Adeena Malik
Biographic Data
| ID | 6072825 |
|---|---|
| NAME | Adeena Malik |
| GIVEN NAMES | Adeena |
| FAMILY NAME | Malik |
| SIGNATURE | MALIK A |
| AFFILIATIONS | Amelia M. Jamison, Kajal S. Parikh, and Adeena Malik are with the Maryland Center for Health Equity, School of Public Health, University of Maryland, College Park. David A. Broniatowski and Michael C. Smith are with the Department of Engineering Management and Systems Engineering, School of Engineering and Applied Science, and Institute for Data, Democracy, and Politics, The George Washington University, Washington, DC. Mark Dredze is with the Department of Computer Science, Whiting School of Engineering... |
| VERIFIED | No |
| TOTAL WORKS | 1 |
| TOTAL CITATIONS | 6 |
| AUTHOR COUNT | 1 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 2020 |
| LATEST PUBLICATION YEAR | 2020 |
| H-INDEX | 1 |
Adapting and Extending a Typology to Identify Vaccine Misinformation on Twitter
Objectives. To adapt and extend an existing typology of vaccine misinformation to classify the major topics of discussion across the total vaccine discourse on Twitter. Methods. Using 1.8 million vaccine-relevant tweets compiled from 2014 to 2017, we adapted an existing typology to Twitter data, first in a manual content analysis and then using latent Dirichlet allocation (LDA) topic modeling to extract 100 topics from the data set. Results. Manu…
Adapting and Extending a Typology to Identify Vaccine Misinformation on Twitter
Objectives. To adapt and extend an existing typology of vaccine misinformation to classify the major topics of discussion across the total vaccine discourse on Twitter. Methods. Using 1.8 million vaccine-relevant tweets compiled from 2014 to 2017, we adapted an existing typology to Twitter data, first in a manual content analysis and then using latent Dirichlet allocation (LDA) topic modeling to extract 100 topics from the data set. Results. Manu…
Adapting and Extending a Typology to Identify Vaccine Misinformation on Twitter
Objectives. To adapt and extend an existing typology of vaccine misinformation to classify the major topics of discussion across the total vaccine discourse on Twitter. Methods. Using 1.8 million vaccine-relevant tweets compiled from 2014 to 2017, we adapted an existing typology to Twitter data, first in a manual content analysis and then using latent Dirichlet allocation (LDA) topic modeling to extract 100 topics from the data set. Results. Manu…
Annotation (1 works) · Artificial Intelligence (1 works) · Artificial Intelligence (1 works) · Computer Science (1 works) · Hate Speech and Cyberbullying Detection (1 works) · Information retrieval (1 works) · Latent Dirichlet allocation (1 works) · Misinformation (1 works) · Misinformation and Its Impacts (1 works) · Political science (1 works)