Shweta Bhatt - Academia.edu (original) (raw)
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Papers by Shweta Bhatt
Companion of the The Web Conference 2018 on The Web Conference 2018 - WWW '18
This paper aims to shed light on alternative news media ecosystems that are believed to have infl... more This paper aims to shed light on alternative news media ecosystems that are believed to have influenced opinions and beliefs by false and/or biased news reporting during the 2016 US Presidential Elections. We examine a large, professionally curated list of 668 hyper-partisan websites and their corresponding Facebook pages, and identify key characteristics that mediate the traffic flow within this ecosystem. We uncover a pattern of new websites being established in the run up to the elections, and abandoned after. Such websites form an ecosystem, creating links from one website to another, and by 'liking' each others' Facebook pages. These practices are highly effective in directing user traffic internally within the ecosystem in a highly partisan manner, with right-leaning sites linking to and liking other right-leaning sites and similarly left-leaning sites linking to other sites on the left, thus forming a filter bubble amongst news producers similar to the filter bubble which has been widely observed among consumers of partisan news. Whereas there is activity along both left-and right-leaning sites, right-leaning sites are more evolved, accounting for a disproportionate number of abandoned websites and partisan internal links. We also examine demographic characteristics of consumers of hyper-partisan news and find that some of the more populous demographic groups in the US tend to be consumers of more right-leaning sites. CCS CONCEPTS • Social and professional topics → Technology and censorship; Political speech; • Human-centered computing → Social networking sites;
The SARS-CoV-2 (COVID-19) pandemic spotlighted the importance of moving quickly with biomedical r... more The SARS-CoV-2 (COVID-19) pandemic spotlighted the importance of moving quickly with biomedical research. However, as the number of biomedical research papers continue to increase, the task of finding relevant articles to answer pressing questions has become significant. In this work, we propose a textual data mining tool that supports literature search to accelerate the work of researchers in the biomedical domain. We achieve this by building a neural-based deep contextual understanding model for Question-Answering (QA) and Information Retrieval (IR) tasks. We also leverage the new BREATHE dataset which is one of the largest available datasets of biomedical research literature, containing abstracts and full-text articles from ten different biomedical literature sources on which we pre-train our BioMedBERT model. Our work achieves state-of-the-art results on the QA fine-tuning task on BioASQ 5b, 6b and 7b datasets. In addition, we observe superior relevant results when BioMedBERT em...
Companion of the The Web Conference 2018 on The Web Conference 2018 - WWW '18
This paper aims to shed light on alternative news media ecosystems that are believed to have infl... more This paper aims to shed light on alternative news media ecosystems that are believed to have influenced opinions and beliefs by false and/or biased news reporting during the 2016 US Presidential Elections. We examine a large, professionally curated list of 668 hyper-partisan websites and their corresponding Facebook pages, and identify key characteristics that mediate the traffic flow within this ecosystem. We uncover a pattern of new websites being established in the run up to the elections, and abandoned after. Such websites form an ecosystem, creating links from one website to another, and by 'liking' each others' Facebook pages. These practices are highly effective in directing user traffic internally within the ecosystem in a highly partisan manner, with right-leaning sites linking to and liking other right-leaning sites and similarly left-leaning sites linking to other sites on the left, thus forming a filter bubble amongst news producers similar to the filter bubble which has been widely observed among consumers of partisan news. Whereas there is activity along both left-and right-leaning sites, right-leaning sites are more evolved, accounting for a disproportionate number of abandoned websites and partisan internal links. We also examine demographic characteristics of consumers of hyper-partisan news and find that some of the more populous demographic groups in the US tend to be consumers of more right-leaning sites. CCS CONCEPTS • Social and professional topics → Technology and censorship; Political speech; • Human-centered computing → Social networking sites;
The SARS-CoV-2 (COVID-19) pandemic spotlighted the importance of moving quickly with biomedical r... more The SARS-CoV-2 (COVID-19) pandemic spotlighted the importance of moving quickly with biomedical research. However, as the number of biomedical research papers continue to increase, the task of finding relevant articles to answer pressing questions has become significant. In this work, we propose a textual data mining tool that supports literature search to accelerate the work of researchers in the biomedical domain. We achieve this by building a neural-based deep contextual understanding model for Question-Answering (QA) and Information Retrieval (IR) tasks. We also leverage the new BREATHE dataset which is one of the largest available datasets of biomedical research literature, containing abstracts and full-text articles from ten different biomedical literature sources on which we pre-train our BioMedBERT model. Our work achieves state-of-the-art results on the QA fine-tuning task on BioASQ 5b, 6b and 7b datasets. In addition, we observe superior relevant results when BioMedBERT em...