The DOI matches its Crossref record; 9 of the 9 references it cites were found in a registry or an index.
Rapid assessment of disaster damage using social media activity
Open accessAbstract
Researchers show a correlation between per-capita social media activity and disaster damage, facilitating its rapid assessment.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Tropical cyclones and climate change
Knutson, T. R., McBride, J. L., Chan, J., Emanuel, K., Holland, G., Landsea, C., Held, I., Kossin, J. P., Srivastava, A. K., & Sugi, M. (2010) · Nature Geoscience · doi:10.1038/ngeo779 · Cited by 3,194
A 61-million-person experiment in social influence and political mobilization
Bond, R. M., Fariss, C. J., Jones, J. J., Kramer, A. D. I., Marlow, C., Settle, J. E., & Fowler, J. H. (2012) · Nature · doi:10.1038/nature11421 · Cited by 2,575
Self-citationThe Parable of Google Flu: Traps in Big Data Analysis
Lazer, D., Kennedy, R., King, G., & Vespignani, A. (2014) · Science · doi:10.1126/science.1248506 · Cited by 2,523 · cited 1×
“Moreover, an indirect and potentially nonstationary relationship between social media signals and real-world phenomena, compounded by changing social norms in the use of particular online platforms, calls for caution in developing predictive tools based on Big Data analysis (68).”
MethodSentiment strength detection in short informal text
Thelwall, M., Buckley, K., Paltoglou, G., Cai, D., & Kappas, A. (2010) · Journal of the American Society for Information Science and Technology · doi:10.1002/asi.21416 · Cited by 1,449
Diurnal and seasonal mood vary with work, sleep, and daylength across diverse cultures
Golder, S. A., & Macy, M. W. (2011) · Science · doi:10.1126/science.1202775 · Cited by 1,155 · cited 1×
“Sentiment in Twitter has been studied and demonstrated to reflect temporal (19, 70) and geographical (71) mood variations.”
BackgroundA unified theory of urban living
Bettencourt, L., & West, G. (2010) · Nature · doi:10.1038/467912a · Cited by 1,009
Is the Sample Good Enough? Comparing Data from Twitter's Streaming API with Twitter's Firehose
Morstatter, F., Pfeffer, J., Liu, H., & Carley, K. (2021) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v7i1.14401 · Cited by 863
Temporal patterns of happiness and information in a global social network: hedonometrics and Twitter
Dodds, P. S., Harris, K. D., Kloumann, I. M., Bliss, C. A., & Danforth, C. M. (2011) · PLoS ONE · doi:10.1371/journal.pone.0026752 · Cited by 809
The impact of climate change on global tropical cyclone damage
Mendelsohn, R., Emanuel, K., Chonabayashi, S., & Bakkensen, L. (2012) · Nature Climate Change · doi:10.1038/nclimate1357 · Cited by 805
Cited by
Applications of artificial intelligence for disaster management
Sun, W., Bocchini, P., & Davison, B. D. (2020) · Natural Hazards · doi:10.1007/s11069-020-04124-3 · Cited by 491
Crisis informatics—New data for extraordinary times
Palen, L., & Anderson, K. M. (2016) · Science · doi:10.1126/science.aag2579 · Cited by 309
Eight grand challenges in socio-environmental systems modeling
Elsawah, S., Filatova, T., Jakeman, A. J., Kettner, A. J., Zellner, M. L., Athanasiadis, I. N., Hamilton, S. H., Axtell, R. L., Brown, D. G., Gilligan, J. M., et al. (2020) · Socio-Environmental Systems Modelling · doi:10.18174/sesmo.2020a16226 · Cited by 205
A novel surveillance approach for disaster mental health
Gruebner, O., Lowe, S. R., Sykora, M., Shankardass, K., Subramanian, S. V., & Galea, S. (2017) · PLOS ONE · doi:10.1371/journal.pone.0181233 · Cited by 105
Digitalization and the Anthropocene
Creutzig, F., Acemoglu, D., Bai, X., Edwards, P. N., Hintz, M. J., Kaack, L. H., Kilkis, S., Kunkel, S., Luers, A., Milojevic-Dupont, N., et al. (2022) · Annual Review of Environment and Resources · doi:10.1146/annurev-environ-120920-100056 · Cited by 112
Metadata
- Authors
- Kryvasheyeu, Y., Chen, H., Obradovich, N., Moro, E., Van Hentenryck, P., Fowler, J., & Cebrian, M.
- Venue
- Science Advances
- Published
- 2016-03-04
- Volume
- 2
- Issue
- 3
- Pages
- e1500779-e1500779
- ISSN
- 2375-2548 (ISSN-L)
- OpenAlex
- W2298612130
- Cited by
- 637
- References
- 9
- FWCI
- 156.25
- Type
- journal article
- Language
- en
- Open access
- gold
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Rapid assessment of disaster damage using social media activity. https://app.cochat.ai/papers/10.1126/sciadv.1500779
- The paper
- Kryvasheyeu, Y., Chen, H., Obradovich, N., Moro, E., Van Hentenryck, P., Fowler, J., & Cebrian, M. (2016). Rapid assessment of disaster damage using social media activity. Science Advances. https://doi.org/10.1126/sciadv.1500779
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
