CrisisBench: Benchmarking Crisis-related Social Media Datasets for Humanitarian Information Processing
Bibliographic record
Abstract
The CrisisBench dataset consists data from several different data sources such as CrisisLex (CrisisLex26, CrisisLex6), CrisisNLP, SWDM2013, ISCRAM13, Disaster Response Data (DRD), Disasters on Social Media (DSM), CrisisMMD and data from AIDR. The purpose of this work was to map the class label, remove duplicates and provide a benchmark results for the community. Class labels Informative vs not-informative: Informative Not informative Humanitarian categories Affected individual Caution and advice Displaced and evacuations Donation and volunteering Infrastructure and utilities damage Injured or dead people Missing and found people Not humanitarian Requests or needs Response efforts Sympathy and support https://crisisnlp.qcri.org/crisis_datasets_benchmarks.html https://github.com/firojalam/crisis_datasets_benchmarks Please cite the following papers if you use any of these resources in your research. Firoj Alam, Hassan Sajjad, Muhammad Imran and Ferda Ofli, CrisisBench: Benchmarking Crisis-related Social Media Datasets for Humanitarian Information Processing, In ICWSM, 2021. Firoj Alam, Ferda Ofli and Muhammad Imran. CrisisMMD: Multimodal Twitter Datasets from Natural Disasters. In Proceedings of the International AAAI Conference on Web and Social Media (ICWSM), 2018, Stanford, California, USA. Muhammad Imran, Prasenjit Mitra, and Carlos Castillo: Twitter as a Lifeline: Human-annotated Twitter Corpora for NLP of Crisis-related Messages. In Proceedings of the 10th Language Resources and Evaluation Conference (LREC), pp. 1638-1643. May 2016, Portorož, Slovenia. A. Olteanu, S. Vieweg, C. Castillo. 2015. What to Expect When the Unexpected Happens: Social Media Communications Across Crises. In Proceedings of the ACM 2015 Conference on Computer Supported Cooperative Work and Social Computing (CSCW '15). ACM, Vancouver, BC, Canada. A. Olteanu, C. Castillo, F. Diaz, S. Vieweg. 2014. CrisisLex: A Lexicon for Collecting and Filtering Microblogged Communications in Crises. In Proceedings of the AAAI Conference on Weblogs and Social Media (ICWSM'14). AAAI Press, Ann Arbor, MI, USA. Muhammad Imran, Shady Elbassuoni, Carlos Castillo, Fernando Diaz and Patrick Meier. Extracting Information Nuggets from Disaster-Related Messages in Social Media. In Proceedings of the 10th International Conference on Information Systems for Crisis Response and Management (ISCRAM), May 2013, Baden-Baden, Germany. Muhammad Imran, Shady Elbassuoni, Carlos Castillo, Fernando Diaz and Patrick Meier. Practical Extraction of Disaster-Relevant Information from Social Media. In Social Web for Disaster Management (SWDM'13) - Co-located with WWW, May 2013, Rio de Janeiro, Brazil. https://appen.com/datasets/combined- disaster-response-data/ https://data.world/crowdflower/disasters- on-social-media
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 0.019 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".