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}
Aarjav Chauhan, Jasmine Yiyuan Qin, J. Sury, R. Soden
During disasters, emergency shelters play a central role in emergency management, providing both a secure environment and centralized sites for the distribution of information, material relief supplies, and access to health and human services. Despite their importance, challenges such as physical access, public awareness, and peoples’ willingness to relocate limit the impact of both shelters managed by emergency responders and informal locations created by affected communities. This paper presents research conducted as part of a long-term project aimed at designing digital tools to assist communities and formal responders plan and manage emergency shelters. Working with partners in Puerto Rico, we developed and distributed cultural probes in three communities with recent experience of hurricanes and earthquakes to better understand the needs and resources of disaster affected people related to shelter. This approach yielded novel insights that challenge and expand traditional views of emergency shelters and identified several areas where HCI research and design can contribute to the sector.
{"title":"Exploring Community Needs for Disaster Shelters Using Cultural Probes","authors":"Aarjav Chauhan, Jasmine Yiyuan Qin, J. Sury, R. Soden","doi":"10.1145/3530190.3534822","DOIUrl":"https://doi.org/10.1145/3530190.3534822","url":null,"abstract":"During disasters, emergency shelters play a central role in emergency management, providing both a secure environment and centralized sites for the distribution of information, material relief supplies, and access to health and human services. Despite their importance, challenges such as physical access, public awareness, and peoples’ willingness to relocate limit the impact of both shelters managed by emergency responders and informal locations created by affected communities. This paper presents research conducted as part of a long-term project aimed at designing digital tools to assist communities and formal responders plan and manage emergency shelters. Working with partners in Puerto Rico, we developed and distributed cultural probes in three communities with recent experience of hurricanes and earthquakes to better understand the needs and resources of disaster affected people related to shelter. This approach yielded novel insights that challenge and expand traditional views of emergency shelters and identified several areas where HCI research and design can contribute to the sector.","PeriodicalId":257424,"journal":{"name":"ACM SIGCAS/SIGCHI Conference on Computing and Sustainable Societies (COMPASS)","volume":"24 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":"122360527","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}
P. Gujarathi, Sai Krishna Reddy Gopi Reddy, Venkatanaidu Karri, A. Bhimireddy, A. Rajapuri, M. Reddy, Mounika Sabbani, Biju Cheriyan, Jack VanSchaik, T. Thyvalikakath, Sunandan Chakraborty
Research articles published in medical journals often present findings from causal experiments. In this paper, we use this intuition to build a model that leverages causal relations expressed in text to unearth factors related to Sjögren’s syndrome. Sjögren’s syndrome is an auto-immune disease affecting up to 3.1 million Americans. The uncommon nature of the disease, coupled with common symptoms with other autoimmune conditions make the timely diagnosis of this disease very hard. A centralized information system with easy access to common and uncommon factors related to Sjögren’s syndrome may alleviate the problem. We use automatically extracted causal relationships from text related to Sjögren’s syndrome collected from the medical literature to identify a set of factors, such as “signs and symptoms” and “associated conditions”, related to this disease. We show that our approach is capable of retrieving such factors with a high precision and recall values. Comparative experiments show that this approach leads to 25% improvement in retrieval F1-score compared to several state-of-the-art biomedical models, including BioBERT and Gram-CNN.
{"title":"Note: Using Causality to Mine Sjögren’s Syndrome related Factors from Medical Literature","authors":"P. Gujarathi, Sai Krishna Reddy Gopi Reddy, Venkatanaidu Karri, A. Bhimireddy, A. Rajapuri, M. Reddy, Mounika Sabbani, Biju Cheriyan, Jack VanSchaik, T. Thyvalikakath, Sunandan Chakraborty","doi":"10.1145/3530190.3534850","DOIUrl":"https://doi.org/10.1145/3530190.3534850","url":null,"abstract":"Research articles published in medical journals often present findings from causal experiments. In this paper, we use this intuition to build a model that leverages causal relations expressed in text to unearth factors related to Sjögren’s syndrome. Sjögren’s syndrome is an auto-immune disease affecting up to 3.1 million Americans. The uncommon nature of the disease, coupled with common symptoms with other autoimmune conditions make the timely diagnosis of this disease very hard. A centralized information system with easy access to common and uncommon factors related to Sjögren’s syndrome may alleviate the problem. We use automatically extracted causal relationships from text related to Sjögren’s syndrome collected from the medical literature to identify a set of factors, such as “signs and symptoms” and “associated conditions”, related to this disease. We show that our approach is capable of retrieving such factors with a high precision and recall values. Comparative experiments show that this approach leads to 25% improvement in retrieval F1-score compared to several state-of-the-art biomedical models, including BioBERT and Gram-CNN.","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":"130190350","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}
Human-wildlife conflict (HWC) is one of the most pressing conservation issues at present, with incidents leading to human injury and death, crop and property damage, and livestock predation. Since acquiring real-time data and performing manual analysis on those incidents are costly, we propose to leverage machine learning techniques to build an automated pipeline to construct an HWC knowledge base from historical news articles. Our unsupervised and active learning methods are not only able to recognize the major causes of HWC such as construction, pollution, and farming, but can also classify an unseen news article into its major cause with 90% accuracy. Moreover, our interactive visualizations of the knowledge base illustrate the spatial and temporal trend of human-wildlife conflicts across India for index by cities and animals. Based on our findings that most conflict zones include areas where human settlements are near forested areas, we extend our study to include satellite imagery to identify such proximity zones. We conduct a case study to use this method to identify human-elephant conflict hotspots in northern and western parts of the Indian state of West Bengal. We expect that our findings can inform the public of HWC hotspots and help in much more informed policymaking.
{"title":"Detecting Hotspots of Human-Wildlife Conflicts in India using News Articles and Aerial Images","authors":"Gokhan Egri, Xinran Han, Zilin Ma, Priyanka Surapaneni, Sunandan Chakraborty","doi":"10.1145/3530190.3534818","DOIUrl":"https://doi.org/10.1145/3530190.3534818","url":null,"abstract":"Human-wildlife conflict (HWC) is one of the most pressing conservation issues at present, with incidents leading to human injury and death, crop and property damage, and livestock predation. Since acquiring real-time data and performing manual analysis on those incidents are costly, we propose to leverage machine learning techniques to build an automated pipeline to construct an HWC knowledge base from historical news articles. Our unsupervised and active learning methods are not only able to recognize the major causes of HWC such as construction, pollution, and farming, but can also classify an unseen news article into its major cause with 90% accuracy. Moreover, our interactive visualizations of the knowledge base illustrate the spatial and temporal trend of human-wildlife conflicts across India for index by cities and animals. Based on our findings that most conflict zones include areas where human settlements are near forested areas, we extend our study to include satellite imagery to identify such proximity zones. We conduct a case study to use this method to identify human-elephant conflict hotspots in northern and western parts of the Indian state of West Bengal. We expect that our findings can inform the public of HWC hotspots and help in much more informed policymaking.","PeriodicalId":257424,"journal":{"name":"ACM SIGCAS/SIGCHI Conference on Computing and Sustainable Societies (COMPASS)","volume":"10 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":"126442243","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. Singanamalla, A. Mehra, Nishanth Chandran, Himanshi Lohchab, Seshanuradha Chava, Asit Kadayan, Sunil Bajpai, Kurtis Heimerl, R. Anderson, S. Lokam
The use of blockchain in regulatory ecosystems is a promising approach to address challenges of compliance among mutually untrusted entities. In this work, we consider applications of blockchain technologies in telecom regulations. In particular, we address growing concerns around Unsolicited Commercial Communication (UCC aka. spam) sent through text messages (SMS) and phone calls in India. Despite several regulatory measures taken to curb the menace of spam it continues to be a nuisance to subscribers while posing challenges to telecom operators and regulators alike. In this paper, we present a consortium blockchain based architecture to address the problem of UCC in India. Our solution improves subscriber experiences, improves the efficiency of regulatory processes while also positively impacting all stakeholders in the telecom ecosystem. Unlike previous approaches to the problem of UCC, which are all ex-post, our approach to adherence to the regulations is ex-ante. The proposal described in this paper is a primary contributor to the revision of regulations concerning UCC and spam by the Telecom Regulatory Authority of India (TRAI). The new regulations published in July 2018 were first of a kind in the world and amended the 2010 Telecom Commercial Communication Customer Preference Regulation (TCCCPR), through mandating the use of a blockchain/distributed ledgers in addressing the UCC problem. In this paper, we provide a holistic account of of the projects’ evolution from (1) its design and strategy, to (2) regulatory and policy action, (3) country wide implementation and deployment, and (4) evaluation and impact of the work. While the scope of the work presented in this paper is in the context of the UCC problem in India, we believe that the approach can be generalized to adopt blockchain based solutions to improve regulatory processes in other contexts and countries. We hope this account will serve as a useful case study for the stakeholders of the telecommunications ecosystem and regulators, and motivate countries across the world facing similar challenges to consider the viability of the technology, be convinced to establish it, continue efforts at addressing active research challenges, and scale the technology from our experiences.
{"title":"Telechain: Bridging Telecom Policy and Blockchain Practice","authors":"S. Singanamalla, A. Mehra, Nishanth Chandran, Himanshi Lohchab, Seshanuradha Chava, Asit Kadayan, Sunil Bajpai, Kurtis Heimerl, R. Anderson, S. Lokam","doi":"10.1145/3530190.3534820","DOIUrl":"https://doi.org/10.1145/3530190.3534820","url":null,"abstract":"The use of blockchain in regulatory ecosystems is a promising approach to address challenges of compliance among mutually untrusted entities. In this work, we consider applications of blockchain technologies in telecom regulations. In particular, we address growing concerns around Unsolicited Commercial Communication (UCC aka. spam) sent through text messages (SMS) and phone calls in India. Despite several regulatory measures taken to curb the menace of spam it continues to be a nuisance to subscribers while posing challenges to telecom operators and regulators alike. In this paper, we present a consortium blockchain based architecture to address the problem of UCC in India. Our solution improves subscriber experiences, improves the efficiency of regulatory processes while also positively impacting all stakeholders in the telecom ecosystem. Unlike previous approaches to the problem of UCC, which are all ex-post, our approach to adherence to the regulations is ex-ante. The proposal described in this paper is a primary contributor to the revision of regulations concerning UCC and spam by the Telecom Regulatory Authority of India (TRAI). The new regulations published in July 2018 were first of a kind in the world and amended the 2010 Telecom Commercial Communication Customer Preference Regulation (TCCCPR), through mandating the use of a blockchain/distributed ledgers in addressing the UCC problem. In this paper, we provide a holistic account of of the projects’ evolution from (1) its design and strategy, to (2) regulatory and policy action, (3) country wide implementation and deployment, and (4) evaluation and impact of the work. While the scope of the work presented in this paper is in the context of the UCC problem in India, we believe that the approach can be generalized to adopt blockchain based solutions to improve regulatory processes in other contexts and countries. We hope this account will serve as a useful case study for the stakeholders of the telecommunications ecosystem and regulators, and motivate countries across the world facing similar challenges to consider the viability of the technology, be convinced to establish it, continue efforts at addressing active research challenges, and scale the technology from our experiences.","PeriodicalId":257424,"journal":{"name":"ACM SIGCAS/SIGCHI Conference on Computing and Sustainable Societies (COMPASS)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126774243","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}
Xiou Ge, Richard Goodwin, Haizi Yu, Pablo Romero, Omar Abdelrahman, Amruta Sudhalkar, J. Kusuma, Ryan Cialdella, Nakul Garg, L. Varshney
Concrete is the most widely used engineered material in the world with more than 10 billion tons produced annually. Unfortunately, with that scale comes a significant burden in terms of energy, water, and release of greenhouse gases and other pollutants; indeed 8% of worldwide carbon emissions are attributed to the production of cement, a key ingredient in concrete. As such, there is interest in creating concrete formulas that minimize this environmental burden, while satisfying engineering performance requirements including compressive strength. Specifically for computing, concrete is a major ingredient in the construction of data centers. In this work, we use conditional variational autoencoders (CVAEs), a type of semi-supervised generative artificial intelligence (AI) model, to discover concrete formulas with desired properties. Our model is trained just using a small open dataset from the UCI Machine Learning Repository joined with environmental impact data from standard lifecycle analysis. Computational predictions demonstrate CVAEs can design concrete formulas with much lower carbon requirements than existing formulations while meeting design requirements. Next we report laboratory-based compressive strength experiments for five AI-generated formulations, which demonstrate that the formulations exceed design requirements. The resulting formulations were then used by Ozinga Ready Mix—a concrete supplier—to generate field-ready concrete formulations, based on local conditions and their expertise in concrete design. Finally, we report on how these formulations were used in the construction of buildings and structures in a Meta data center in DeKalb, IL, USA. Results from field experiments as part of this real-world deployment corroborate the efficacy of AI-generated low-carbon concrete mixes.
{"title":"Accelerated Design and Deployment of Low-Carbon Concrete for Data Centers","authors":"Xiou Ge, Richard Goodwin, Haizi Yu, Pablo Romero, Omar Abdelrahman, Amruta Sudhalkar, J. Kusuma, Ryan Cialdella, Nakul Garg, L. Varshney","doi":"10.1145/3530190.3534817","DOIUrl":"https://doi.org/10.1145/3530190.3534817","url":null,"abstract":"Concrete is the most widely used engineered material in the world with more than 10 billion tons produced annually. Unfortunately, with that scale comes a significant burden in terms of energy, water, and release of greenhouse gases and other pollutants; indeed 8% of worldwide carbon emissions are attributed to the production of cement, a key ingredient in concrete. As such, there is interest in creating concrete formulas that minimize this environmental burden, while satisfying engineering performance requirements including compressive strength. Specifically for computing, concrete is a major ingredient in the construction of data centers. In this work, we use conditional variational autoencoders (CVAEs), a type of semi-supervised generative artificial intelligence (AI) model, to discover concrete formulas with desired properties. Our model is trained just using a small open dataset from the UCI Machine Learning Repository joined with environmental impact data from standard lifecycle analysis. Computational predictions demonstrate CVAEs can design concrete formulas with much lower carbon requirements than existing formulations while meeting design requirements. Next we report laboratory-based compressive strength experiments for five AI-generated formulations, which demonstrate that the formulations exceed design requirements. The resulting formulations were then used by Ozinga Ready Mix—a concrete supplier—to generate field-ready concrete formulations, based on local conditions and their expertise in concrete design. Finally, we report on how these formulations were used in the construction of buildings and structures in a Meta data center in DeKalb, IL, USA. Results from field experiments as part of this real-world deployment corroborate the efficacy of AI-generated low-carbon concrete mixes.","PeriodicalId":257424,"journal":{"name":"ACM SIGCAS/SIGCHI Conference on Computing and Sustainable Societies (COMPASS)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125973636","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}
Saloni Dash, Rynaa Grover, Gazal Shekhawat, Sukhnidh Kaur, Dibyendu Mishra, J. Pal
Dangerous speech on social media platforms can be framed as blatantly inflammatory, or be couched in innuendo. It is also centrally tied to who engages it – it can be driven by openly sectarian social media accounts, or through subtle nudges by influential accounts, allowing for complex means of reinforcing vilification of marginalized groups, an increasingly significant problem in the media environment in the Global South. We identify dangerous speech by influential accounts on Twitter in India around three key events, examining both the language and networks of messaging that condones or actively promotes violence against vulnerable groups. We characterize dangerous speech users by assigning Danger Amplification Belief scores and show that dangerous users are more active on Twitter as compared to other users as well as most influential in the network, in terms of a larger following as well as volume of verified accounts. We find that dangerous users have a more polarized viewership, suggesting that their audience is more susceptible to incitement. Using a mix of network centrality measures and qualitative analysis, we find that most dangerous accounts tend to either be in mass media related occupations or allied with low-ranking, right-leaning politicians, and act as “broadcasters” in the network, where they are best positioned to spearhead the rapid dissemination of dangerous speech across the platform.
{"title":"Insights Into Incitement: A Computational Perspective on Dangerous Speech on Twitter in India","authors":"Saloni Dash, Rynaa Grover, Gazal Shekhawat, Sukhnidh Kaur, Dibyendu Mishra, J. Pal","doi":"10.1145/3530190.3534800","DOIUrl":"https://doi.org/10.1145/3530190.3534800","url":null,"abstract":"Dangerous speech on social media platforms can be framed as blatantly inflammatory, or be couched in innuendo. It is also centrally tied to who engages it – it can be driven by openly sectarian social media accounts, or through subtle nudges by influential accounts, allowing for complex means of reinforcing vilification of marginalized groups, an increasingly significant problem in the media environment in the Global South. We identify dangerous speech by influential accounts on Twitter in India around three key events, examining both the language and networks of messaging that condones or actively promotes violence against vulnerable groups. We characterize dangerous speech users by assigning Danger Amplification Belief scores and show that dangerous users are more active on Twitter as compared to other users as well as most influential in the network, in terms of a larger following as well as volume of verified accounts. We find that dangerous users have a more polarized viewership, suggesting that their audience is more susceptible to incitement. Using a mix of network centrality measures and qualitative analysis, we find that most dangerous accounts tend to either be in mass media related occupations or allied with low-ranking, right-leaning politicians, and act as “broadcasters” in the network, where they are best positioned to spearhead the rapid dissemination of dangerous speech across the platform.","PeriodicalId":257424,"journal":{"name":"ACM SIGCAS/SIGCHI Conference on Computing and Sustainable Societies (COMPASS)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127473684","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}
Phone sharing is pervasive in many low- and middle-income countries, affecting how millions of people interact with technology and each other. Yet there is very little quantitative evidence available on the extent or nature of phone sharing in resource-constrained contexts. This paper provides a comprehensive quantitative analysis of demographic variation in phone sharing patterns in Togo, and documents how a large cash transfer program during the COVID-19 pandemic impacted sharing. We analyze mobile phone records from the entire Togolese mobile network to measure the movement of SIM cards between SIM card slots (often on different mobile devices). By matching phone sharing measures derived from SIM reshuffling to demographic data from a government-run cash transfer program covering hundreds of thousands of individuals, we find that phone sharing is most common among women, young people, and people in rural areas. We also leverage randomization in the cash transfer program to find that the delivery of cash aid via mobile money significantly increases phone sharing among beneficiaries. We discuss the limitations of measuring phone sharing with mobile network data and the implications of our results for future aid programs delivered via mobile money.
{"title":"Phone Sharing and Cash Transfers in Togo: Quantitative Evidence from Mobile Phone Data","authors":"Emily L. Aiken, Viraj Thakur, J. Blumenstock","doi":"10.1145/3530190.3534796","DOIUrl":"https://doi.org/10.1145/3530190.3534796","url":null,"abstract":"Phone sharing is pervasive in many low- and middle-income countries, affecting how millions of people interact with technology and each other. Yet there is very little quantitative evidence available on the extent or nature of phone sharing in resource-constrained contexts. This paper provides a comprehensive quantitative analysis of demographic variation in phone sharing patterns in Togo, and documents how a large cash transfer program during the COVID-19 pandemic impacted sharing. We analyze mobile phone records from the entire Togolese mobile network to measure the movement of SIM cards between SIM card slots (often on different mobile devices). By matching phone sharing measures derived from SIM reshuffling to demographic data from a government-run cash transfer program covering hundreds of thousands of individuals, we find that phone sharing is most common among women, young people, and people in rural areas. We also leverage randomization in the cash transfer program to find that the delivery of cash aid via mobile money significantly increases phone sharing among beneficiaries. We discuss the limitations of measuring phone sharing with mobile network data and the implications of our results for future aid programs delivered via mobile money.","PeriodicalId":257424,"journal":{"name":"ACM SIGCAS/SIGCHI Conference on Computing and Sustainable Societies (COMPASS)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134466114","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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