Social media platforms play a crucial role in how people communicate, particularly during crisis situations such as natural disasters. People share and disseminate information on social media platforms that relates to updates, alerts, rescue and relief requests among other crisis relevant information. Hurricane Harvey and Hurricane Sandy saw over tens of millions of posts getting generated, on Twitter, in a short span of time. The ambit of such posts spreads across a wide range such as personal and official communications, and citizen sensing, to mention a few. This makes social media platforms a source of vital information to different stakeholders in crisis situations such as impacted communities, relief agencies, and civic authorities. However, the overwhelming volume of data generated during such times, makes it impossible to manually identify information relevant to crisis. Additionally, a large portion of posts in voluminous streams is not relevant or bears minimal relevance to crisis situations.
This has steered much research towards exploring methods that can automatically identify crisis relevant information from voluminous streams of data during such scenarios. However, the problem of identifying crisis relevant information from social media platforms, such as Twitter, is not trivial given the nature of unstructured text such as short text length and syntactic variations among other challenges. A key objective, while creating automatic crisis relevancy classification systems, is to make them adaptable to a wide range of crisis types and languages. Many related approaches rely on statistical features which are quantifiable properties and linguistic properties of the text. A general approach is to train the classification model on labelled data acquired from crisis events and evaluate on other crisis events. A key aspect missing from explored literature is the validity of crisis relevancy classification models when applied to data from unseen types of crisis events and languages. For instance, how would the accuracy of a crisis relevancy classification model, trained on earthquake type of events, change when applied to flood type of events. Or, how would a model perform when trained on crisis data in English but applied to data in Italian.
This thesis investigates these problems from a semantics perspective, where the challenges posed by diverse types of crisis and language variations are seen as the problems that can be tackled by enriching the data semantically. The use of knowledge bases such as DBpedia, BabelNet, and Wikipedia, for semantic enrichment of data in text classification problems has often been studied. Semantic enrichment of data through entity linking and expansion of context via knowledge bases can take advantage of connections between different concepts and thus enhance contextual coherency across crisis types and languages. Several previous works have focused on similar problems and proposed approaches using statistical features and/or non-semantic features. The use of semantics extracted through knowledge graphs has remained unexplored in building crisis relevancy classifiers that are adaptive to varying crisis types and multilingual data. Experiments conducted in this thesis consider data from Twitter, a micro-blogging social media platform, and analyse multiple aspects of crisis data classification. The results obtained through various analyses in this thesis demonstrate the value of semantic enrichment of text through knowledge graphs in improving the adaptability of crisis relevancy classifiers across crisis types and languages, in comparison to statistical features as often used in much of the related work
