CrisisBench: Benchmarking Crisis-related Social Media Datasets for Humanitarian Information Processing
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The <strong> CrisisBench</strong> 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. <h3><strong>Class labels </strong> </h3> <ul> <li>Informative vs not-informative:</li> <ul> <li>Informative</li> <li>Not informative</li> </ul> <li>Humanitarian categories</li> <ul> <li>Affected individual</li> <li>Caution and advice</li> <li>Displaced and evacuations</li> <li>Donation and volunteering</li> <li>Infrastructure and utilities damage</li> <li>Injured or dead people</li> <li>Missing and found people</li> <li>Not humanitarian</li> <li>Requests or needs</li> <li>Response efforts</li> <li>Sympathy and support</li> </ul> </ul> <a href="https://crisisnlp.qcri.org/crisis_datasets_benchmarks.html">https://crisisnlp.qcri.org/crisis_datasets_benchmarks.html</a> <a href="https://github.com/firojalam/crisis_datasets_benchmarks">https://github.com/firojalam/crisis_datasets_benchmarks</a> <br> <strong>Please cite the following papers if you use any of these resources in your research.</strong> <ol> <li>Firoj Alam, Hassan Sajjad, Muhammad Imran and Ferda Ofli, CrisisBench: Benchmarking Crisis-related Social Media Datasets for Humanitarian Information Processing, In ICWSM, 2021. </li> <li>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.</li> <li>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.</li> <li>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.</li> <li>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.</li> <li>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.</li> <li>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.</li> <li>https://appen.com/datasets/combined- disaster-response-data/</li> <li>https://data.world/crowdflower/disasters- on-social-media</li> </ol>
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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 it