In countries like India which have historically been reliant on rainfed agriculture, the increasing need of water for irrigation to support greater cropping intensity and shifts towards horticulture, has largely been supported through groundwater based irrigation. Cheap electricity has enabled a rapid increase in borewells almost across the country, which to some extent has enabled more equitable access to water than other irrigation approaches like canals, but has also led to groundwater stress in many regions. One way to indirectly estimate groundwater abstraction is to estimate evapotranspiration from cropping areas as a proxy for crop water consumption. Remote sensing based methods have been used to estimate evapotranspiration but existing open data products largely have a low spatial resolution which is not adequate to support local decision making for water use. In this study, we build machine learning methods to develop downscaled data outputs of evapotranspiration at fine spatial scales. Our approach uses satellite data, meteorological variables, and land surface characteristics as input features to obtain field-scale fortnightly time-series of evapotranspiration. We validate the results results across multiple geographic locations and also study its correlation with in situ evapotranspiration measurements. We find that our method is not able to accurately match in situ data but is able to successfully provide relative differences in evapotranspiration. We make our trained models available on the Google Earth Engine platform for use by other researchers and practitioners to obtain evapotranspiration outputs for their areas of interest. Our research contributes a scalable and adaptable solution to address the growing demand for fine-resolution hydro-climatic information.

Government welfare schemes such as the Mahatma Gandhi National Rural Employment Guarantee Act (MGNREGA) in India fund the creation of assets for natural resource management in rural villages to support farmers for their agricultural and livelihoods-based needs. With most agriculture in India being rain-fed, structures such as farm ponds, checkdams, trenches and bunds play a crucial role in supporting groundwater recharge and providing critical lifesaving irrigation in times of dry spells and droughts. In this study, we investigate the impact of farm ponds built under the MGNREGA scheme in Northern India as a source of protective irrigation for cropping areas in their immediate neighbourhood. We assess the impact of farm ponds on the following aspects: (i) we study their impact on agricultural productivity for up to five years since their construction, (ii) we separately study their impact in drought years during this period, (iii) we study the extent to which they are able to to reduce the sensitivity to droughts of sites having farm ponds. A causal analysis framework was designed by identifying control sites that did not have farm ponds, and the treatment effect of having farm ponds was computed using the difference-in-differences approach. Remote sensing data was processed to compute changes in vegetation indices around the treated and control locations before and after the construction of farm ponds. Our results indicate that farm ponds were instrumental in improving the agricultural productivity during the monsoon season in general. The impact during the monsoon season in drought years is also positive and significant. Furthermore, farm ponds also facilitated in reducing drought sensitivity during the monsoon season. The impact during the post-monsoon season was found to be lower, and the impact during the summer agricultural season was found to be the least.

With almost half the Indian population facing water stress [ 42 ], likely to be aggravated with climate change [ 76 , 122 ], an intersecting crises of environment, livelihood, and social justice can worsen the vulnerability of rural communities that are directly dependent upon water resources for their livelihood and sustenance. To address this challenge of rural water security especially for marginalized communities, MGNREGA is a public works and employment generation scheme in India that contributes towards the construction of water related assets such as farm ponds, checkdams, and trenches and bunds, to improve irrigation and climate resilience of rural communities. MGNREGA is a demand-driven scheme that solicits demands for water related assets through the local governance structure in India of village-level community meetings called Gram Sabhas. In many areas, Civil Society Organizations (CSOs) and community based institutions also play a significant role by coordinating participatory processes to build the capacity of rural community members to participate in local governance and demand such assets under MGNREGA. However, through multiple field visits, we observed a need to strengthen local processes with appropriate data and tools to support context-sensitive and evidence-based bottom-up planning. In this study, we adopt an approach of building a digital participatory tool, built on a technology stack that encapsulates different geospatial datasets, to answer key research questions of whether such technology artefacts can improve the participation of rural communities in water security planning and management, through, (1) community empowerment by building a data-driven understanding of water security, (2) ecological sustainability of water resources through scientific planning of assets, and (3) equity led participatory processes to improve fairness in the distribution of assets. We discuss here the design of the tool, Commons Connect, underlying datasets on which it is built, and findings from an initial field testing of the tool across four locations in India. We outline several improvements and open questions that remain, and highlight how such tools can be integrated into the MGNREGA operational workflow and improve the sustainability and resilience of rural social-ecological systems.

Participatory approaches may empower community members toward their development; however, structural and identity-based factors may impede participation. It becomes imperative to critically examine the politics, dynamics, and rhetoric of participation in development contexts. We deconstruct the meaning, means, and need for participation by situating our study in the Indian government's growing efforts to digitalize a nationwide scheme implemented to support rural farmers' participation in natural resource management (NRM) to achieve secure employment and build climate change resilience. Capturing the situated essence through field studies that include observations, interviews, and focus groups, we systematically analyze challenges to participation in NRM and uncover the technological gaps affecting its workflow digitalization. We discuss sociotechnical considerations to meaningfully design and integrate digital technologies toward supporting equitable participation in achieving sustainable development.

Patriarchal practices and other socio-cultural norms in rural North India often prevent women from participating in digital platforms. In this paper we present a voice-based participatory community media platform that runs over simple voice-based phone calls. The platform is meant for women users and is led by women and has been used by 12,222 women users over the course of 20 months. We conduct a qualitative study and present several innovations, such as creating a safe space for women, which have contributed to more participation of women on the platform. We present a framework which can serve as a guidance for researchers and practitioners who want to set up digital platforms for women users.

Abstract: The mass media plays an important role in democratic societies to impose checks and balances on the functioning of various institutions of the state, and in shaping public opinion by informing people about the performance of these institutions. The agenda of the mass media can however be influenced by the government in power, especially if the media is dependent on the government for funding, or the government is powerful and can compromise the safety of media personnel. In this paper, we carefully examine the interactions between three factors: the performance in India of a social welfare scheme on rural employment guarantee (obtained from official records), the volume and sentiment of coverage of these factors in the mass media (obtained through an analysis of news articles of six English national newspapers), and the political alignment between the state governments in different states with the central government (obtained from election data). We construct a time series of these three datasets from 2014 to 2021, and show (a) how various performance factors of the welfare scheme are treated differently by the media in different states based on whether they are aligned or non-aligned with the Central government, and (b) whether coverage in the media is able to influence the performance of the welfare scheme. To the best of our knowledge, this is the first study of its kind to examine the interplay between media bias, government performance, and government influence, and helps uncover the complexities and nuances of these relationships.

Initiation, monitoring, and evaluation of development programmes can involve field-based data collection about project activities. This data collection through digital devices may not always be feasible though, for reasons such as unaffordability of smartphones and tablets by field-based cadre, or shortfalls in their training and capacity building. Paper-based data collection has been argued to be more appropriate in several contexts, with automated digitization of the paper forms through OCR (Optical Character Recognition) and OMR (Optical Mark Recognition) techniques. We contribute with providing a large dataset of handwritten digits, and deep learning based models and methods built using this data, that are effective in real-world environments. We demonstrate the deployment of these tools in the context of a maternal and child health and nutrition awareness project, which uses IVR (Interactive Voice Response) systems to provide awareness information to rural women SHG (Self Help Group) members in north India. Paper forms were used to collect phone numbers of the SHG members at scale, which were digitized using the OCR tools developed by us, and used to push almost 4 million phone calls. The data, model, and code have been released in the open-source domain.

Many citizens consume information on government policies from the mass media. Consequently, biases existing in the policy discourse in media sources may influence citizens’ understanding of the policies, about how they may affect diverse communities. These biases may also get amplified further through social media if it simply echoes the biases of mass media content. We build methods to quantify media bias in terms of preferred treatment given to certain issues corresponding to four economic policies, and alignment observed with the ideological stance of different political parties. We also examine how the social media community of followers of these media houses contribute to the policy discourse. Other than being one of the first large scale studies in the Indian context, our work contributes towards creating a standardized methodology to assess the ideological stance of a news-source, and its alignment with the social media discourse of its follower community. We find that the Indian mass media exhibits bias towards certain aspects or topics related to policy events. It also provides a significantly high coverage to aspects concerning the middle class and to political statements, neglecting the aspects directly relevant to the poor. Additionally, we find evidence of bias also in the representation provided to different political parties in the media. Social media seems to echo these biases rather than mitigate them. The tools and methods developed in this work can be useful for media watchdog institutions to call out biases in the media, and advocate for more complete coverage of issues across different news sources.

This paper describes the corpus and baseline systems for the Gram Vaani Automatic Speech Recognition (ASR) challenge in regional variations of Hindi. The corpus for this challenge comprises the spontaneous telephone speech recordings collected by a social technology enterprise, Gram Vaani. The regional variations of Hindi together with spontaneity of speech, natural background and transcriptions with variable accuracy due to crowdsourcing make it a unique corpus for ASR on spontaneous telephonic speech. Around, 1108 hours of real-world spontaneous speech recordings, including 1000 hours of unlabelled training data, 100 hours of labelled training data, 5 hours of development data and 3 hours of evaluation data, have been released as a part of the challenge. The efficacy of both training and test sets are validated on different ASR systems in both traditional time-delay neural network-hidden Markov model (TDNN-HMM) frameworks and fully-neural end-to-end (E2E) setup. The word error rate (WER) and character error rate (CER) on eval set for a TDNN model trained on 100 hours of labelled data are 29.7 and 15.1, respectively. While, in E2E setup, WER and CER on eval set for a conformer model trained on 100 hours of data are 32.9 and 19.0, respectively.

Online participatory media platforms that enable one-to-many communication among users, see a significant amount of user generated content and consequently face a problem of being able to recommend a subset of this content to its users. We address the problem of recommending and ranking this content such that different viewpoints about a topic get exposure in a fair and diverse manner. We build our model in the context of a voice-based participatory media platform running in rural central India, for low-income and less-literate communities, that plays audio messages in a ranked list to users over a phone call and allows them to contribute their own messages. In this paper, we describe our model and evaluate it using call-logs from the platform, to compare the fairness and diversity performance of our model with the manual editorial processes currently being followed. Our models are generic and can be adapted and applied to other participatory media platforms as well.

Land-cover (LC) classification is required for land management and planning models, and is increasingly done through remote sensing data. Supervised machine learning methods applied to satellite imagery can help with high-resolution LC classification but demand a labeled dataset for training and evaluation of the models. The availability of such datasets is limited though, especially for developing regions like in India. We describe a large pixel-level dataset, IndiaSat, that we have curated and provided for open use, consisting of 180,414 pixels labeled into four LC classes: greenery, water bodies, barren land, and built-up area. Initial labels are obtained through the crowd-sourced mapping platform Open Street Maps (OSM), and then manually curated and corrected. We describe our data cleaning methodology and ensure spatial diversity across different geographic regions in the country. We show that the IndiaSat dataset can be used to train simple classifiers deployed on commodity platforms like Google Earth Engine (GEE) for three popular and openly accessible satellite systems: Landsat-7, Landsat-8, and Sentinel-2, with high accuracy, and to additionally build LC change detection models to determine pixel-level changes over a sequence of several years.

Recent improvements through machine learning in speech technologies and natural language processing has prompted active interest in the development of conversational agents for various tasks. We look at the area of data collection in low-resource settings among rural women in North India, and explore the feasibility of using voice-based surveys conducted through IVR (Interactive Voice Response) systems where users may speak their responses in a conversational manner through natural speech. Through an iterative design process and detailed user feedback, we describe several nuances in running voice-based surveys, and compare their accuracy of data collection through equivalent keypress-based surveys. We find strong user preferences for voice-based surveys, and comparable performance with keypress-based surveys for most types of questions. Our results suggest that voice-based conversational interfaces may hold significant potential to build interactive applications for low-income and less-literate populations. Our findings are likely to be useful for other researchers and practitioners using ICTs (Information and Communication Technologies) in developing regions.

Question-answering systems where users can ask questions based on emergent needs which are then answered by experts or peers, have emerged as an important information seeking modality on digital platforms. Automating this process has been an active area of research since many years, to identify relevant answers from pre-existing question-answer databases. We report on the feasibility of running automated question-answering systems in the context of rural and less-literate users in India, accessed through IVR (Interactive Voice Response) systems. We use commercial speech recognition APIs to convert audio questions asked by users into their equivalent transcripts in real time, in Hindi, and use deep-learning based architectures to retrieve corresponding candidate answers which are instantly played to the users. We report several insights from an earlier phase of running question-answering programmes through a manual operation, to how it was transitioned to an automated setup, and document the user experiences during this journey.

Ideological biases in the mass media can shape public opinion. In this study, we aim to understand ideological bias in the Indian mass media, in terms of the coverage it provides to statements made by prominent people on key economic and technology policies. We build an end-to-end system that starts with a news article and parses it to obtain statements made by people in the article; on these statements, we apply a Recursive Neural Network based model to detect whether the statements express an ideological bias or not. The system then classifies the stance of the non-neutral statements. For economic policies, we determine if the statements express a pro or anti slant about the policy, and for technology policies, we determine if the statements are positive or skeptical about technology. The proposed research method can be applied to other domains as well and can serve as a basis to contrast social media self-expression by prominent people with how the mass media portrays them.

With growing urbanization, being able to track urban change is important to plan cities better. Land-use classification of satellite data has been actively used for this purpose. We augment this analysis through the use of crowd-sourced data about the road network in cities, obtained through the Open Street Maps platform. We develop several indicators to quantify the spatial layout of cities and how different localities have changed over time. We apply our methods to study seven Indian cities (Bangalore, Chennai, Delhi, Gurgaon, Hyderabad, Kolkata, and Mumbai) and relate our findings with that of other studies. Our contribution lies in synthesizing two freely available datasets of satellite imagery and road information to develop a series of standardized indicators for different aspects of urbanization, which can serve to compare various cities with one another and to track change happening in the cities over time.

Corporate Social Responsibility (CSR) is seen as a means for companies to contribute towards broader societal goals beyond their immediate industrial focus, and companies are known to donate a part of their profits to social development for education, health, and other sectors. In 2014, the Government of India made CSR mandatory for companies beyond a certain level of profitability. It was observed however that many geographies in need of financial assistance for social development actually did not receive much CSR funds. In this paper, we investigate what might be the reasons behind how companies choose the locations for their CSR investments. In particular, we examine political reasons where companies may use CSR to seek favours from politicians. We find several interesting patterns and show that there might be grounds for the government to regulate CSR to some extent.

Mobile health interventions are an innovative way to improve health outcomes and may play a powerful role in mitigating health disparities. However, their use poses special challenges and few articles have reported specifically on digital technology interventions for vulnerable populations. This article shares our experience from the Tika Vaani ("vaccine voice") Intervention which uses a combined face-to-face and mHealth strategy to educate and empower beneficiaries to improve immunization uptake and child health for a poor, low-literate population in rural Uttar Pradesh, India. Based on the mERA checklist, a guide to improve the completeness of reporting mHealth interventions, we provide information about the process of development, implementation and lessons for scaling up the Tika Vaani intervention. This study contributes to the literature to improve reporting on mHealth interventions and provide researchers with key points and actions to take during intervention development to serve hard-to-reach communities and improve health outcomes.

With improvements in speech recognition and natural language processing capabilities, voicebot systems show promise to run interactive information services for less-literate populations in developing regions. In this context, we describe our initial experiences towards building an automated question-answering system in the domain of sexual and reproductive health and rights. This system is trained on data acquired from an IVR (Interactive Voice Response) platform on which users could record questions, which were then moderated and sent to an expert to get answers. Our goal is to now use this data to build an automated answer retrieval system so that questions can be answered in real time by retrieving an appropriate answer from the corpus of questions and answers available so far. Our insights are likely to be useful for several initiatives using IVR systems and looking to automate their search and retrieval functionality.

Ensuring responsible outcomes from technology is a challenging goal, and several design methods have been proposed to embed ethical values into the technology itself. Based on a case-study of a voice-based community media platform running in rural India, we argue that design alone is not sufficient to prevent harmful effects and that careful management of the technology during its deployment is also essential. We identify several processes that can be useful to specifically manage digital platforms for information sharing among users. While this may not be a significant insight, we draw attention for researchers to understand the deployment management of technology, beyond its design, as an important area of research in technology and ethics.

The design of participatory media platforms can have many variations in terms of whether or not anonymous communication is allowed, what moderation policies are in use, the conversation model that is supported, etc. We use six case-studies of voice-based ICT4D participatory media platforms running in India to outline a typology of design axes that can be used to characterize these platforms. Such typologies can be useful for ICT4D practitioners to conceptualize suitable designs for their platforms in different contexts, and to reconfigure the platforms in case design changes are required. We also use this typology to discuss the design constraints and flexibilities allowed by Internet based social media platforms.

Models for socio-economic development are useful for planners to build appropriate policies. Such models are ideally constructed based on empirical data, and we take up the problem of working towards a district development model for India by using two waves of census data. India has almost six hundred districts, diverse in terms of their social and economic development, and hence presents a unique natural experiment to understand how social and economic factors interplay with one another. In this paper, we present some interesting observations we are able to make from the analysis of census data from the years 2001 and 2011, and also raise some questions calling for the need for additional ethnographic and other surveys to be able to understand the underlying mechanisms that would have led to the observed patterns.

Fluctuations in food prices can cause distress among both consumers and producers, and are often exacerbated by trading networks especially in developing economies where marketplaces may not be operating under conditions of perfect competition for various contextual reasons. We look at onion and potato trading in India and present the evaluation of a price forecasting model, and an anomaly detection and classification system to identify incidents of hoarding of stock by the traders. Our dataset is composed of time series of wholesale prices and arrival volumes of the agricultural commodities at several village-level marketplaces, and retail prices of the commodities at the city centers. We also provide an in-depth qualitative analysis of the effect on these time series of events such as hoarding, weather disturbances, and external shocks. Our results are encouraging and point towards the possibility of building pricing models for agricultural commodities which can be used to reduce information asymmetries and to detect anomalies that can help regulate agricultural markets to operate more fairly.

The parliament, media, and citizens, are key stakeholders in any democracy. We study their priorities pertaining to four economic policies in India - Demonetization, Aadhaar, GST, and Farmers' Protests - by examining the content of questions asked by politicians in the parliament, news articles published in the mass media, and data from social media. We find that the mass media covers stories about different constituencies (the poor, middle-class, corporates, etc.) but shows biases in terms of which constituencies it chooses to focus on for each policy. The parliament tends to focus on procedural aspects of the policies. We also find that the social media simply echoes the trends of whatever is emphasized more in the mass media, without much deviation in the attention placed by social media users on different issues. We further find out that instead of selecting issues of importance based on feedback from citizens, the parliamentarians mostly indulge in partisanship, and shape their questions based on party goals that change depending on whether their party is in power or not. Overall, we are able to use this analysis to comment on how representative and responsive the Indian democracy is to its citizens.

Social and behaviour change communication interventions are believed to gain from using mobile technology owing to the reach and rapid scaling promise of technology. In this paper we describe our experiences from the layering of an IVR based content platform for behaviour change in maternal and child nutrition practices in the state of Bihar in rural central India. We specifically show that being able to leverage technology effectively can be quite complex and needs a careful implementation design. We demonstrate that technology adoption takes time and has to be encouraged through offline mechanisms. We also demonstrate that diversification of content on the IVR platform can facilitate greater usage of the platform. We outline the technology assisted concurrent monitoring methods developed by us, and how the data analysis helps identify gaps to take timely corrective action. Finally, we discuss the pros and cons of different pathways to reach women users in rural areas. The insights derived from our work can serve as guidelines for designing technology-based behaviour change campaigns.

Policy making is influenced by a number of factors, including electoral politics, ideological biases of actors involved in the policy making process, and the interlocks between corporate and government entities. This influence is also exercised by shaping public opinion through mass media. In this paper, we study four ICTD policies in India, and explore the political economy around them by using data about how these policies are covered in the mass media. We study which actors are covered more in media, how they speak on the policy issues, and which aspects are given more coverage for these policies. We find that politicians get the highest coverage in mass media regarding discussions on policies, and that the politicians and business-persons often express similar ideologies related to these policies. We also observe that mass media is often biased towards issues related to its middle class reader base with a strong sense of technology driven high-modernism, and negative aspects of these policies and issues faced by the poor due to improper policy implementation are often not given significant coverage. Our key contribution is a methodology of using automated analysis of mass media data to reveal the factors that might be shaping the political economy behind policy making.

Corporate executives who are linked to politicians or administrative officials, or family members of public officials with links to corporate organizations, are known to build an interlocking social network that becomes a power structure of highly influential entities. Such power structures often lead to an inequitable distribution of resources and manipulation of policies. A deeper look at this power structure and the constituent interlocks can provide users with valuable insights on these influential connections, and eventually, on the shaping of socio-economic outcomes by the interlocked political economy. In this paper, we describe the design of a platform to empirically monitor the degree of corporate-government interlock in India over time, by making use of publicly available data on the web. We find that the interlock has strengthened over the last decade, and we report the kind of interconnections and structural changes that have happened during this time. We also describe the design of an application to present news articles about an event or topic alongside the interconnection network of entities referred to in the news articles, to help users get a quick view of the main actors involved in the event. We find that this news search application is able to highlight several interconnections between prominent entities in an event, which had not been reported in the media. Overall, we find it relevant to build a technology platform which can help researchers and journalists to monitor the extent of interlocks between powerful stakeholders in the corporate and government spheres.

Politicians, politically connected business persons, bureaucrats, celebrities, and highly placed government officials (collectively termed as the power elite in sociology literature) can influence national and regional policy for personal and organizational benefit, which may not always be in the best interests of the people. Media is a crucial tool to shape public opinion, and is used heavily by the power elite to bring legitimacy to their policy decisions. In this paper, we empirically analyze the coverage given to the power elite in mainstream media on Demonetization, a significant, recent policy event in India. We compare the extent of coverage given to the elite and non-elite, the policy slant expressed by them, and differences in coverage between seven of the largest news media organizations in India. We find that among the power elite, powerful politicians and political parties are given the maximum coverage in the media, with conspicuous negligence in coverage given to expert opinions. Sentiment analysis clearly reveals that opposing political factions express opposing views towards the policy, and there is variation across different news sources as well. We are applying our methods on other contentious policy events to be able to do a more systematic analysis of how media can aid the power elite to shape public opinion by giving them disproportionately large coverage and visibility.

Social welfare schemes and public infrastructure services often face implementation and last mile delivery challenges. Governments in India have set up phone helplines for beneficiaries to report irregularities but we find that it remains challenging for beneficiaries to realise positive outcomes through these helplines; without a personal follow-up with the concerned local government officials by experienced members from the civil society, the redressals done are often inadequate. To combine the scalability of centralised helplines with the effectiveness of decentralised mediation by civil society groups, we set up an IVR (Interactive Voice Response) system where beneficiaries can record their grievances, which are then delegated to a network of civil society volunteers who do the required local follow-ups for resolution of these grievances. Through this pilot, we analysed over 200 cases of redressal and document how volunteer networks operate and can plug accountability gaps in government services. We advocate for formalisation of this civil-society mediated and technology assisted model into the design of public welfare schemes to enable citizens to engage with government departments in productive ways.

It is well known that technology based interventions targeted towards development need to look beyond technology to be successful - cultural nuances, capability and intentions of the people, institutional linkages, financial sustainability, etc all need to fall in place to build a robust development program. Supporting this argument, we describe an interactive voice-based community media platform in rural India that works through mobile phones, and how several non-technological innovations in content management, community mobilization, and social impact processes were brought about to make the program an effective social development intervention. We believe that the challenges and insights described in this paper, which span over the last three years of functioning of the platform, will be useful for other researchers and practitioners involved in both mobile and non-mobile based community media projects around the world.

Citizen feedback on the implementation of social welfare schemes can help fine tune their design, understand problems, and assess the benefits and impact from these schemes. Such feedback loops however are singularly missing in most schemes in India, and are conveyed only indirectly via civil society and social audit organisations that try to serve as a bridge between citizens and the government. We leverage the deep penetration of mobile phones in India to design a suite of IVR (Interactive Voice Response) tools that can help capture community perceptions, improve awareness of the people, and verify official records directly by the beneficiaries themselves. In the context of a rural employment guarantee scheme, we evaluate these tools in a few villages in the state of Haryana and demonstrate that there is good scope for using IVR tools to serve as a citizen engagement channel for welfare schemes. Our contribution lies in outlining several use-cases for technology interventions, and uncovering nuances that should be addressed if such systems are integrated into the implementation of social welfare schemes.

With Online Social Networking (OSN) services coming to prominence as a major platform for the popularization of Web content, recent research has focused on using information gleaned from these services to improve the performance of Content Distribution Networks (CDN). In this paper we find that contrary to popular belief, the simple cache replacement policy LRU outperforms methods based on OSN input. To establish this we propose an OSN-assisted caching strategy which we evaluate using a large Twitter dataset and through simulation. Our results show that vanilla LRU mostly outperforms the OSN-aided mechanisms. We study the reasons for this and find that if at any time there is a large enough percentage of highly popular content, which becomes popular within a short enough time span, CDN can do well by just following an LRU principle. While we do not claim generalizability to other workloads, or other caching architectures on the Internet, our results are important as they apply to the common use-case and show that simple algorithms do quite well.

We present the first comprehensive characterization of the diffusion of ideas on Twitter, studying more than 5.96 million topics that include both popular and less popular topics. On a data set containing approximately 10 million users and a comprehensive scraping of 196 million tweets, we perform a rigorous temporal and spatial analysis, investigating the time-evolving properties of the subgraphs formed by the users discussing each topic. We focus on two different notions of the spatial: the network topology formed by follower-following links on Twitter, and the geospatial location of the users. We investigate the effect of initiators on the popularity of topics and find that users with a high number of followers have a strong impact on topic popularity. We deduce that topics become popular when disjoint clusters of users discussing them begin to merge and form one giant component that grows to cover a significant fraction of the network. Our geospatial analysis shows that highly popular topics are those that cross regional boundaries aggressively.

Activist groups have taken up information dissemination and feedback collection as a means of rights advocacy in India. However, it is not easy given the difficulty in procuring and disseminating information at a large scale. Beneficiaries are often not able to help themselves as information systems are administration facing, because of poor literacy and the inability to access the Internet. Further, beneficiaries are not well informed of their rights and entitlements under different government schemes to know how and when to file grievances. We aim to solve these problems by designing and testing prototypes for information dissemination and feedback collection in various contexts. In our current prototype we describe an automated tool that sifts through the data on an MIS and conveys personalised information to the beneficiaries through voice calls. This is a work in progress, and our first exercise on providing MIS-extracted information to people through phone calls led to 70% of the beneficiaries who noticed a discrepancy in the data to agree to file a grievance on their behalf. We are continuing to scale the work, make it more automated, and run qualitative interviews with all stakeholders to understand causality linkages with transparency led grievance filing, assisted by appropriate ICTs, to increase accountability.

Community Radio (CR) stations are short range radio stations that serve the local media needs of their surrounding communities. Community participation by way of helping set the station agenda, airing of people's voices, and providing them with a local communication medium, is the defining feature of CR. But this philosophy has been hard to execute in practice because of logistical difficulties, with station staff not being able to reach out to a listenership-base spread across several hundreds of square kilometers. In today's context though, the high penetration of mobile phones has made it easier for listeners to participate in the running of radio stations, but the potential of telephony and radio integration has been exploited only minimally.

Rural Internet kiosks in developing countries can cost-effectively provide communication and e-governance services to the poorest sections of society. Unfortunately, a variety of technical and non-technical issues have caused most kiosk deployments to be unsustainable [1]. KioskNet addresses the key technical problems underlying kiosk failure by using robust ‘mechanical backhaul’ for connectivity [2], and by using low-cost and reliable kiosk controllers to support services delivered from one or more recycled PCs. KioskNet also addresses related issues such as security, user management, and log collection. In this paper, we describe the KioskNet system and outline its hardware, software, and security architectures. We describe a pilot deployment, and how we used lessons from this deployment to re-design our initial prototype.

Rural kiosks in developing countries provide a variety of services such as birth, marriage, and death certificates, electricity bill collection, land records, email services, and consulting on medical and agricultural problems. Fundamental to a kiosk's operation is its connection to the Internet. Network connectivity today is primarily provided by dialup telephone, although Very Small Aperture Terminals (VSAT) or long-distance wireless links are also being deployed. These solutions tend to be both expensive and failure prone. Instead, we propose the use of buses and cars as "mechanical backhaul" devices to carry data to and from a village and an internet gateway. Building on the pioneering lead of Daknet [15], and extending the Delay Tolerant Networking Research Group architecture [24], we describe a comprehensive solution, encompassing naming, addressing, forwarding, routing, identity management, application support, and security. We believe that this architecture not only meets the top-level goals of low cost and robustness, but also exposes fundamental architectural principles necessary for any such design. We also describe our experiences in implementing a prototype of this architecture.

No abstract available.