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Derek Ruths

Alumni

Publications

Who Controlled the Evidence? Question Answering for Disclosure Information Extraction
Hardy Hardy
Derek Ruths
Nicholas B King
Conflict of interest (COI) disclosure statements provide rich information to support trans-parency and reduce bias in research. We introduce… (voir plus) a novel task to identify relationships between sponsoring entities and the research studies they sponsor from the disclosure statement. This task is challenging due to the complexity of recognizing all potential relationship patterns and the hierarchical nature of identifying entities first and then extracting their relationships to the study. To overcome these challenges, in this paper, we also constructed a new annotated dataset and proposed a Question Answering-based method to recognize entities and extract relationships. Our method has demonstrated robustness in handling diverse relationship patterns, and it remains effective even when trained on a low-resource dataset.
2023-06-13
Proceedings of the Conference on Health, Inference, and Learning (publié)
proceedings.mlr.press
Invasion of Ukraine Discourse on TikTok Dataset
Benjamin D. Steel
Sara J. Parker
Derek Ruths
We present a dataset of videos and comments from the social media platform TikTok, centred around the invasion of Ukraine in 2022, an event … (voir plus)that launched TikTok into the geopolitical arena. The discourse around the invasion exposed myriad political behaviours and dynamics that are unexplored on this platform. To this end we provide a mass scale language and interaction dataset for further research into these processes. An initial investigation of language and social interaction dynamics are explored in this paper. The dataset and the library used to collect it are open sourced to the public.
2023-01-01
arXiv.org (prépublication)
doi.org
The epidemiological impact of the Canadian COVID Alert app
Shuo Sun
Mairead Shaw
Erica E. M. Moodie
Derek Ruths
We analyzed the effectiveness of the Canadian COVID Alert app on reducing COVID-19 infections and deaths due to the COVID-19 virus. Two sepa… (voir plus)rate but complementary approaches were taken. First, we undertook a comparative study to assess how the adoption and usage of the COVID Alert app compared to those of similar apps deployed in other regions. Next, we used data from the COVID Alert server and a range of plausible parameter values to estimate the numbers of infections and deaths averted in Canada using a model that combines information on number of notifications, secondary attack rate, expected fraction of transmissions that could be prevented, quarantine effectiveness, and expected size of the full transmission chain in the absence of exposure notification. The comparative analysis revealed that the COVID Alert app had among the lowest adoption levels among apps that reported usage. Our model indicates that use of the COVID Alert app averted between 6284 and 10,894 infections across the six Canadian provinces where app usage was highest during the March–July 2021 period. This range is equivalent to 1.6–2.9% of the total recorded infections across Canada in that time. Using province-specific case fatality rates, 57–101 deaths were averted during the same period. The number of cases and deaths averted was greatest in Ontario, whereas the proportion of cases and deaths averted was greatest in Newfoundland and Labrador. App impact measures were reported so rarely and so inconsistently by other regions that the relative assessment of impact is inconclusive. While the nationwide rates are low, provinces with widespread adoption of the app showed high ratios of averted cases and deaths (upper bound was greater than 60% of averted cases). This finding suggests that the COVID Alert app, when adopted at sufficient levels, can be an effective public health tool for combatting a pandemic such as COVID-19.
2022-01-05
Canadian journal of public health (published)
doi.org
The epidemiological impact of the Canadian COVID Alert app
Shuo Sun
Mairead Shaw
Erica E.M. Moodie
Derek Ruths
We analyzed the effectiveness of the Canadian COVID Alert app on reducing COVID-19 infections and deaths due to the COVID-19 virus. Two sepa… (voir plus)rate but complementary approaches were taken. First, we undertook a comparative study to assess how the adoption and usage of the COVID Alert app compared to those of similar apps deployed in other regions. Next, we used data from the COVID Alert server and a range of plausible parameter values to estimate the numbers of infections and deaths averted in Canada using a model that combines information on number of notifications, secondary attack rate, expected fraction of transmissions that could be prevented, quarantine effectiveness, and expected size of the full transmission chain in the absence of exposure notification. The comparative analysis revealed that the COVID Alert app had among the lowest adoption levels among apps that reported usage. Our model indicates that use of the COVID Alert app averted between 6284 and 10,894 infections across the six Canadian provinces where app usage was highest during the March–July 2021 period. This range is equivalent to 1.6–2.9% of the total recorded infections across Canada in that time. Using province-specific case fatality rates, 57–101 deaths were averted during the same period. The number of cases and deaths averted was greatest in Ontario, whereas the proportion of cases and deaths averted was greatest in Newfoundland and Labrador. App impact measures were reported so rarely and so inconsistently by other regions that the relative assessment of impact is inconclusive. While the nationwide rates are low, provinces with widespread adoption of the app showed high ratios of averted cases and deaths (upper bound was greater than 60% of averted cases). This finding suggests that the COVID Alert app, when adopted at sufficient levels, can be an effective public health tool for combatting a pandemic such as COVID-19.
2022-01-05
Canadian journal of public health (publié)
doi.org
All in This Together? A Preregistered Report on Deservingness of Government Aid During the COVID-19 Pandemic
Aengus Bridgman
Eric Roman Owen Merkley
Peter John Loewen
Taylor Reid Owen
Derek Ruths
Abstract The COVID-19 pandemic has placed unprecedented pressure on governments to engage in widespread cash transfers directly to citizens … (voir plus)to help mitigate economic losses. Major and near-universal redistribution efforts have been deployed, but there is remarkably little understanding of where the mass public believes financial support is warranted. Using experimental evidence, we evaluate whether considerations related to deservingness, similarity, and prejudicial attitudes structure support for these transfers. A preregistered experiment found broad, generous, and nondiscriminatory support for direct cash transfers related to COVID-19 in Canada. The second study, accepted as a preregistered report, further probes these dynamics by comparing COVID-19-related outlays with nonemergency ones. We find that COVID-19-related spending was more universal as compared to a more generic cash allocation program. Given that the results were driven by the income of hypothetical recipients, we find broad support for disaster relief that is not means-tested or otherwise constrained by pre-disaster income.
2021-03-31
Journal of Experimental Political Science (published)
doi.org
All in This Together? A Preregistered Report on Deservingness of Government Aid During the COVID-19 Pandemic
Aengus Bridgman
Eric Merkley
Peter John Loewen
Taylor Owen
Derek Ruths
2021-03-31
Journal of Experimental Political Science (publié)
doi.org
A Hierarchical Neural Attention-based Text Classifier
Koustuv Sinha
Yue Dong
Jackie Cheung
Derek Ruths
Deep neural networks have been displaying superior performance over traditional supervised classifiers in text classification. They learn to… (voir plus) extract useful features automatically when sufficient amount of data is presented. However, along with the growth in the number of documents comes the increase in the number of categories, which often results in poor performance of the multiclass classifiers. In this work, we use external knowledge in the form of topic category taxonomies to aide the classification by introducing a deep hierarchical neural attention-based classifier. Our model performs better than or comparable to state-of-the-art hierarchical models at significantly lower computational cost while maintaining high interpretability.
2018-01-01
Conference on Empirical Methods in Natural Language Processing (publié)
doi.org