Social media are a potentially valuable source of situational awareness information during crisis events. Consistently, "digital volunteers" and others are coming together to filter and process this data into usable resources, often coordinating their work within distributed online groups. However, current tools and practices are frequently unable to keep up with the speed and volume of incoming data during large events. Through contextual interviews with emergency response professionals and digital volunteers, this research examines the ad hoc, collaborative practices that have emerged to help process this data and outlines strategies for supporting and leveraging these efforts in future designs. We argue for solutions that align with current group values, work practices, volunteer motivations, and organizational structures, but also allow these groups to increase the scale and efficiency of their operations.

We present results from a mixed methods study of screen reader use and switching behavior among people with vision impairments in India. We examine loyalty and experimentation with screen readers and find that the main drivers of adoption for early users differ significantly from the factors that drive continued use by advanced users. We discuss the factor that emerges as one of the strongest stated drivers of early adoption, TTS “voice” quality, particularly a “human-sounding voice” as one of the key features differentiating free/open source (FOSS) products from more expensive proprietary products. While the initial preferences are driven by voice quality, application support becomes more important over time as users speed up their sound settings and become more comfortable with the resultant non-human-sounding speech. We discuss these findings from two theoretical perspectives – first, through the application of the economics of behavior switching, and second, vis-à-vis novice and expert approaches toward new product adoption. We argue that these findings further our understanding of initial user comfort related to assistive technology adoption, and the impact of early technology choices on long-term technology switching behavior.

We present the results of two surveys and a qualitative interviewbased study with users of screen readers in India. Our early interviews moved us in the direction of examining patterns that differentiate users of two particular software applicationsthe dominant market standard JAWS and the free, open source challenger NVDA. A comparison between the two is timely and particularly relevant to issues elsewhere in the developing world. In the short term, the question of choosing one application over another could be based on price and support for custom-made applications, but in the long term, issues of language support are likely to be of concern as well. We explore software adoption behavior and present results that show the relationship between the quality of audio and peoples" willingness to use one software over another. We also compare the switch from JAWS to NVDA to other kinds of switches from dominant software to open source options. In conclusion, we discuss the business aspects of screen readers and examine why the comparison between these two applications is particularly important in the discussion on accessible personal computing for people with vision impairments in the developing world.

In this paper, we extend work examining the issues in language support for screen readers for vision-impaired computer users by building and testing a Hindi-language text-to-speech interface for screen reading. The paper first discusses the importance of 'mechanical voice' screen reading for the ICTD community in an environment where many 'natural voice' options are proprietary and expensive. We find that the main criticism against mechanical voice -the issue of comprehension -can be overcome by training using familiar metaphors, and that the most persistent problems in audio comprehension are ones that are solvable through technical means. Given the structure of vowel-consonant structure of most Indo-Aryan languages, we argue that mechanical voice audio output holds very significant scope for screen reading for vision impaired populations in India.

Efficient health systems require reliable data. In developing countries the need for accurate data is particularly acute, as organizations are often forced to make decisions on a tight budget with limited capacity for data collection. In this note, we describe recent progress toward developing a set of algorithms that can help detect and classify anomalies in health worker data. Building on recent efforts to use unsupervised multinomial techniques for outlier detection, we outline the steps required to turn a set of statistical tests into a framework that can be implemented by health organizations, and calibrate these algorithms on a large dataset from a partner health organization. Here, we describe the core methods, present results from ongoing analyses, and outline our plan for future work, including plans to obtain labeled training data that will allow us to detect and classify different types of outlier in community health worker data.