Identifying valuable information from twitter during natural disasters

Abstract

Social media is a vital source of information during any major event, especially natural disasters. However, with the exponential increase in volume of social media data, so comes the increase in conversational data that does not provide valuable information, especially in the context of disaster events, thus, diminishing peoples’ ability to find the information that they need in order to organize relief efforts, find help, and potentially save lives. This project focuses on the development of a Bayesian approach to the classification of tweets (posts on Twitter) during Hurricane Sandy in order to distinguish “informational” from “conversational” tweets. We designed an effective set of features and used them as input to Naïve Bayes classifiers. In comparison to a “bag of words” approach, the new feature set provides similar results in the classification of tweets. However, the designed feature set contains only 9 features compared with more than 3000 features for “bag of words.” When the feature set is combined with “bag of words”, accuracy achieves 85.2914%. If integrated into disaster‐related systems, our approach can serve as a boon to any person or organization seeking to extract useful information in the midst of a natural disaster.

Natural disasters detection in social media and satellite imagery: a survey

“…However, the existing literature on disaster analysis lacks in dealing with the authenticity of the content, and additional measures need to be taken to check the authenticity of news and other information shared in social media. Moreover, the domain also lacks of public datasets, e.g., for Twitter most of the works [110,124,80,125] use self-collected datasets. Moreover, the datasets are often not large enough in terms of total number of images and types of the natural disaster events they cover.…”

“…Truong et al [125] rely on a Bayesian approach for the identification and classification of disaster-related tweets to differentiate hurricanes and sand storms tweets from the conversational ones. The proposed system uses an effective set of features, which are feed into a Bayes classifier.…”

Analytics and Evolving Landscape of Machine Learning for Emergency Response

“…Their model was evaluated with the data from Chennai flood 2015. Truong et al developed a Bayesian approach to the classification of tweets during Hurricane Sandy in order to distinguish "informational" from "conversational" tweets [130]. They designed an effective set of features and used them as input to NB classifiers.…”

Flood Relevance Estimation from Visual and Textual Content in Social Media Streams

“…First, within the hackAIR project [12], a platform has been developed for gathering and fusing environmental data and specifically Particulate Matter measurements from official sources and social media communities such as publicly available images shared through Instagram. In [21], the authors describe a framework that distinguishes between informational and conversational tweets shared during any major event and especially natural disasters. The framework uses a Naïve Bayes classifier for tweet classification and proposes the use of nine tweet-based features including emoticons, URLs, and instructional keywords.…”