Weizhe Chen, Eshwar Prasad Sivaramakrishnan, B. Dilkina
Wildfires have increased in extent and severity, and are posing a growing threat to people’s well-being and the environment. Prescribed burns (burning on purpose parts of the landscape) are one of the key mitigation strategies available to reduce the potential damage of wildfires. However, where to conduct prescribed burns has long been a problem for domain experts. With the advancement of forest science, weather science, and computational modeling, there produced powerful fire simulators that can help inform how wildfires will start and grow. In this paper, we model the problem of selecting where to perform a set of prescribed burns across a large landscape into a multi-objective optimization problem. We build a surrogate objective function from simulation data and solve the multi-objective optimization problem with genetic algorithms. We name our solution as Spatial Multi-Objective for Prescribed Burn (SMO-PB). We also investigate three variants of the approach that further consider spatial fairness. With a case study of Dogrib, Canada, we show that our formulations can successfully provide solutions capable of real world deployment, and showed how fairness can be reached without diminishing the performance a lot.
{"title":"Landscape Optimization for Prescribed Burns in Wildfire Mitigation Planning","authors":"Weizhe Chen, Eshwar Prasad Sivaramakrishnan, B. Dilkina","doi":"10.1145/3530190.3534816","DOIUrl":"https://doi.org/10.1145/3530190.3534816","url":null,"abstract":"Wildfires have increased in extent and severity, and are posing a growing threat to people’s well-being and the environment. Prescribed burns (burning on purpose parts of the landscape) are one of the key mitigation strategies available to reduce the potential damage of wildfires. However, where to conduct prescribed burns has long been a problem for domain experts. With the advancement of forest science, weather science, and computational modeling, there produced powerful fire simulators that can help inform how wildfires will start and grow. In this paper, we model the problem of selecting where to perform a set of prescribed burns across a large landscape into a multi-objective optimization problem. We build a surrogate objective function from simulation data and solve the multi-objective optimization problem with genetic algorithms. We name our solution as Spatial Multi-Objective for Prescribed Burn (SMO-PB). We also investigate three variants of the approach that further consider spatial fairness. With a case study of Dogrib, Canada, we show that our formulations can successfully provide solutions capable of real world deployment, and showed how fairness can be reached without diminishing the performance a lot.","PeriodicalId":257424,"journal":{"name":"ACM SIGCAS/SIGCHI Conference on Computing and Sustainable Societies (COMPASS)","volume":"16 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":"115930203","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}
Beatriz Palacios Abad, E. Belding-Royer, Morgan Vigil-Hayes, E. Zegura
The Federal Communications Commission (FCC) has recently released official technical requirements for its Broadband Data Collection (BDC) processes, with the purpose of improving the accuracy of broadband coverage data in the United States. A key process in the BDC establishes the opportunity for communities to crowdsource Internet measurements that may dispute coverage data maintained by Internet service providers. This process outlines complex requirements that may provide a substantial barrier to community participation. In this poster we share the design of a network measurement tool suite and the requirements for a community coordination tool to support community-led efforts to challenge official reports. Our design is based on “counter-data action” principles, which call unethical and authoritative uses of data into question.
{"title":"Note: Towards Community-Empowered Network Data Action","authors":"Beatriz Palacios Abad, E. Belding-Royer, Morgan Vigil-Hayes, E. Zegura","doi":"10.1145/3530190.3534836","DOIUrl":"https://doi.org/10.1145/3530190.3534836","url":null,"abstract":"The Federal Communications Commission (FCC) has recently released official technical requirements for its Broadband Data Collection (BDC) processes, with the purpose of improving the accuracy of broadband coverage data in the United States. A key process in the BDC establishes the opportunity for communities to crowdsource Internet measurements that may dispute coverage data maintained by Internet service providers. This process outlines complex requirements that may provide a substantial barrier to community participation. In this poster we share the design of a network measurement tool suite and the requirements for a community coordination tool to support community-led efforts to challenge official reports. Our design is based on “counter-data action” principles, which call unethical and authoritative uses of data into question.","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":"115721370","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}
Anurag Aribandi, Divyanshu Agrawal, D. Chakraborty
Most online information sources are text-based and in Western Languages like English. However, many new and first time users of the Internet are in contexts with low English proficiency and are unable to access vital information online. Several researchers have focused on building conversational information systems over voice for this demographic, and also highlighted the importance of building trust towards the information source. In this work we develop four versions of a voice based chat-bot on the Google Assistant platform in which we vary the gender, friendliness and personalisation of the bot. We find that the users rank the female version of the bot with more personalisations over the others; however when rating the bots individually, the ratings depend on the ability of the bot to understand the users’ spoken query and respond accurately.
{"title":"Note: Evaluating Trust in the Context of Conversational Information Systems for new users of the Internet","authors":"Anurag Aribandi, Divyanshu Agrawal, D. Chakraborty","doi":"10.1145/3530190.3534852","DOIUrl":"https://doi.org/10.1145/3530190.3534852","url":null,"abstract":"Most online information sources are text-based and in Western Languages like English. However, many new and first time users of the Internet are in contexts with low English proficiency and are unable to access vital information online. Several researchers have focused on building conversational information systems over voice for this demographic, and also highlighted the importance of building trust towards the information source. In this work we develop four versions of a voice based chat-bot on the Google Assistant platform in which we vary the gender, friendliness and personalisation of the bot. We find that the users rank the female version of the bot with more personalisations over the others; however when rating the bots individually, the ratings depend on the ability of the bot to understand the users’ spoken query and respond accurately.","PeriodicalId":257424,"journal":{"name":"ACM SIGCAS/SIGCHI Conference on Computing and Sustainable Societies (COMPASS)","volume":"35 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":"132822459","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}
J. Mondal, M. Islam, Sarah Zabeen, A. Islam, Jannatun Noor
Agriculture is the fundamental source of revenue and Gross Domestic Product (GDP) in many countries where economically developing countries; especially the Global South are no exception. Various types of plant-based diseases are strongly intertwined with the everyday lives of those who are connected with agriculture. Among the diseases, most of them can be diagnosed by leaves. However, due to the variety of illnesses, identifying and classifying any plant leaf disease is difficult and time-consuming. Besides, late identifications of diseases cause losses for the farmers on a large scale, which in turn affects their financial state. Therefore, to overcome this problem, we present a lightweight approach (called PLeaD-Net) to accurately recognize and categorize plant leaf diseases in this paper. Here, leveraging a limited-resource deep convolutional network (Deep CNN) model, we extract information from sick sections of a leaf to accurately identify locations of disease. In comparison to existing deep learning methods and other prior research, our proposed approach achieves a much higher performance using fewer parameters as per our experimental results. In our study and experimentation, we develop and implement an architecture based on Deep CNN. We test our architecture on a publicly available dataset that contains different types of plant leaves images and backgrounds.
{"title":"Note: Plant Leaf Disease Network (PLeaD-Net): Identifying Plant Leaf Diseases through Leveraging Limited-Resource Deep Convolutional Neural Network","authors":"J. Mondal, M. Islam, Sarah Zabeen, A. Islam, Jannatun Noor","doi":"10.1145/3530190.3534844","DOIUrl":"https://doi.org/10.1145/3530190.3534844","url":null,"abstract":"Agriculture is the fundamental source of revenue and Gross Domestic Product (GDP) in many countries where economically developing countries; especially the Global South are no exception. Various types of plant-based diseases are strongly intertwined with the everyday lives of those who are connected with agriculture. Among the diseases, most of them can be diagnosed by leaves. However, due to the variety of illnesses, identifying and classifying any plant leaf disease is difficult and time-consuming. Besides, late identifications of diseases cause losses for the farmers on a large scale, which in turn affects their financial state. Therefore, to overcome this problem, we present a lightweight approach (called PLeaD-Net) to accurately recognize and categorize plant leaf diseases in this paper. Here, leveraging a limited-resource deep convolutional network (Deep CNN) model, we extract information from sick sections of a leaf to accurately identify locations of disease. In comparison to existing deep learning methods and other prior research, our proposed approach achieves a much higher performance using fewer parameters as per our experimental results. In our study and experimentation, we develop and implement an architecture based on Deep CNN. We test our architecture on a publicly available dataset that contains different types of plant leaves images and backgrounds.","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":"130564043","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}
Fariha Tasmin Jaigirdar, C. Rudolph, Rayhan Rashed, Nahiyan Uddin, Chris Bain, Alim Al Islam
Secure management of healthcare records in dynamic contexts requires an understanding of the overall infrastructure of record flows and poses more challenges for vulnerable environments such as amongst the Rohingya refugees in Bangladesh. Understanding the overall infrastructure of how health clinics are providing medical treatments and how they are collecting and storing patient records is crucial as any changes or mismanagement in these records enables misuse or deliberate misinterpretations of medical data on various levels amongst individuals and Rohingya communities. Through an extensive field study in the Rohingya refugee camps in Bangladesh, we explored the management of healthcare records in different organizations. Over the course of our fieldwork, we interviewed 22 medical service providers from nine healthcare organizations connected to the Rohingya camps. Based on our findings, we design an abstract record management model and analyze it using a data provenance approach to identify the limitations of the existing record management. Our study shows vulnerabilities in ID management and security practices in healthcare record management. We further illustrate potential exploitation of these vulnerabilities through political, financial, and social lenses. To the best of our knowledge, this study is the first to discuss vulnerabilities in Rohingya refugees’ medical record management from political, social and economic views.
{"title":"NOTE: Unavoidable Service to Unnoticeable Risks: A Study on How Healthcare Record Management Opens the Doors of Unnoticeable Vulnerabilities for Rohingya Refugees","authors":"Fariha Tasmin Jaigirdar, C. Rudolph, Rayhan Rashed, Nahiyan Uddin, Chris Bain, Alim Al Islam","doi":"10.1145/3530190.3534846","DOIUrl":"https://doi.org/10.1145/3530190.3534846","url":null,"abstract":"Secure management of healthcare records in dynamic contexts requires an understanding of the overall infrastructure of record flows and poses more challenges for vulnerable environments such as amongst the Rohingya refugees in Bangladesh. Understanding the overall infrastructure of how health clinics are providing medical treatments and how they are collecting and storing patient records is crucial as any changes or mismanagement in these records enables misuse or deliberate misinterpretations of medical data on various levels amongst individuals and Rohingya communities. Through an extensive field study in the Rohingya refugee camps in Bangladesh, we explored the management of healthcare records in different organizations. Over the course of our fieldwork, we interviewed 22 medical service providers from nine healthcare organizations connected to the Rohingya camps. Based on our findings, we design an abstract record management model and analyze it using a data provenance approach to identify the limitations of the existing record management. Our study shows vulnerabilities in ID management and security practices in healthcare record management. We further illustrate potential exploitation of these vulnerabilities through political, financial, and social lenses. To the best of our knowledge, this study is the first to discuss vulnerabilities in Rohingya refugees’ medical record management from political, social and economic views.","PeriodicalId":257424,"journal":{"name":"ACM SIGCAS/SIGCHI Conference on Computing and Sustainable Societies (COMPASS)","volume":"126 ","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"113993927","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}
Syeda Zainab Akbar, Ankur Sharma, Dibyendu Mishra, R. Mothilal, Himani Negi, Sachita Nishal, Anmol Panda, J. Pal
The death of Indian film star Sushant Singh Rajput at the peak of the COVID lockdown triggered chaos on the news cycle in India with a range of conspiracy theories that led to a witch hunt of sorts, and the hounding of several entertainers and public figures in the months that followed. Using data from Twitter, YouTube, and an archive of debunked misinformation stories, we examine the drivers and consequences of social media outrage in this case. We analyse these patterns from the framework of conspiracy and astroturfing and contextualize our findings to the socio-political background currently prevalent in India. Primarily, retweet rates on Twitter suggest that commentators benefited from talking about the case, which got higher engagement than other topics. Moreover, we report evidence of political hands in the way the discourse has shaped online, but more importantly that the story bears warnings for the shape and impact of witch-hunts in the backdrop of a fractured media environment. In conclusion, we consider the effects of Rajput’s outsider status as a small-town implant in the film industry within the broader narrative of systemic injustice, as well as the gendered aspects of mob justice that have taken aim at his former partner in the months since.
{"title":"Devotees on an Astroturf: Media, Politics, and Outrage in the Suicide of a Popular FilmStar","authors":"Syeda Zainab Akbar, Ankur Sharma, Dibyendu Mishra, R. Mothilal, Himani Negi, Sachita Nishal, Anmol Panda, J. Pal","doi":"10.1145/3530190.3534801","DOIUrl":"https://doi.org/10.1145/3530190.3534801","url":null,"abstract":"The death of Indian film star Sushant Singh Rajput at the peak of the COVID lockdown triggered chaos on the news cycle in India with a range of conspiracy theories that led to a witch hunt of sorts, and the hounding of several entertainers and public figures in the months that followed. Using data from Twitter, YouTube, and an archive of debunked misinformation stories, we examine the drivers and consequences of social media outrage in this case. We analyse these patterns from the framework of conspiracy and astroturfing and contextualize our findings to the socio-political background currently prevalent in India. Primarily, retweet rates on Twitter suggest that commentators benefited from talking about the case, which got higher engagement than other topics. Moreover, we report evidence of political hands in the way the discourse has shaped online, but more importantly that the story bears warnings for the shape and impact of witch-hunts in the backdrop of a fractured media environment. In conclusion, we consider the effects of Rajput’s outsider status as a small-town implant in the film industry within the broader narrative of systemic injustice, as well as the gendered aspects of mob justice that have taken aim at his former partner in the months since.","PeriodicalId":257424,"journal":{"name":"ACM SIGCAS/SIGCHI Conference on Computing and Sustainable Societies (COMPASS)","volume":"69 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":"130133157","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}
Alyssa Donawa, C. Powell, Rong Wang, Ming-Yuan Chih, E. Spencer, C. Baker
Despite the increase of accessibility and availability of technology in recent years, equality and access to health-related technology remains limited to certain demographics. In particular, patients who are older or from rural communities represent a large segment of people who are currently not utilizing electronic health solutions; and are considered medically underserved. Rural communities commonly have a higher rate of chronic disease and reduced access to providers; therefore, rural patients could benefit from the adoption of electronic health solutions such as mobile health apps. This pilot study explores the usability of the mobile iOS application, Assuage; designed for remote symptom monitoring in rural cancer patients and built using Apple’s ResearchKit, CareKit, and HealthKit frameworks. Two different interfaces for reporting symptoms are assessed using the System Usability Scale by fifteen (15) current and/or post surgery cancer patients.
{"title":"Note: Assessing Cancer Patient Usability of a Mobile Distress Screening App","authors":"Alyssa Donawa, C. Powell, Rong Wang, Ming-Yuan Chih, E. Spencer, C. Baker","doi":"10.1145/3530190.3534833","DOIUrl":"https://doi.org/10.1145/3530190.3534833","url":null,"abstract":"Despite the increase of accessibility and availability of technology in recent years, equality and access to health-related technology remains limited to certain demographics. In particular, patients who are older or from rural communities represent a large segment of people who are currently not utilizing electronic health solutions; and are considered medically underserved. Rural communities commonly have a higher rate of chronic disease and reduced access to providers; therefore, rural patients could benefit from the adoption of electronic health solutions such as mobile health apps. This pilot study explores the usability of the mobile iOS application, Assuage; designed for remote symptom monitoring in rural cancer patients and built using Apple’s ResearchKit, CareKit, and HealthKit frameworks. Two different interfaces for reporting symptoms are assessed using the System Usability Scale by fifteen (15) current and/or post surgery cancer patients.","PeriodicalId":257424,"journal":{"name":"ACM SIGCAS/SIGCHI Conference on Computing and Sustainable Societies (COMPASS)","volume":"79 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":"130273493","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}
Udit Paul, Jiamo Liu, David FARIAS-LLERENAS, V. Adarsh, Arpit Gupta, E. Belding-Royer
It is well documented that, in the United States (U.S.), the availability of Internet access is related to several demographic attributes. Data collected through end user network diagnostic tools, such as the one provided by the Measurement Lab (M-Lab) Speed Test, allows the extension of prior work by exploring the relationship between the quality, as opposed to only the availability, of Internet access and demographic attributes of users of the platform. In this study, we use network measurements collected from the users of Speed Test by M-Lab and demographic data to characterize the relationship between the quality-of-service (QoS) metric download speed, and various critical demographic attributes, such as income, education level, and poverty. For brevity, we limit our focus to the state of California. For users of the M-Lab Speed Test, our study has the following key takeaways: (1) geographic type (urban/rural) and income level in an area have the most significant relationship to download speed; (2) average download speed in rural areas is 2.5 times lower than urban areas; (3) the COVID-19 pandemic had a varied impact on download speeds for different demographic attributes; and (4) the U.S. Federal Communication Commission’s (FCC’s) broadband speed data significantly over-represents the download speed for rural and low-income communities compared to what is recorded through Speed Test.
{"title":"Characterizing Internet Access and Quality Inequities in California M-Lab Measurements","authors":"Udit Paul, Jiamo Liu, David FARIAS-LLERENAS, V. Adarsh, Arpit Gupta, E. Belding-Royer","doi":"10.1145/3530190.3534813","DOIUrl":"https://doi.org/10.1145/3530190.3534813","url":null,"abstract":"It is well documented that, in the United States (U.S.), the availability of Internet access is related to several demographic attributes. Data collected through end user network diagnostic tools, such as the one provided by the Measurement Lab (M-Lab) Speed Test, allows the extension of prior work by exploring the relationship between the quality, as opposed to only the availability, of Internet access and demographic attributes of users of the platform. In this study, we use network measurements collected from the users of Speed Test by M-Lab and demographic data to characterize the relationship between the quality-of-service (QoS) metric download speed, and various critical demographic attributes, such as income, education level, and poverty. For brevity, we limit our focus to the state of California. For users of the M-Lab Speed Test, our study has the following key takeaways: (1) geographic type (urban/rural) and income level in an area have the most significant relationship to download speed; (2) average download speed in rural areas is 2.5 times lower than urban areas; (3) the COVID-19 pandemic had a varied impact on download speeds for different demographic attributes; and (4) the U.S. Federal Communication Commission’s (FCC’s) broadband speed data significantly over-represents the download speed for rural and low-income communities compared to what is recorded through Speed Test.","PeriodicalId":257424,"journal":{"name":"ACM SIGCAS/SIGCHI Conference on Computing and Sustainable Societies (COMPASS)","volume":"9 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":"116671950","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}
Joy Ming, Srujana Kamath, Elizabeth Kuo, M. Sterling, Nicola Dell, Aditya Vashistha
Frontline health workers provide essential services for their communities, but much of their work remains invisible—undervalued and underappreciated. Examining this invisible work ensures new technologies do not amplify or reinforce inequitable power structures, especially as governments and organizations push to digitize health work processes. We build on a burgeoning conversation by studying how invisible work manifests and how this invisibility can be challenged in two contexts of frontline health: home health aides in New York City, USA and Accredited Social Health Activists (ASHAs) in Uttar Pradesh, India. We highlight three shared manifestations of invisible work: (1) work done outside of the workers’ boundaries (2) work done to gain and share knowledge and (3) work done to manage relationships. These common categories are experienced differently in the two contexts, raising nuances to consider when designing technology for frontline health workers. We discuss these nuances and other tensions through concrete examples of how workers can escalate feedback and conflicts, quantify implicit expertise about patients, or build more awareness of their situation. Our paper guides the creation of technologies that take into account a more comprehensive understanding of the frontline health workers’ processes and highlight more of their contributions.
{"title":"Invisible Work in Two Frontline Health Contexts","authors":"Joy Ming, Srujana Kamath, Elizabeth Kuo, M. Sterling, Nicola Dell, Aditya Vashistha","doi":"10.1145/3530190.3534814","DOIUrl":"https://doi.org/10.1145/3530190.3534814","url":null,"abstract":"Frontline health workers provide essential services for their communities, but much of their work remains invisible—undervalued and underappreciated. Examining this invisible work ensures new technologies do not amplify or reinforce inequitable power structures, especially as governments and organizations push to digitize health work processes. We build on a burgeoning conversation by studying how invisible work manifests and how this invisibility can be challenged in two contexts of frontline health: home health aides in New York City, USA and Accredited Social Health Activists (ASHAs) in Uttar Pradesh, India. We highlight three shared manifestations of invisible work: (1) work done outside of the workers’ boundaries (2) work done to gain and share knowledge and (3) work done to manage relationships. These common categories are experienced differently in the two contexts, raising nuances to consider when designing technology for frontline health workers. We discuss these nuances and other tensions through concrete examples of how workers can escalate feedback and conflicts, quantify implicit expertise about patients, or build more awareness of their situation. Our paper guides the creation of technologies that take into account a more comprehensive understanding of the frontline health workers’ processes and highlight more of their contributions.","PeriodicalId":257424,"journal":{"name":"ACM SIGCAS/SIGCHI Conference on Computing and Sustainable Societies (COMPASS)","volume":"128 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":"128212988","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}
Karm Patel, Rishiraj Adhikary, Zeel B Patel, Nipun Batra, S. Guttikunda
Air pollution killed 1.67M people in India in 2019. Previous work has shown that accurate public perception can help people identify the health risks of air pollution and act accordingly. News media influence how the public defines a social problem. However, news media analysis on air pollution has been on a small scale and regional. In this work, we gauge print news media response to air pollution in India on a larger scale. We curated a dataset of 17.4K news articles on air pollution from two leading English daily newspapers spanning 11 years. We performed exploratory data analysis and topic modeling to reveal the news media response to air pollution. Our study shows that, although air pollution is a year-long problem in India, the news media limelight on the issue is periodic (temporal bias). News media prefer to focus on the air pollution issue of metropolitan cities rather than the cities which are worst hit by air pollution (geographical bias). Also, the air pollution source contributions discussed in news articles significantly deviate from the scientific studies. Finally, we analyze the challenges raised by our findings and suggest potential solutions as well as the policy implications of our work.
{"title":"Samachar: Print News Media on Air Pollution in India","authors":"Karm Patel, Rishiraj Adhikary, Zeel B Patel, Nipun Batra, S. Guttikunda","doi":"10.1145/3530190.3534812","DOIUrl":"https://doi.org/10.1145/3530190.3534812","url":null,"abstract":"Air pollution killed 1.67M people in India in 2019. Previous work has shown that accurate public perception can help people identify the health risks of air pollution and act accordingly. News media influence how the public defines a social problem. However, news media analysis on air pollution has been on a small scale and regional. In this work, we gauge print news media response to air pollution in India on a larger scale. We curated a dataset of 17.4K news articles on air pollution from two leading English daily newspapers spanning 11 years. We performed exploratory data analysis and topic modeling to reveal the news media response to air pollution. Our study shows that, although air pollution is a year-long problem in India, the news media limelight on the issue is periodic (temporal bias). News media prefer to focus on the air pollution issue of metropolitan cities rather than the cities which are worst hit by air pollution (geographical bias). Also, the air pollution source contributions discussed in news articles significantly deviate from the scientific studies. Finally, we analyze the challenges raised by our findings and suggest potential solutions as well as the policy implications of our work.","PeriodicalId":257424,"journal":{"name":"ACM SIGCAS/SIGCHI Conference on Computing and Sustainable Societies (COMPASS)","volume":"80 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":"126391513","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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