Anirban Sen, Priya Chhillar, Pooja Aggarwal, Sravan Verma, Debanjan Ghatak, P. Kumari, Manpreet Singh Agandh, Aditya Guru, Aaditeshwar Seth
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.
{"title":"An attempt at using mass media data to analyze the political economy around some key ICTD policies in India","authors":"Anirban Sen, Priya Chhillar, Pooja Aggarwal, Sravan Verma, Debanjan Ghatak, P. Kumari, Manpreet Singh Agandh, Aditya Guru, Aaditeshwar Seth","doi":"10.1145/3287098.3287108","DOIUrl":"https://doi.org/10.1145/3287098.3287108","url":null,"abstract":"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.","PeriodicalId":159525,"journal":{"name":"Proceedings of the Tenth International Conference on Information and Communication Technologies and Development","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-01-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130029775","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Fahad Idrees, Junaid Qadir, H. Mehmood, Saeed-Ul Hassan, Amna Batool
This paper describes the design process by which we designed an Android application equipped with audio, textual menus and visuals components for use by farmers of diverse literacy levels looking for vital weather information after the conclusion of research-work that productivity lags due to information inadequacies. The intervention provides more timely access to accurate information to low-literate farmers and thereby help in making the agricultural ecosystem more robust. We discuss the various design and implementation features of our system and presents our findings from the field on the usability of our application. We have also openly released our source code so that other users and developers can also benefit from our work.
{"title":"Urdu language based information dissemination system for low-literate farmers","authors":"Fahad Idrees, Junaid Qadir, H. Mehmood, Saeed-Ul Hassan, Amna Batool","doi":"10.1145/3287098.3287126","DOIUrl":"https://doi.org/10.1145/3287098.3287126","url":null,"abstract":"This paper describes the design process by which we designed an Android application equipped with audio, textual menus and visuals components for use by farmers of diverse literacy levels looking for vital weather information after the conclusion of research-work that productivity lags due to information inadequacies. The intervention provides more timely access to accurate information to low-literate farmers and thereby help in making the agricultural ecosystem more robust. We discuss the various design and implementation features of our system and presents our findings from the field on the usability of our application. We have also openly released our source code so that other users and developers can also benefit from our work.","PeriodicalId":159525,"journal":{"name":"Proceedings of the Tenth International Conference on Information and Communication Technologies and Development","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-01-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114430627","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Based on preliminary work with humanitarian organizations working with migrants in the US, we propose a set of Privacy Guidelines for Humanitarian Information Activities (HIA), in the context of undocumented migration. We discuss both technology and human risks in HIA, the limitations of privacy self-management, and the need for clear guidelines for HIA, such as the ones we tentatively suggest here.
{"title":"Privacy and security guidelines for humanitarian work with undocumented migrants","authors":"Sara Vannini, R. Gómez, B. Newell","doi":"10.1145/3287098.3287120","DOIUrl":"https://doi.org/10.1145/3287098.3287120","url":null,"abstract":"Based on preliminary work with humanitarian organizations working with migrants in the US, we propose a set of Privacy Guidelines for Humanitarian Information Activities (HIA), in the context of undocumented migration. We discuss both technology and human risks in HIA, the limitations of privacy self-management, and the need for clear guidelines for HIA, such as the ones we tentatively suggest here.","PeriodicalId":159525,"journal":{"name":"Proceedings of the Tenth International Conference on Information and Communication Technologies and Development","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-01-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122850424","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Knowledge of cell tower locations enables multiple applications including identifying unserved or poorly served regions. We consider the problem of estimating the locations of cell towers using crowdsourced measurements, which is challenging due to the uncontrolled nature of the sample collection process. Using large-scale crowdsourced datasets from OpenCelliD with ground-truth cell tower locations, we find that none of the several commonly used localization algorithms (e.g., Weighted Centroid) nor the state of the art Filtered Weighted Centroid (FWC) approach that filters out less predictive measurements manage to deliver robust localization performance. We propose a novel supervised machine learning based approach termed as Adaptive Algorithm Selection (AAS) that adaptively selects the localization algorithm likely to provide the most accurate localization performance for a given cell and its crowdsourced samples. We show that AAS not only significantly outperforms the state-of-the-art FWC approach, with median error improvement over 65%, but also achieves localization performance within 20% of an idealized Oracle solution. We validate the applicability of AAS in new and different settings (including WLAN AP localization) before presenting case studies in three different African countries that demonstrate the use of AAS based cell tower localization to reliably infer mobile infrastructure in developing countries.
{"title":"Uncovering mobile infrastructure in developing countries with crowdsourced measurements","authors":"Mah-Rukh Fida, M. Marina","doi":"10.1145/3287098.3287113","DOIUrl":"https://doi.org/10.1145/3287098.3287113","url":null,"abstract":"Knowledge of cell tower locations enables multiple applications including identifying unserved or poorly served regions. We consider the problem of estimating the locations of cell towers using crowdsourced measurements, which is challenging due to the uncontrolled nature of the sample collection process. Using large-scale crowdsourced datasets from OpenCelliD with ground-truth cell tower locations, we find that none of the several commonly used localization algorithms (e.g., Weighted Centroid) nor the state of the art Filtered Weighted Centroid (FWC) approach that filters out less predictive measurements manage to deliver robust localization performance. We propose a novel supervised machine learning based approach termed as Adaptive Algorithm Selection (AAS) that adaptively selects the localization algorithm likely to provide the most accurate localization performance for a given cell and its crowdsourced samples. We show that AAS not only significantly outperforms the state-of-the-art FWC approach, with median error improvement over 65%, but also achieves localization performance within 20% of an idealized Oracle solution. We validate the applicability of AAS in new and different settings (including WLAN AP localization) before presenting case studies in three different African countries that demonstrate the use of AAS based cell tower localization to reliably infer mobile infrastructure in developing countries.","PeriodicalId":159525,"journal":{"name":"Proceedings of the Tenth International Conference on Information and Communication Technologies and Development","volume":"180 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-01-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129941807","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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.
{"title":"Experiences from a mobile-based behaviour change campaign on maternal and child nutrition in rural India","authors":"D. Chakraborty, Akshay Gupta, Aaditeshwar Seth","doi":"10.1145/3287098.3287110","DOIUrl":"https://doi.org/10.1145/3287098.3287110","url":null,"abstract":"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.","PeriodicalId":159525,"journal":{"name":"Proceedings of the Tenth International Conference on Information and Communication Technologies and Development","volume":"302 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-01-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116882722","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Wei Ma, Kendall Nowocin, Niraj Marathe, George H. Chen
Small and marginal farmers, who account for over 80% of India's agricultural population, often sell their harvest at low, unfavorable prices before spoilage. These farmers often lack access to either cold storage or market forecasts. In particular, by having access to cold storage, farmers can store their produce for longer and thus have more flexibility as to when they should sell their harvest by. Meanwhile, by having access to market forecasts, farmers can more easily identify which markets to sell at and when. While affordable cold storage solutions have become more widely available, there has been less work on produce price forecasting. A key challenge is that in many regions of India, predominantly in rural and remote areas, we have either very limited or no produce pricing data available from public online sources. In this paper, we present a produce price forecasting system that pulls data from the Indian Ministry of Agriculture and Farmers Welfare's website Agmarknet, trains a model of prices using over a thousand markets, and displays interpretable price forecasts in a web application viewable from a mobile phone. Due to the pricing data being extremely sparse, our method first imputes missing entries using collaborative filtering to obtain a dense dataset. Using this imputed dense dataset, we then train a decision-tree-based classifier to predict whether the price for a specific produce at a specific market will go up, stay the same, or go down. In terms of interpretability, we display the most relevant historical pricing data that drive each forecasted price trend, where we take advantage of the fact that a wide family of decision-tree-based ensemble learning methods are adaptive nearest neighbor methods. We also show how our approach generalizes to forecasting exact produce prices and constructing heuristic price uncertainty intervals. We validate forecast accuracy on data from Agmarknet and a small field survey of a few markets in Odisha.
{"title":"An interpretable produce price forecasting system for small and marginal farmers in India using collaborative filtering and adaptive nearest neighbors","authors":"Wei Ma, Kendall Nowocin, Niraj Marathe, George H. Chen","doi":"10.1145/3287098.3287100","DOIUrl":"https://doi.org/10.1145/3287098.3287100","url":null,"abstract":"Small and marginal farmers, who account for over 80% of India's agricultural population, often sell their harvest at low, unfavorable prices before spoilage. These farmers often lack access to either cold storage or market forecasts. In particular, by having access to cold storage, farmers can store their produce for longer and thus have more flexibility as to when they should sell their harvest by. Meanwhile, by having access to market forecasts, farmers can more easily identify which markets to sell at and when. While affordable cold storage solutions have become more widely available, there has been less work on produce price forecasting. A key challenge is that in many regions of India, predominantly in rural and remote areas, we have either very limited or no produce pricing data available from public online sources. In this paper, we present a produce price forecasting system that pulls data from the Indian Ministry of Agriculture and Farmers Welfare's website Agmarknet, trains a model of prices using over a thousand markets, and displays interpretable price forecasts in a web application viewable from a mobile phone. Due to the pricing data being extremely sparse, our method first imputes missing entries using collaborative filtering to obtain a dense dataset. Using this imputed dense dataset, we then train a decision-tree-based classifier to predict whether the price for a specific produce at a specific market will go up, stay the same, or go down. In terms of interpretability, we display the most relevant historical pricing data that drive each forecasted price trend, where we take advantage of the fact that a wide family of decision-tree-based ensemble learning methods are adaptive nearest neighbor methods. We also show how our approach generalizes to forecasting exact produce prices and constructing heuristic price uncertainty intervals. We validate forecast accuracy on data from Agmarknet and a small field survey of a few markets in Odisha.","PeriodicalId":159525,"journal":{"name":"Proceedings of the Tenth International Conference on Information and Communication Technologies and Development","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114416840","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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