Allison K Gallegos-Jeffrey
Biographic Data
| ID | 6059650 |
|---|---|
| NAME | Allison K Gallegos-Jeffrey |
| GIVEN NAMES | Allison K |
| FAMILY NAME | Gallegos-Jeffrey |
| SIGNATURE | GALLEGOS-JEFFREY A K |
| AFFILIATIONS | Erika Bonnevie, Jaclyn Goldbarg, Allison K. Gallegos-Jeffrey, Sarah D. Rosenberg, and Joe Smyser were with The Public Good Projects, Alexandria, VA, at the time the work was conducted. Ellen Wartella is with The Northwestern School of Communication, Evanston, IL. |
| VERIFIED | No |
| TOTAL WORKS | 1 |
| TOTAL CITATIONS | 3 |
| WORKS AS AUTHOR | 1 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2020 |
| LATEST PUBLICATION YEAR | 2020 |
| H-INDEX | 1 |
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…
Communication (1 work) · Conversation (1 work) · Hate Speech and Cyberbullying Detection (1 work) · Law (1 work) · Medicine (1 work) · Misinformation (1 work) · Misinformation and Its Impacts (1 work) · Opposition (politics) (1 work) · Political science (1 work) · Psychology (1 work)