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Research Article

Localizing COVID-19 Misinformation: A Case Study of Tracking Twitter Pandemic Narratives in Pennsylvania Using Computational Network Science

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Figures & data

Table 1. Percent of tweets from various PA cities

Table 2. Percent of tweets from various PA counties

Figure 1. COVID-19 communication analysis pipeline.

Figure 1. COVID-19 communication analysis pipeline.

Table 3. Categories for BEND maneuvers

Figure 2. Distribution of PA tweets over time.

Figure 2. Distribution of PA tweets over time.

Figure 3. Distribution of PA tweets between bots (red) and not bots (blue) over time.

Figure 3. Distribution of PA tweets between bots (red) and not bots (blue) over time.

Figure 4. Distribution of PA tweets between actors propagating negative stance narratives (red) and positive stance narratives (green) over time.

Figure 4. Distribution of PA tweets between actors propagating negative stance narratives (red) and positive stance narratives (green) over time.

Figure 5. All-communication network for users shows the prevalence of positive stance users (green agents) over negative stance users (red agents).

Figure 5. All-communication network for users shows the prevalence of positive stance users (green agents) over negative stance users (red agents).

Figure 6. Number of documents per BEND category.

Figure 6. Number of documents per BEND category.

Table 4. Examples for dismiss, distract, distort, and dismay

Table 5. Examples for explain, enhance, excite, and engage

Table 6. Examples of neutralize, nuke, narrow, and neglect

Table 7. Examples for build, back, boost, and bridge