Sarah D Rosenberg
Biographic Data
| ID | 6059651 |
|---|---|
| NAME | Sarah D Rosenberg |
| GIVEN NAMES | Sarah D |
| FAMILY NAME | Rosenberg |
| SIGNATURE | ROSENBERG S D |
| AFFILIATIONS | The Public Good Projects, Alexandria, Estados Unidos de América |
| VERIFIED | No |
| TOTAL WORKS | 2 |
| TOTAL CITATIONS | 3 |
| WORKS AS AUTHOR | 2 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2020 |
| LATEST PUBLICATION YEAR | 2021 |
| H-INDEX | 1 |
Temas de contenido y voces influyentes dentro de la oposición a las vacunas en Twitter, 2019
Objetivo. Informar sobre la oposición a las vacunas y la información errónea fomentadas en Twitter, destacando las cuentas de Twitter que dirigen estas conversaciones. Métodos. Utilizamos el aprendizaje automático supervisado para codificar todos los mensajes publicados en Twitter. En primer lugar, identificamos manualmente los códigos y los temas mediante un enfoque teórico fundamentado y, a continuación, los aplicamos a todo el conjunto de dato…
Content Themes and Influential Voices Within Vaccine Opposition on Twitter, 2019
Objectives. To report on vaccine opposition and misinformation promoted on Twitter, highlighting Twitter accounts that drive conversation. Methods. We used supervised machine learning to code all Twitter posts. We first identified codes and themes manually by using a grounded theoretical approach and then applied them to the full data set algorithmically. We identified the top 50 authors month-over-month to determine influential sources of inform…
Content Themes and Influential Voices Within Vaccine Opposition on Twitter, 2019
Objectives. To report on vaccine opposition and misinformation promoted on Twitter, highlighting Twitter accounts that drive conversation. Methods. We used supervised machine learning to code all Twitter posts. We first identified codes and themes manually by using a grounded theoretical approach and then applied them to the full data set algorithmically. We identified the top 50 authors month-over-month to determine influential sources of inform…
Content Themes and Influential Voices Within Vaccine Opposition on Twitter, 2019
Objectives. To report on vaccine opposition and misinformation promoted on Twitter, highlighting Twitter accounts that drive conversation. Methods. We used supervised machine learning to code all Twitter posts. We first identified codes and themes manually by using a grounded theoretical approach and then applied them to the full data set algorithmically. We identified the top 50 authors month-over-month to determine influential sources of inform…
Temas de contenido y voces influyentes dentro de la oposición a las vacunas en Twitter, 2019
Objetivo. Informar sobre la oposición a las vacunas y la información errónea fomentadas en Twitter, destacando las cuentas de Twitter que dirigen estas conversaciones. Métodos. Utilizamos el aprendizaje automático supervisado para codificar todos los mensajes publicados en Twitter. En primer lugar, identificamos manualmente los códigos y los temas mediante un enfoque teórico fundamentado y, a continuación, los aplicamos a todo el conjunto de dato…
Communication (2 works) · Conversation (2 works) · Hate Speech and Cyberbullying Detection (2 works) · Law (2 works) · Misinformation (2 works) · Misinformation and Its Impacts (2 works) · Political science (2 works) · Vaccine Coverage and Hesitancy (2 works) · Communication (1 work) · Law (1 work)