Science Advances

Supplementary Materials

This PDF file includes:

  • Table S1. List of keywords included in the analysis, with their corresponding
    message counts.
  • Table S2. Ranking of the keywords included in the analysis according to the strength of the correlation between distance and activity for East Coast cities.
  • Table S3. Activity-damage correlations across keywords in order of decreasing
  • Table S4. Effect of normalization variable choice on the strength of activity-damage relationship (ZCTA resolution).
  • Table S5. County-level estimates of damage: Modeling (Hazus-MH) and ex-post
    data on insurance and FEMA individual assistance grants.
  • Table S6. Strength of activity-damage correlations for different damage estimates.
  • Table S7. Predictive power of sentiment, analyzed at different spatial resolutions and normalized by either area Census population or local Twitter user count (“Twitter population”).
  • Table S8. List of the disasters considered in the study, with a description of the damage data available for analysis.
  • Table S9. Effect of the activity threshold filter on the strength of the relationship between Twitter activity and damage.
  • Table S10. Mutual correlations between sentiment metrics at the level of individual messages.
  • Table S11. Top-ranking words by frequency of occurrence in positive and negative messages.
  • Table S12. Sentiment as a predictor of damage: Comparison between metrics.
  • Fig. S1. Normalized local activity on the topic as a function of distance to the hurricane path.
  • Fig. S2. Originality of content, expressed through the fraction of retweets in the stream of messages.
  • Fig. S3. Global popularity of local content.
  • Fig. S4. Comparison of predictive capacity of activity and sentiment.
  • Fig. S5. Comparison of activity-damage correlation strength for different precision levels of geo-location.
  • Fig. S6. Average sentiment trends over time: Comparison between sentiment metrics.

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