Omais Shafi, S. Chauhan, Gayathri Ananthanarayanan, Rijurekha Sen
Road traffic congestion increases vehicular emissions and air pollution. Traffic rule violation causes road accidents. Both pollution and accidents take tremendous social and economic toll worldwide, and more so in developing countries where the skewed vehicle to road infrastructure ratio amplifies the problems. Automating traffic intersection management to detect and penalize traffic rule violations and reduce traffic congestion, is the focus of this paper, using state-of-the-art Convolutional Neural Network (CNN) on traffic camera feeds. There are however non-trivial challenges in handling the chaotic, non-laned traffic scenes in developing countries. Maintaining high throughput is one of the challenges, as broadband connectivity to remote GPU servers is absent in developing countries, and embedded GPU platforms on roads need to be low cost due to budget constraints. Additionally, ambient temperatures in developing country cities can go to 45-50 degree Celsius in summer, where continuous embedded processing can lead to lower lifetimes of the embedded platforms. In this paper, we present DynCNN, an application dynamism and ambient temperature aware controller for Neural Network concurrency. DynCNN effectively uses processor heterogeneity to control the number of threads and frequencies on the accelerator to manage application utility under strict thermal and power thresholds. We evaluate the efficiency of DynCNN on three different commercially available embedded GPUs (Jetson TX2TM, Xavier NXTM and Xavier AGXTM) using a real traffic intersection’s 40 days’ dataset. Experimental results show that in comparison to all existing state-of-the art- GPU governors for two different CPU settings, DynCNN reduces the average temperature and power by ~12°C and 68.82% respectively for one CPU setting (Baseline1) and similarly, it improves the performance by around 31.2% compared to the other CPU setting (Baseline2).
{"title":"DynCNN: Application Dynamism and Ambient Temperature Aware Neural Network Scheduler in Edge Devices for Traffic Control","authors":"Omais Shafi, S. Chauhan, Gayathri Ananthanarayanan, Rijurekha Sen","doi":"10.1145/3530190.3534823","DOIUrl":"https://doi.org/10.1145/3530190.3534823","url":null,"abstract":"Road traffic congestion increases vehicular emissions and air pollution. Traffic rule violation causes road accidents. Both pollution and accidents take tremendous social and economic toll worldwide, and more so in developing countries where the skewed vehicle to road infrastructure ratio amplifies the problems. Automating traffic intersection management to detect and penalize traffic rule violations and reduce traffic congestion, is the focus of this paper, using state-of-the-art Convolutional Neural Network (CNN) on traffic camera feeds. There are however non-trivial challenges in handling the chaotic, non-laned traffic scenes in developing countries. Maintaining high throughput is one of the challenges, as broadband connectivity to remote GPU servers is absent in developing countries, and embedded GPU platforms on roads need to be low cost due to budget constraints. Additionally, ambient temperatures in developing country cities can go to 45-50 degree Celsius in summer, where continuous embedded processing can lead to lower lifetimes of the embedded platforms. In this paper, we present DynCNN, an application dynamism and ambient temperature aware controller for Neural Network concurrency. DynCNN effectively uses processor heterogeneity to control the number of threads and frequencies on the accelerator to manage application utility under strict thermal and power thresholds. We evaluate the efficiency of DynCNN on three different commercially available embedded GPUs (Jetson TX2TM, Xavier NXTM and Xavier AGXTM) using a real traffic intersection’s 40 days’ dataset. Experimental results show that in comparison to all existing state-of-the art- GPU governors for two different CPU settings, DynCNN reduces the average temperature and power by ~12°C and 68.82% respectively for one CPU setting (Baseline1) and similarly, it improves the performance by around 31.2% compared to the other CPU setting (Baseline2).","PeriodicalId":257424,"journal":{"name":"ACM SIGCAS/SIGCHI Conference on Computing and Sustainable Societies (COMPASS)","volume":"15 5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123658452","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}
Morva Saaty, Jaitun V. Patel, Norhan Abdelgawad, J. Marion, D. McCrickard, Shalini Misra, K. Wernstedt
Thru-hiking the Appalachian Trail (AT) is an adventure of a lifetime that necessitates long-term planning and knowledge of challenges and practices in the outdoors. One important but oft-ignored step is to establish awareness about sustainable practices captured in Leave No Trace (LNT) principles for minimizing the impact on the trail. This paper seeks to understand practices of hikers with regards to trail sustainability and LNT. Since hikers often leverage virtual communities on social media for asking questions and sharing resources, we analyzed Reddit top-level comments on /r/AppalachianTrail to understand AT hiking discussions and explore their connections with sustainable practices in the outdoors. The findings will inform AT stakeholders and researchers in the field about the hikers’ practices and the role of social media platforms in supporting sustainable trail management.
{"title":"Note: Studying Sustainable Practices of Appalachian Trail Community based on Reddit Topic Modelling Analysis","authors":"Morva Saaty, Jaitun V. Patel, Norhan Abdelgawad, J. Marion, D. McCrickard, Shalini Misra, K. Wernstedt","doi":"10.1145/3530190.3534848","DOIUrl":"https://doi.org/10.1145/3530190.3534848","url":null,"abstract":"Thru-hiking the Appalachian Trail (AT) is an adventure of a lifetime that necessitates long-term planning and knowledge of challenges and practices in the outdoors. One important but oft-ignored step is to establish awareness about sustainable practices captured in Leave No Trace (LNT) principles for minimizing the impact on the trail. This paper seeks to understand practices of hikers with regards to trail sustainability and LNT. Since hikers often leverage virtual communities on social media for asking questions and sharing resources, we analyzed Reddit top-level comments on /r/AppalachianTrail to understand AT hiking discussions and explore their connections with sustainable practices in the outdoors. The findings will inform AT stakeholders and researchers in the field about the hikers’ practices and the role of social media platforms in supporting sustainable trail management.","PeriodicalId":257424,"journal":{"name":"ACM SIGCAS/SIGCHI Conference on Computing and Sustainable Societies (COMPASS)","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121339814","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}
Food waste and food insecurity are prevalent challenges on college campuses. Studies show that approximately 30-40% of students are food insecure while over 22 million pounds of food on college campuses is wasted. Food waste also contributes to global warming where methane is produced in landfills and has a higher global warming potential than carbon dioxide. Several technical solutions have been proposed to reduce both food waste and food insecurity, however, there has been less focus on solving this challenge within college campuses. In this work, we present Campus Plate, a platform that allows members of the campus community to quickly identify and retrieve excess food from dining services and campus events. We describe the technical implementation and the people and partnerships that were established to ensure Campus Plate was successfully implemented on our campus where in a short period, over one thousand food items have been retrieved. Through Campus Plate, we are able to reduce food waste and food insecurity and contribute to a more sustainable college campus.
{"title":"Note: Campus Plate: Reducing Food Waste and Food Insecurity on College Campuses using Smartphones","authors":"Brian Krupp, Franklin Lebo","doi":"10.1145/3530190.3534840","DOIUrl":"https://doi.org/10.1145/3530190.3534840","url":null,"abstract":"Food waste and food insecurity are prevalent challenges on college campuses. Studies show that approximately 30-40% of students are food insecure while over 22 million pounds of food on college campuses is wasted. Food waste also contributes to global warming where methane is produced in landfills and has a higher global warming potential than carbon dioxide. Several technical solutions have been proposed to reduce both food waste and food insecurity, however, there has been less focus on solving this challenge within college campuses. In this work, we present Campus Plate, a platform that allows members of the campus community to quickly identify and retrieve excess food from dining services and campus events. We describe the technical implementation and the people and partnerships that were established to ensure Campus Plate was successfully implemented on our campus where in a short period, over one thousand food items have been retrieved. Through Campus Plate, we are able to reduce food waste and food insecurity and contribute to a more sustainable college campus.","PeriodicalId":257424,"journal":{"name":"ACM SIGCAS/SIGCHI Conference on Computing and Sustainable Societies (COMPASS)","volume":"116 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134240146","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}
Yasaman Rohanifar, S. Sultana, Swapnil Nandy, Pratyasha Saha, Md. Jonayed Hossain Chowdhury, M. N. Al-Ameen, Syed Ishtiaque Ahmed
This work addresses the challenges associated with cashless transactions and Mobile Financial Services (MFS) in the Global South. In our 19-months long interview study in Dhaka, Bangladesh, we engaged with 38 participants, including everyday users, bank employees, and policymakers, and investigated their experiences and perspectives associated with financial services. Our findings reveal a wide range of factors, naming intermediaries, terrorist assemblage, and re-skilling the existing employees that impede the mass adoption of cashless transaction services in Bangladesh. The findings from this study contribute to the ongoing discourse on the challenges and opportunities offered by the digitization of financial systems in Bangladesh. Our recommendations aim to improve the integration of the cashless systems within the societal context of Bangladesh and, more broadly, the Global South.
{"title":"The Role of Intermediaries, Terrorist Assemblage, and Re-skilling in the Adoption of Cashless Transaction Systems in Bangladesh","authors":"Yasaman Rohanifar, S. Sultana, Swapnil Nandy, Pratyasha Saha, Md. Jonayed Hossain Chowdhury, M. N. Al-Ameen, Syed Ishtiaque Ahmed","doi":"10.1145/3530190.3534810","DOIUrl":"https://doi.org/10.1145/3530190.3534810","url":null,"abstract":"This work addresses the challenges associated with cashless transactions and Mobile Financial Services (MFS) in the Global South. In our 19-months long interview study in Dhaka, Bangladesh, we engaged with 38 participants, including everyday users, bank employees, and policymakers, and investigated their experiences and perspectives associated with financial services. Our findings reveal a wide range of factors, naming intermediaries, terrorist assemblage, and re-skilling the existing employees that impede the mass adoption of cashless transaction services in Bangladesh. The findings from this study contribute to the ongoing discourse on the challenges and opportunities offered by the digitization of financial systems in Bangladesh. Our recommendations aim to improve the integration of the cashless systems within the societal context of Bangladesh and, more broadly, the Global South.","PeriodicalId":257424,"journal":{"name":"ACM SIGCAS/SIGCHI Conference on Computing and Sustainable Societies (COMPASS)","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132717384","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}
Millend Roy, A. Nambi, Anupam Sobti, T. Ganu, S. Kalyanaraman, S. Akella, Jaya Subha Devi, S. Sundaresan
Accurately predicting the energy consumption of an electric vehicle (EV) under real-world circumstances (such as varying road, traffic, weather conditions, etc.) is critical for a number of decisions like range estimation and route planning. A major concern for electric vehicle owners is the uncertain nature of the battery consumption. This results in the “range anxiety” and reluctance from users for mass adoption of EVs, since they are concerned about untimely drainage of battery. Even at the organizational level, a company running a fleet of electric vehicles must understand the battery consumption profiles accurately for tasks such as route and driver planning, battery sizing, maintenance planning, etc. In this paper, firstly, we highlight the challenges in modelling energy consumption and demonstrate the nature of data which is required to understand the energy consumption of electric vehicles under real-world conditions. Then, through a large and diverse dataset collected over 23,500 hours spanning ≈ 460,000 km with 27 vehicles, we demonstrate our two-stage approach to predict the energy consumption of an EV before the start of the trip. In our energy consumption modelling approach, apart from the primary features recorded directly before the trip, we also construct and predict secondary features through an extensive feature engineering process, both of which are then used to predict the energy consumption. We show that our approach outperforms Deep Learning based modelling for EV energy consumption prediction, and also provides explainable and interpretable models for domain experts. This novel method results in energy consumption modelling with of Mean Absolute Percentage Error (MAPE) on our dataset and significantly outperforms state-of-the-art results in EV energy consumption modeling.
{"title":"Reliable Energy Consumption Modeling for an Electric Vehicle Fleet","authors":"Millend Roy, A. Nambi, Anupam Sobti, T. Ganu, S. Kalyanaraman, S. Akella, Jaya Subha Devi, S. Sundaresan","doi":"10.1145/3530190.3534803","DOIUrl":"https://doi.org/10.1145/3530190.3534803","url":null,"abstract":"Accurately predicting the energy consumption of an electric vehicle (EV) under real-world circumstances (such as varying road, traffic, weather conditions, etc.) is critical for a number of decisions like range estimation and route planning. A major concern for electric vehicle owners is the uncertain nature of the battery consumption. This results in the “range anxiety” and reluctance from users for mass adoption of EVs, since they are concerned about untimely drainage of battery. Even at the organizational level, a company running a fleet of electric vehicles must understand the battery consumption profiles accurately for tasks such as route and driver planning, battery sizing, maintenance planning, etc. In this paper, firstly, we highlight the challenges in modelling energy consumption and demonstrate the nature of data which is required to understand the energy consumption of electric vehicles under real-world conditions. Then, through a large and diverse dataset collected over 23,500 hours spanning ≈ 460,000 km with 27 vehicles, we demonstrate our two-stage approach to predict the energy consumption of an EV before the start of the trip. In our energy consumption modelling approach, apart from the primary features recorded directly before the trip, we also construct and predict secondary features through an extensive feature engineering process, both of which are then used to predict the energy consumption. We show that our approach outperforms Deep Learning based modelling for EV energy consumption prediction, and also provides explainable and interpretable models for domain experts. This novel method results in energy consumption modelling with of Mean Absolute Percentage Error (MAPE) on our dataset and significantly outperforms state-of-the-art results in EV energy consumption modeling.","PeriodicalId":257424,"journal":{"name":"ACM SIGCAS/SIGCHI Conference on Computing and Sustainable Societies (COMPASS)","volume":"127 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133392851","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}
S. M. Hasan, Alabi Mehzabin Anisha, Rudaiba Adnin, Ishrat Jahan Eliza, Ishika Tarin, Sadia Afroz, A. Islam
The recent Covid-19 pandemic elucidates the need for a better disease outbreak analysis and surveillance system, which can harness state-of-the-art data mining and machine learning techniques to produce better forecasting. In this regard, understanding the correlation between disease outbreaks and socioeconomic factors should pave the way for such systems by providing useful indicators, which are yet to be explored in the literature to the best of our knowledge. Therefore, in this study, we accumulated data on 72 infectious diseases and their outbreaks all over the globe over a period of 23 years as well as corresponding different socioeconomic data. We, then, performed point-biserial and spearman correlation analysis over the collected data. Our analysis of the obtained correlations demonstrates that various disease outbreak attributes are positively and negatively correlated with different socioeconomic indicators. For example, indicators such as lifetime risk of maternal death, adolescent fertility rate, etc., are positively correlated, while indicators such as life expectancy at birth, measles immunization, etc., are negatively correlated, with disease outbreaks that affect the digestive organ system. In this paper, we find and summarize the correlations between 126 outbreak attributes derived from the characteristics of the 72 diseases in consideration and 192 socioeconomic factors which is a novel contribution to the field of disease outbreak analysis and prediction.
{"title":"Revealing Influences of Socioeconomic Factors over Disease Outbreaks","authors":"S. M. Hasan, Alabi Mehzabin Anisha, Rudaiba Adnin, Ishrat Jahan Eliza, Ishika Tarin, Sadia Afroz, A. Islam","doi":"10.1145/3530190.3534804","DOIUrl":"https://doi.org/10.1145/3530190.3534804","url":null,"abstract":"The recent Covid-19 pandemic elucidates the need for a better disease outbreak analysis and surveillance system, which can harness state-of-the-art data mining and machine learning techniques to produce better forecasting. In this regard, understanding the correlation between disease outbreaks and socioeconomic factors should pave the way for such systems by providing useful indicators, which are yet to be explored in the literature to the best of our knowledge. Therefore, in this study, we accumulated data on 72 infectious diseases and their outbreaks all over the globe over a period of 23 years as well as corresponding different socioeconomic data. We, then, performed point-biserial and spearman correlation analysis over the collected data. Our analysis of the obtained correlations demonstrates that various disease outbreak attributes are positively and negatively correlated with different socioeconomic indicators. For example, indicators such as lifetime risk of maternal death, adolescent fertility rate, etc., are positively correlated, while indicators such as life expectancy at birth, measles immunization, etc., are negatively correlated, with disease outbreaks that affect the digestive organ system. In this paper, we find and summarize the correlations between 126 outbreak attributes derived from the characteristics of the 72 diseases in consideration and 192 socioeconomic factors which is a novel contribution to the field of disease outbreak analysis and prediction.","PeriodicalId":257424,"journal":{"name":"ACM SIGCAS/SIGCHI Conference on Computing and Sustainable Societies (COMPASS)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122561243","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}
D. Pant, Dibyendu Talukder, D. Kumar, Rachit Pandey, Aaditeshwar Seth, Chetan Arora
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.
{"title":"Use of Metric Learning for the Recognition of Handwritten Digits, and its Application to Increase the Outreach of Voice-based Communication Platforms","authors":"D. Pant, Dibyendu Talukder, D. Kumar, Rachit Pandey, Aaditeshwar Seth, Chetan Arora","doi":"10.1145/3530190.3534795","DOIUrl":"https://doi.org/10.1145/3530190.3534795","url":null,"abstract":"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.","PeriodicalId":257424,"journal":{"name":"ACM SIGCAS/SIGCHI Conference on Computing and Sustainable Societies (COMPASS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130516079","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}
Khurram Yamin, Matthew Oswald, Nima Jadali, Yao Xie, E. Zegura, D. Nazzal
The 2020 election was fraught with allegations of fraud. To respond to a lack of a robust method to investigate these allegations, we propose a multi-step clustering based approach. We first solve a regression problem to find a group of influential variables, then cluster on these variables to get a set of precincts that should have similar election results. Re-clustering each cluster shows us the outliers. We then apply the approach to Fulton County, Georgia’s largest county and an epicenter of allegations of corruption and fraud. We show that the level of fraud detected is not significant and would not be enough to change the election results in Georgia. In fact, the majority of the precincts that showed to be anomalous were ones where Trump received more votes than was expected. We also validate our analysis through application to the 2015 Argentina National Election.
{"title":"An Unsupervised Density Based Clustering Algorithm to Detect Election Anomalies : Evidence from Georgia’s Largest County","authors":"Khurram Yamin, Matthew Oswald, Nima Jadali, Yao Xie, E. Zegura, D. Nazzal","doi":"10.1145/3530190.3534799","DOIUrl":"https://doi.org/10.1145/3530190.3534799","url":null,"abstract":"The 2020 election was fraught with allegations of fraud. To respond to a lack of a robust method to investigate these allegations, we propose a multi-step clustering based approach. We first solve a regression problem to find a group of influential variables, then cluster on these variables to get a set of precincts that should have similar election results. Re-clustering each cluster shows us the outliers. We then apply the approach to Fulton County, Georgia’s largest county and an epicenter of allegations of corruption and fraud. We show that the level of fraud detected is not significant and would not be enough to change the election results in Georgia. In fact, the majority of the precincts that showed to be anomalous were ones where Trump received more votes than was expected. We also validate our analysis through application to the 2015 Argentina National Election.","PeriodicalId":257424,"journal":{"name":"ACM SIGCAS/SIGCHI Conference on Computing and Sustainable Societies (COMPASS)","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127932730","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}
Anirban Sen, Debanjan Ghatak, Gurjeet Khanuja, K. Rekha, Mehak Gupta, Sanket Dhakate, Kartikeya Sharma, Aaditeshwar Seth
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.
{"title":"Analysis of Media Bias in Policy Discourse in India","authors":"Anirban Sen, Debanjan Ghatak, Gurjeet Khanuja, K. Rekha, Mehak Gupta, Sanket Dhakate, Kartikeya Sharma, Aaditeshwar Seth","doi":"10.1145/3530190.3534798","DOIUrl":"https://doi.org/10.1145/3530190.3534798","url":null,"abstract":"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.","PeriodicalId":257424,"journal":{"name":"ACM SIGCAS/SIGCHI Conference on Computing and Sustainable Societies (COMPASS)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123444753","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}
Data is an inseparable part of community management. Data openness and transparency has been a driver for change in government accountability and public engagement by providing unprecedented access to information. More prominently, there exists enthusiasm about the possibilities created by new and more extensive sources of data to improve our understanding and management of communities. This work examines a case study in collecting and operationalizing sustainable open data and specifically open government or civic data - information, public or otherwise, which anyone is free to access, analyze and re-use for any purpose - through a platform and community organizing effort in crowdsourcing open pedestrian network data. We outline a number of tensions or challenges in opening data, specifically in a number of realms where public interest stands to benefit from uses of the data, yet no single commercial or governmental entity is either liable or has a clear monetary interest associated with freely opening that data. In these specific cases, collection of these open data becomes a community-based challenge to undertake, which raises a number of additional socio-technical, political, and data provenance considerations. Beyond the technical contributions of our framework (in the open-source tools to support community activities, our case study contributes a number of insights and recommendations regarding community engagement, use of participatory co-design jointly with data collection tools, and planning for sustainable data stewardship in the involved communities.
{"title":"Towards operationalizing the communal production and management of public (open) data: a pedestrian network case study: A pedestrian network case study in operationalizing communal open data","authors":"N. Bolten, A. Caspi","doi":"10.1145/3530190.3534821","DOIUrl":"https://doi.org/10.1145/3530190.3534821","url":null,"abstract":"Data is an inseparable part of community management. Data openness and transparency has been a driver for change in government accountability and public engagement by providing unprecedented access to information. More prominently, there exists enthusiasm about the possibilities created by new and more extensive sources of data to improve our understanding and management of communities. This work examines a case study in collecting and operationalizing sustainable open data and specifically open government or civic data - information, public or otherwise, which anyone is free to access, analyze and re-use for any purpose - through a platform and community organizing effort in crowdsourcing open pedestrian network data. We outline a number of tensions or challenges in opening data, specifically in a number of realms where public interest stands to benefit from uses of the data, yet no single commercial or governmental entity is either liable or has a clear monetary interest associated with freely opening that data. In these specific cases, collection of these open data becomes a community-based challenge to undertake, which raises a number of additional socio-technical, political, and data provenance considerations. Beyond the technical contributions of our framework (in the open-source tools to support community activities, our case study contributes a number of insights and recommendations regarding community engagement, use of participatory co-design jointly with data collection tools, and planning for sustainable data stewardship in the involved communities.","PeriodicalId":257424,"journal":{"name":"ACM SIGCAS/SIGCHI Conference on Computing and Sustainable Societies (COMPASS)","volume":"76 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117298843","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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