Occasional Groups in Crowdsourcing Platforms

Contributors to online crowdsourcing systems generally work independently on pieces of the product but in some cases, task interdependencies may require collaboration to develop a final product. These collaborations though take a distinctive form because of the nature of crowdsourced work. Collaboration may be implicit instead of explicit. Individuals engaged in a group conversation may not stay with the group for long, i.e., the group is an ``occasional group.\u27\u27 Occasional group interactions are often not well supported by systems, as they are not designed for team work. This dissertation examines the characteristics and work of occasional groups in the Gravity Spy citizen science project. Occasional groups in this system form to reach agreement about the description of novel categories of data that volunteers identify in the system. The author first employed virtual ethnography over six months to investigate volunteers\u27 interactions and to identify features of the occasional groups in this setting. Most groups were transient, interacting only for a short time to develop one product, but a few worked together repeatedly.To describe the overall process of finding new categories brings individuals to work together, the author interviewed nine active volunteers about their work practices. Volunteers individually or collectively use tools such as hashtags, collections and a search tool to identify examples of a new category and to agree on a name and description. Finally, the author investigated the details of the processes of developing proposals for four new categories over three years. She employed virtual and trace ethnography to collect messages from several discussion threads and boards to identify the analytical moves made by occasional group members in developing a new category. Volunteers would speculate on a new pattern and its causes, discuss how different categories are related and split or merge descriptions. They employed techniques such as detailed descriptions of data to create common ground, @-mention of other volunteers to increase the visibility of their work to each other and use of the category proposal as a vehicle to coordinate their actions. Findings contribute to the group literature by recognizing that groups with no formal formation and work processes are capable of doing work that would not otherwise be possible. The results advance our understanding of group categorization literature by showing how the analytical moves are different when group members work occasionally. The thesis also provides some suggestions for better support of occasional groups in crowdsourcing platforms

Extracting and presenting different viewpoints from political news articles

This study explores user interest in browsing different viewpoints from politicians and news agencies in news recommendation system. Along with providing personalized news articles to the user, bringing the opinion of politicians and news agencies about political events might be interesting for users. As there is always bias in publishing the news articles, newsreaders try to realize politicians’ opinions manually. We designed a prototype system to extract relevant politicians' viewpoints to a controversial event, and present them along with the news articles in the same user interface. We then observed user behaviour in browsing viewpoints vs. original news articles. According to the users’ clicks pattern analysis and their responses to the questionnaire they are interested in browsing the different viewpoints. This result suggests that news recommendation system could provide alternative recommendation criteria such as different viewpoints when recommending news articles

The Genie in the Bottle: Different Stakeholders, Different Interpretations of Machine Learning

We explore how people developing or using a system with a machine-learning (ML) component come to understand the capabilities and challenges of ML. We draw on the social construction of technology (SCOT) tradition to frame our analysis of interviews and discussion board posts involving designers and users of a ML-supported citizen-science crowdsourcing project named Gravity Spy. We extend SCOT by anchoring our investigation in the different uses of the technology. We find that the type of understandings achieved by groups having less interaction with the technology is shaped more by outside influences and less by the specifics of the system and its role in the project. This initial understanding of how different participants understand and engage with ML points to challenges that need to be overcome to help users of a system deal with the opaque position that ML often holds in a work system

Sentiment Analysis in political news in news recommender systems

Human beings are always considering the opinions of each other, especially in domains that are related to the business. It has also other applications in social science, psychology and politics. The field of politics is the scope of this thesis that has been applied in the news recommender system. Along with providing personalized news articles to the user, bringing the sentiment of politicians and news agencies about political events might be interesting for users. As there is always bias in publishing the news articles, newsreaders try to realize them manually by themselves. This thesis provides different points of view of politicians to a specific event and the name of a news agency that has published the article. Through the natural language processing methods the sentiment of politicians quotes about a specific event in news articles is realized. This analysis is based on the politician s name that is chosen by the user. The new system is designed beside the primary system of news recommender on the same page with the aim of equal situation for being interacted by the users. Later it is evaluated if this feature is increasing their interaction with the system. According to the users clicks pattern analysis and their responses to the questionnaire they are interested in using the new system instead of the primary one. Through this case study, challenges and possibilities of more development are also specified

Extracting and presenting different viewpoints from political news articles

This study explores user interest in browsing different viewpoints from politicians and news agencies in news recommendation system. Along with providing personalized news articles to the user, bringing the opinion of politicians and news agencies about political events might be interesting for users. As there is always bias in publishing the news articles, newsreaders try to realize politicians’ opinions manually. We designed a prototype system to extract relevant politicians' viewpoints to a controversial event, and present them along with the news articles in the same user interface. We then observed user behaviour in browsing viewpoints vs. original news articles. According to the users’ clicks pattern analysis and their responses to the questionnaire they are interested in browsing the different viewpoints. This result suggests that news recommendation system could provide alternative recommendation criteria such as different viewpoints when recommending news articles

Linguistic Changes in Online Citizen Science: A Structurational Perspective

For peer-production projects to be successful, members must develop a specific and universal language that enables them to cooperate. Complicating the development of language in some projects is the lack of formalized structures (e.g., roles) that communicate to members the norms and practices around language. We address the question of how do role differences among participants interact with the adoption and dissemination of new terminologies in open peer production communities? Answering this question is crucial because we want communities to be productive even when self-managed, which requires understanding how shared language emerges. We examine this question using a structurational lens in the setting of a citizen science project. Exploring the use of words in the Gravity Spy citizen science project, we find that many words are reused and that most new words that are introduced are not picked up, showing a reproduction of structure. However, some novel words are used by others, showing an evolution of the structure. Participants with roles closer to the science are more likely to have their words reused, showing the mutually reinforcing nature of structures of signification, legitimation, and domination

Discovering features in gravitational-wave data through detector characterization, citizen science and machine learning

The observation of gravitational waves is hindered by the presence of transient noise (glitches). We study data from the third observing run of the Advanced LIGO detectors, and identify new glitch classes: Fast Scattering/Crown and Low-frequency Blips. Using training sets assembled by monitoring of the state of the detector, and by citizen-science volunteers, we update the Gravity Spy machine-learning algorithm for glitch classification. We find that Fast Scattering/Crown, linked to ground motion at the detector sites, is especially prevalent, and identify two subclasses linked to different types of ground motion. Reclassification of data based on the updated model finds that 27% of all transient noise at LIGO Livingston belongs to the Fast Scattering class, while 8% belongs to the Low-frequency Blip class, making them the most frequent and fourth most frequent sources of transient noise at that site. Our results demonstrate both how glitch classification can reveal potential improvements to gravitational-wave detectors, and how, given an appropriate framework, citizen-science volunteers may make discoveries in large data sets

Global, regional, and national burden of diabetes from 1990 to 2021, with projections of prevalence to 2050: a systematic analysis for the Global Burden of Disease Study 2021

Background: Diabetes is one of the leading causes of death and disability worldwide, and affects people regardless of country, age group, or sex. Using the most recent evidentiary and analytical framework from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD), we produced location-specific, age-specific, and sex-specific estimates of diabetes prevalence and burden from 1990 to 2021, the proportion of type 1 and type 2 diabetes in 2021, the proportion of the type 2 diabetes burden attributable to selected risk factors, and projections of diabetes prevalence through 2050. Methods: Estimates of diabetes prevalence and burden were computed in 204 countries and territories, across 25 age groups, for males and females separately and combined; these estimates comprised lost years of healthy life, measured in disability-adjusted life-years (DALYs; defined as the sum of years of life lost [YLLs] and years lived with disability [YLDs]). We used the Cause of Death Ensemble model (CODEm) approach to estimate deaths due to diabetes, incorporating 25 666 location-years of data from vital registration and verbal autopsy reports in separate total (including both type 1 and type 2 diabetes) and type-specific models. Other forms of diabetes, including gestational and monogenic diabetes, were not explicitly modelled. Total and type 1 diabetes prevalence was estimated by use of a Bayesian meta-regression modelling tool, DisMod-MR 2.1, to analyse 1527 location-years of data from the scientific literature, survey microdata, and insurance claims; type 2 diabetes estimates were computed by subtracting type 1 diabetes from total estimates. Mortality and prevalence estimates, along with standard life expectancy and disability weights, were used to calculate YLLs, YLDs, and DALYs. When appropriate, we extrapolated estimates to a hypothetical population with a standardised age structure to allow comparison in populations with different age structures. We used the comparative risk assessment framework to estimate the risk-attributable type 2 diabetes burden for 16 risk factors falling under risk categories including environmental and occupational factors, tobacco use, high alcohol use, high body-mass index (BMI), dietary factors, and low physical activity. Using a regression framework, we forecast type 1 and type 2 diabetes prevalence through 2050 with Socio-demographic Index (SDI) and high BMI as predictors, respectively. Findings: In 2021, there were 529 million (95% uncertainty interval [UI] 500-564) people living with diabetes worldwide, and the global age-standardised total diabetes prevalence was 6·1% (5·8-6·5). At the super-region level, the highest age-standardised rates were observed in north Africa and the Middle East (9·3% [8·7-9·9]) and, at the regional level, in Oceania (12·3% [11·5-13·0]). Nationally, Qatar had the world's highest age-specific prevalence of diabetes, at 76·1% (73·1-79·5) in individuals aged 75-79 years. Total diabetes prevalence-especially among older adults-primarily reflects type 2 diabetes, which in 2021 accounted for 96·0% (95·1-96·8) of diabetes cases and 95·4% (94·9-95·9) of diabetes DALYs worldwide. In 2021, 52·2% (25·5-71·8) of global type 2 diabetes DALYs were attributable to high BMI. The contribution of high BMI to type 2 diabetes DALYs rose by 24·3% (18·5-30·4) worldwide between 1990 and 2021. By 2050, more than 1·31 billion (1·22-1·39) people are projected to have diabetes, with expected age-standardised total diabetes prevalence rates greater than 10% in two super-regions: 16·8% (16·1-17·6) in north Africa and the Middle East and 11·3% (10·8-11·9) in Latin America and Caribbean. By 2050, 89 (43·6%) of 204 countries and territories will have an age-standardised rate greater than 10%. Interpretation: Diabetes remains a substantial public health issue. Type 2 diabetes, which makes up the bulk of diabetes cases, is largely preventable and, in some cases, potentially reversible if identified and managed early in the disease course. However, all evidence indicates that diabetes prevalence is increasing worldwide, primarily due to a rise in obesity caused by multiple factors. Preventing and controlling type 2 diabetes remains an ongoing challenge. It is essential to better understand disparities in risk factor profiles and diabetes burden across populations, to inform strategies to successfully control diabetes risk factors within the context of multiple and complex drivers. Funding: Bill & Melinda Gates Foundation