DISMISS: Database of Indian Social Media Influencers on Twitter

Autor: Arshia Arya, Soham De, Dibyendu Mishra, Gazal Shekhawat, Ankur Sharma, Anmol Panda, Faisal Lalani, Parantak Singh, Ramaravind Kommiya Mothilal, Rynaa Grover, Sachita Nishal, Saloni Dash, Shehla Shora, Syeda Zainab Akbar, Joyojeet Pal

Publikováno v: Proceedings of the International AAAI Conference on Web and Social Media. 16:1201-1207

Databases of highly networked individuals have been indispensable in studying narratives and influence on social media. To support studies on Twitter in India, we present a systematically categorized database of accounts of influence on Twitter in In

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::35492a14112387cc622525f3ef121f12
https://doi.org/10.1609/icwsm.v16i1.19370

Narrative Building in Propaganda Networks on Indian Twitter

Autor: Saloni Dash, Arshia Arya, Sukhnidh Kaur, Joyojeet Pal

Publikováno v: 14th ACM Web Science Conference 2022.

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::3c723811e93e4205db0a118c5ce72ebf
https://doi.org/10.1145/3501247.3531581

Insights Into Incitement: A Computational Perspective on Dangerous Speech on Twitter in India

Autor: Saloni Dash, Rynaa Grover, Gazal Shekhawat, Sukhnidh Kaur, Dibyendu Mishra, Joyojeet 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 influ

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::5c418d4e8c9c27768c7dcefd98c2a0bf

Generation and Evaluation of Privacy Preserving Synthetic Health Data

Autor: Adrien Pavao, Saloni Dash, Andrew Yale, Ritik Dutta, Kristin P. Bennett, Isabelle Guyon

Publikováno v: Neurocomputing
Neurocomputing, Elsevier, 2020, 416, pp.244-255. ⟨10.1016/j.neucom.2019.12.136⟩
Neurocomputing, 2020, 416, pp.244-255. ⟨10.1016/j.neucom.2019.12.136⟩

International audience; We develop metrics for measuring the quality of synthetic health data for both education and research. We use novel and existing metrics to capture a synthetic dataset's resemblance, privacy, utility and footprint. Using these

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::82766c99bc1a285bffbbfad3f44d7508
https://hal.inria.fr/hal-03158544/document

Synthesizing Quality Open Data Assets from Private Health Research Studies

Autor: Kristin P. Bennett, Isabelle Guyon, Karan Bhanot, John S. Erickson, Andrew Yale, Saloni Dash

Publikováno v: Business Information Systems Workshops ISBN: 9783030611453
BIS (Workshops)
BIS 2020-International Conference on Business Information Systems
BIS 2020-International Conference on Business Information Systems, Jun 2020, Colorado Springs, United States. pp.324-335

Generating synthetic data represents an attractive solution for creating open data, enabling health research and education while preserving patient privacy. We reproduce the research outcomes obtained on two previously published studies, which used p

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::4eb3ee3d7836164ced2f5c9a7c4056aa
https://doi.org/10.1007/978-3-030-61146-0_26