid writing organization. My research investigated the Washington Post's digital headlines in the summer of 2012. The research questions were RQ1: Which questions are answered most frequently in news story headlines on the iPhone app, Facebook newsfeed, and Twitter tweets? RQ2: What are the question sequences presented in the headlines on the iPhone app, Facebook newsfeed, and Twitter tweets? RQ2A: Is there a difference in organization of questions sequences in the headlines of story topics present in one of each of the following platforms: iPhone app, Facebook newsfeed, and Twitter tweets? For my content analysis of the Washington Post digital headlines, I created a sample of a constructed week and took screenshots of headlines. For analysis, I coded all stories (n = 216) published on at least one other platform. I developed a codebook, and one additional coder and I coded every headline in the sample. Despite some variables receiving lower Krippendorff Alpha results than suggested for publication for intercoder reliability (ranging from 0.33 to 0.83), most variables achieved acceptable percent agreements from 84.7% to 95.8%. Because of the exploratory nature of my study, I proceeded with data analysis. Patterns emerged related to information sequences in headlines. "Who" and "what" were used in 77% (n = 22) as leading information in the headline sequences. "Where" was the only other variable included at the beginning of sequences. While 22 different organizational sequences emerged, 50% were used only once. Research Question 2A investigated organization of question sequences. The variable "what," a main action, was included in 100% (n = 27) of the headlines in a portion of the sample using a single headline for each platform about one story. The sequence (who, what) was included in 22% (n = 9) across all three platforms. Other story topics provided additional variables on different platforms