Arjun Magge

763 total citations
24 papers, 423 citations indexed

About

Arjun Magge is a scholar working on Artificial Intelligence, Molecular Biology and Epidemiology. According to data from OpenAlex, Arjun Magge has authored 24 papers receiving a total of 423 indexed citations (citations by other indexed papers that have themselves been cited), including 11 papers in Artificial Intelligence, 10 papers in Molecular Biology and 6 papers in Epidemiology. Recurrent topics in Arjun Magge's work include Topic Modeling (11 papers), Biomedical Text Mining and Ontologies (6 papers) and Misinformation and Its Impacts (5 papers). Arjun Magge is often cited by papers focused on Topic Modeling (11 papers), Biomedical Text Mining and Ontologies (6 papers) and Misinformation and Its Impacts (5 papers). Arjun Magge collaborates with scholars based in United States, United Kingdom and Russia. Arjun Magge's co-authors include Graciela Gonzalez‐Hernandez, Davy Weissenbacher, Karen O’Connor, Abeed Sarker, Ari Z Klein, Matthew Scotch, Ashlynn R. Daughton, Michael J. Paul, Ilseyar Alimova and Elena Tutubalina and has published in prestigious journals such as Bioinformatics, PLoS ONE and The Science of The Total Environment.

In The Last Decade

Arjun Magge

24 papers receiving 370 citations

Peers

Arjun Magge

SPS

EPIDE

TOXIC

Davy Weissenbacher United States

Ari Z Klein United States

Ashlynn R. Daughton United States

Ai Kawazoe Japan

Douglas Teodoro Switzerland

Zain Hussain United Kingdom

Martin Müller Germany

Herman Tolentino United States

Todd Bodnar United States

Xiaolei Huang United States

Davy Weissenbacher United States

Arjun Magge

342 ×1.4

182 ×1.4

88 ×1.3

SPS

85 ×1.7

EPIDE

63 ×1.5

TOXIC

Citations per year, relative to Arjun Magge Arjun Magge (= 1×) peers Davy Weissenbacher

Countries citing papers authored by Arjun Magge

Since Specialization

Citations

This map shows the geographic impact of Arjun Magge's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Arjun Magge with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Arjun Magge more than expected).

Fields of papers citing papers by Arjun Magge

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Arjun Magge. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Arjun Magge. The network helps show where Arjun Magge may publish in the future.

Co-authorship network of co-authors of Arjun Magge

This figure shows the co-authorship network connecting the top 25 collaborators of Arjun Magge. A scholar is included among the top collaborators of Arjun Magge based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Arjun Magge. Arjun Magge is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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20 of 20 papers shown

O’Connor, Karen, Su Golder, Davy Weissenbacher, et al.. (2024). Methods and Annotated Data Sets Used to Predict the Gender and Age of Twitter Users: Scoping Review. Journal of Medical Internet Research. 26. e47923–e47923. 2 indexed citations

Lanera, Corrado, Giulia Lorenzoni, Elisa Barbieri, et al.. (2023). Monitoring the Epidemiology of Otitis Using Free-Text Pediatric Medical Notes: A Deep Learning Approach. Journal of Personalized Medicine. 14(1). 28–28. 1 indexed citations

Fontenele, Rafaela S., Yi‐Yan Yang, Erin M. Driver, et al.. (2023). Wastewater surveillance uncovers regional diversity and dynamics of SARS-CoV-2 variants across nine states in the USA. The Science of The Total Environment. 877. 162862–162862. 11 indexed citations

Klein, Ari Z, Arjun Magge, & Graciela Gonzalez‐Hernandez. (2022). ReportAGE: Automatically extracting the exact age of Twitter users based on self-reports in tweets. PLoS ONE. 17(1). e0262087–e0262087. 9 indexed citations

Klein, Ari Z, Arjun Magge, Karen O’Connor, & Graciela Gonzalez‐Hernandez. (2022). Automatically Identifying Twitter Users for Interventions to Support Dementia Family Caregivers: Annotated Data Set and Benchmark Classification Models. JMIR Aging. 5(3). e39547–e39547. 8 indexed citations

Golder, Su, et al.. (2022). A chronological and geographical analysis of personal reports of COVID-19 on Twitter from the UK. Digital Health. 8. 2282130079–2282130079. 8 indexed citations

Magge, Arjun, Elena Tutubalina, Zulfat Miftahutdinov, et al.. (2021). DeepADEMiner: a deep learning pharmacovigilance pipeline for extraction and normalization of adverse drug event mentions on Twitter. Journal of the American Medical Informatics Association. 28(10). 2184–2192. 26 indexed citations

Magge, Arjun, Ari Z Klein, Mohammed Ali Al-Garadi, et al.. (2021). Overview of the Sixth Social Media Mining for Health Applications (#SMM4H) Shared Tasks at NAACL 2021. 21–32. 45 indexed citations

Klein, Ari Z, et al.. (2021). Toward Using Twitter for Tracking COVID-19: A Natural Language Processing Pipeline and Exploratory Data Set. Journal of Medical Internet Research. 23(1). e25314–e25314. 46 indexed citations

10.

Klein, Ari Z, Ilseyar Alimova, Arjun Magge, et al.. (2020). Overview of the Fifth Social Media Mining for Health Applications (#SMM4H) Shared Tasks at COLING 2020. HAL (Le Centre pour la Communication Scientifique Directe). 27–36. 32 indexed citations

11.

Magge, Arjun, et al.. (2020). UPennHLP at WNUT-2020 Task 2 : Transformer models for classification of COVID19 posts on Twitter. 378–382. 2 indexed citations

12.

Weissenbacher, Davy, Abeed Sarker, Ari Z Klein, et al.. (2019). Deep neural networks ensemble for detecting medication mentions in tweets. Journal of the American Medical Informatics Association. 26(12). 1618–1626. 29 indexed citations

13.

Weissenbacher, Davy, Abeed Sarker, Arjun Magge, et al.. (2019). Overview of the Fourth Social Media Mining for Health (SMM4H) Shared Tasks at ACL 2019. 21–30. 70 indexed citations

14.

Weissenbacher, Davy, Arjun Magge, Karen O’Connor, Matthew Scotch, & Graciela Gonzalez‐Hernandez. (2019). SemEval-2019 Task 12: Toponym Resolution in Scientific Papers. 20 indexed citations

15.

Magge, Arjun, Davy Weissenbacher, Abeed Sarker, Matthew Scotch, & Graciela Gonzalez‐Hernandez. (2018). Bi-directional Recurrent Neural Network Models for Geographic Location Extraction in Biomedical Literature. PubMed. 24. 100–111. 6 indexed citations

16.

Scotch, Matthew, Davy Weissenbacher, Karen O’Connor, et al.. (2018). Incorporating sampling uncertainty in the geospatial assignment of taxa for virus phylogeography. Virus Evolution. 5(1). vey043–vey043. 11 indexed citations

17.

Magge, Arjun, Matthew Scotch, & Graciela Gonzalez‐Hernandez. (2018). Clinical NER and Relation Extraction using Bi-Char-LSTMs and Random Forest Classifiers. 25–30. 10 indexed citations

18.

Rouhizadeh, Masoud, Arjun Magge, Ari Z Klein, Abeed Sarker, & Graciela Gonzalez‐Hernandez. (2018). A Rule-based Approach to Determining Pregnancy Timeframe from Contextual Social Media Postings. 16–20. 8 indexed citations

19.

Sarker, Abeed, et al.. (2017). Discovering Cohorts of Pregnant Women From Social Media for Safety Surveillance and Analysis. Journal of Medical Internet Research. 19(10). e361–e361. 33 indexed citations

20.

Magge, Arjun, Matthew Scotch, & Graciela Gonzalez‐Hernandez. (2017). CSaRUS-CNN at AMIA-2017 Tasks 1, 2: Under Sampled CNN for Text Classification.. CEUR Workshop Proceedings. 1996. 76–78. 4 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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