Extracting information nuggets from disaster- Related messages in social media
By Muhammad Imran, Shady Elbassuoni, Carlos Castillo, Fernando Diaz, Patrick Meier. International Conference on Information Systems for Crisis Response and Management, 2013
455 citations recorded by the source corpus.
View Paper: https://www.semanticscholar.org/paper/ba8977eb5d737d643f38d84d0c1476211bb88986
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
- Muhammad Imran
- Alexandra Olteanu
- Carlos Castillo
- Fernando Diaz
- Sarah Vieweg
- Prasenjit Mitra
- João Porto de Albuquerque
- Benjamin Herfort
Citations in the corpus
- Processing Social Media Messages in Mass Emergency (2014, methodology): “By information provided Caution and advice caution and advice [Imran et al. 2013b]; warnings [Acar and Muraki 2011]; hazard,”
- Twitter as a Lifeline: Human-annotated Twitter Corpora for NLP of Crisis-related Messages (2016, background): “Recent studies have shown the importance of social media messages to enhance situational awareness and also indicate that these messages contain significant actionable and tactical information (Cameron et al., 2012; Imran et al., 2013; Purohit et al., 2013).”
- What to Expect When the Unexpected Happens: Social Media Communications Across Crises (2015, methodology): “This practice follows customary procedures used for using crowdsourced annotation of text messages for both information type [4, 10, 24, 34] and information source [14, 34].”
- Characterizing the Propagation of Situational Information in Social Media During COVID-19 Epidemic: A Case Study on Weibo (2020, background): “to learn what kinds of help are available [15].”
- AIDR: artificial intelligence for disaster response (2014, background): “Despite the fact that social media streams contain a significant amount of noise, much research [9, 4] has shown that these same streams of information also include relevant, tactical information (e.g., regarding infrastructure damage, needs, donations).”
- A geographic approach for combining social media and authoritative data towards identifying useful information for disaster management (2015, methodology): “…al. 2010, Acar and Muraki 2011) or using machine learning algorithms based on the content of messages to classify messages as ‘ on the ground ’ (Starbird and Muzny 2012) or as coming from an ‘ eyewitness ’ (Imran et al. 2013) that may provide ‘ first-hand ’ observations (Landwehr and Carley 2014).”