Information Disclosure and Online Social Networks: From the Case of Facebook News Feed Controversy to a Theoretical Understanding

Based on the insights learned from the case analysis of the Facebook News Feed outcry, we develop a theoretical understanding that identifies major drivers and impediments of information disclosure in Online Social Networks (OSNs). Research propositions are derived to highlight the roles of privacy behavioral responses, privacy concerns, perceived information control, trust in OSN providers, trust in social ties, and organizational privacy interventions. The synthesis of privacy literature, bounded rationality and trust theories provides a rich understanding of the adoption of OSNs that creates privacy and security vulnerabilities, and therefore, informs the privacy research in the context of OSNs. The findings are also potentially useful to privacy advocates, regulatory bodies, OSN providers, and marketers to help shape or justify their decisions concerning OSNs

Robust estimation of bacterial cell count from optical density

Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals <1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data

26th Annual Computational Neuroscience Meeting (CNS*2017): Part 3 - Meeting Abstracts - Antwerp, Belgium. 15–20 July 2017

This work was produced as part of the activities of FAPESP Research,\ud Disseminations and Innovation Center for Neuromathematics (grant\ud 2013/07699-0, S. Paulo Research Foundation). NLK is supported by a\ud FAPESP postdoctoral fellowship (grant 2016/03855-5). ACR is partially\ud supported by a CNPq fellowship (grant 306251/2014-0)

MessageLens: A Visual Analytics System to Support Multifaceted Exploration of MOOC Forum Discussions

Massive Open Online Courses (MOOCs) often provide online discussion forum tools to facilitate learner interaction and communication. Having massive forum messages posted by learners everyday, MOOC forums are regarded as an important source for understanding learners activities and opinions. However, the high volume and heterogeneity of MOOC forum contents make it challenging to analyze forum data effectively from different perspectives of discussions and to integrate diverse information into a coherent understanding of issues of concern. In this paper, we report a study on the design of a visual analytics tool to facilitate the multifaceted analysis of online discussion forums. This tool, called MessageLens, aims at helping MOOC instructors to gain a better understanding of forum discussions from three facets: discussion topic, learner attitude, and communication among learners. With various visualization tools, instructors can investigate learner activities from different perspectives. We report a case study with real-world MOOC forum data to present the features of MessageLens and a preliminary evaluation study on the benefits and areas of improvement of the system . Our research suggests an approach to analyzing rich communication contents as well as dynamic social interactions among people. Keywords: Multifaceted analysis, MOOC forum, visual analytic

daisy viz: a model-based user interface toolkit for interactive information visualization systems

While information visualization technologies have transformed our life and work, designing information visualization systems still faces challenges. Non-expert users or end-users need toolkits that allow for rapid design and prototyping, along with supporting unified data structures suitable for different data types (e.g., tree, network, temporal, and multi-dimensional data), various visualization, interaction tasks. To address these issues, we designed DaisyViz, a model-based user interface toolkit, which enables end-users to rapidly develop domain-specific information visualization applications without traditional programming. DaisyViz is based on a user interface model for information (UIMI), which includes three declarative models: data model, visualization model, and control model. In the development process, a user first constructs a UIMI with interactive visual tools. The results of the UIMI are then parsed to generate a prototype system automatically. In this paper, we discuss the concept of UIMI, describe the architecture of DaisyViz, and show how to use DaisyViz to build an information visualization system. We also present a usability study of DaisyViz we conducted. Our findings indicate DaisyViz is an effective toolkit to help end-users build interactive information visualization systems. (C) 2010 Elsevier Ltd. All rights reserved