Applications of Topics Models to Analysis of Disaster-Related Twitter Data

How researchers see this paper

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

Is the Sample Good Enough? Comparing Data from Twitter's Streaming API with Twitter's Firehose (2013, background): “(Kireyev, Palen, and Anderson 2009) drills the problem down to disaster-related tweets, discovering two main types of topics: informational and emotional.”
Information credibility on twitter (2011, unknown): “Twitter has been used widely during emergency situations, such as wildfires [6], hurricanes [12], floods [32, 33, 31] and earthquakes [15, 7].”
Twitter under crisis: can we trust what we RT? (2010, background): “[4] studied Twitter during two earthquakes in American Samoa and Sumatra that overlapped in time (both on September 30th 2009), with an emphasis on studying how to use topic modeling on the content of the tweets.”
Processing Social Media Messages in Mass Emergency (2014, methodology): “In the crisis domain, one example is [Kireyev et al. 2009], who use topics extracted using LDA, including a weighting scheme that accounts for the document frequency (number of tweets containing a word) of the words in the tweet, as well as for the length of the tweet.”
Applications of Topic Models (2017, background): “This is useful for companies trying to understand their customers, for politicians trying to target voters, for first responders reacting to a disaster [Kireyev et al., 2009], and for academics trying to understand how online communication morphs social norms.”