Harnessing the Crowdsourcing Power of Social Media for Disaster Relief

By Huiji Gao, Geoffrey Barbier, Rebecca Goolsby. IEEE Intelligent Systems, 2011

862 citations recorded by the source corpus.

Subfields: Communication, Epidemiology, Sociology and Political Science

View Paper: https://www.semanticscholar.org/paper/a7247c4e4273bfccb59e3d51836fd9da7ce78bab

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Communication

1. James H. Fowler
2. Christopher R'e
3. Bruria Adini
4. Olga Russakovsky
5. Tomer Simon
6. Avishay Goldberg
7. Li Fei-Fei

In Epidemiology

1. Alexander J. Ratner
2. Christopher R'e
3. James H. Fowler
4. Li Fei-Fei
5. Jeannette Bohg
6. Toki Migimatsu

In Sociology and Political Science

1. Alexander J. Ratner
2. Sen Wu
3. James H. Fowler
4. Christopher R'e
5. Bruria Adini
6. Olga Russakovsky
7. Tomer Simon
8. Avishay Goldberg

Citations in the corpus

• Data Programming: Creating Large Training Sets, Quickly (2016, background): “There is also a wealth of examples where additional heuristic patterns used to label training data are collected from unlabeled data [6] or directly from users [19,27], in a similar manner to our approach, but without a framework to deal with the fact that said labels are explicitly noisy.”
• Rapid assessment of disaster damage using social media activity (2016, background): “Others use Twitter’s network properties to devise sensor techniques for early awareness (44), to gauge the dynamics of societal response (45, 46), and to crowdsource relief efforts (47).”
• Fonduer: Knowledge Base Construction from Richly Formatted Data (2017, neutral): “In addition to existing knowledge bases, crowdsourcing [12] and heuristics from domain experts [29] have also proven to be effective weak supervision sources.”
• Best of both worlds: Human-machine collaboration for object annotation (2015, background): “The field of crowd engineering has provided lots of insight into human-machine collaboration for solving difficult problems in computing such as protein folding [41, 9], disaster relief distribution [18] and galaxy discovery [38].”
• Socratic Learning: Augmenting Generative Models to Incorporate Latent Subsets in Training Data (2016, background): “Estimating the accuracies of these sources without access to ground truth labels is a classic problem [9], especially in the field of crowdsourcing [10, 7, 14, 33].”
• Socializing in emergencies - A review of the use of social media in emergency situations (2015, background): “, 2012) create SA reports and cooperation (Gao et al., 2011).”