Interpretable classification and summarization of crisis events from microblogs

The widespread use of social media platforms has created convenient ways to obtain and spread up-to-date information during crisis events such as disasters. Time-critical analysis of crisis-related information helps humanitarian organizations and governmental bodies gain actionable information and plan for aid response. However, situational information is often immersed in a high volume of irrelevant content. Moreover, crisis-related messages also vary greatly in terms of information types, ranging from general situational awareness - such as information about warnings, infrastructure damages, and casualties - to individual needs. Different humanitarian organizations or governmental bodies usually demand information of different types for various tasks such as crisis preparation, resource planning, and aid response. To cope with information overload and efficiently support stakeholders in crisis situations, it is necessary to (a) classify data posted during crisis events into fine-grained humanitarian categories, (b) summarize the situational data in near real-time. In this thesis, we tackle the aforementioned problems and propose novel methods for the classification and summarization of user-generated posts from microblogs. Previous studies have introduced various machine learning techniques to assist humanitarian or governmental bodies, but they primarily focused on model performance. Unlike those works, we develop interpretable machine-learning models which can provide explanations of model decisions. Generally, we focus on three methods for reducing information overload in crisis situations: (i) post classification, (ii) post summarization, (iii) interpretable models for post classification and summarization. We evaluate our methods using posts from the microblogging platform Twitter, so-called tweets. First, we expand publicly available labeled datasets with rationale annotations. Each tweet is annotated with a class label and rationales, which are short snippets from the tweet to explain its assigned label. Using the data, we develop trustworthy classification methods that give the best tradeoff between model performance and interoperability. Rationale snippets usually convey essential information in the tweets. Hence, we propose an integer linear programming-based summarization method that maximizes the coverage of rationale phrases to generate summaries of class-level tweet data. Next, we introduce an approach that can enhance latent embedding representations of tweets in vector space. Our approach helps improve the classification performance-interpretability tradeoff and detect near duplicates for designing a summarization model with low computational complexity. Experiments show that rationale labels are helpful for developing interpretable-by-design models. However, annotations are not always available, especially in real-time situations for new tasks and crisis events. In the last part of the thesis, we propose a two-stage approach to extract the rationales under minimal human supervision

EveTAR: Building a Large-Scale Multi-Task Test Collection over Arabic Tweets

This article introduces a new language-independent approach for creating a large-scale high-quality test collection of tweets that supports multiple information retrieval (IR) tasks without running a shared-task campaign. The adopted approach (demonstrated over Arabic tweets) designs the collection around significant (i.e., popular) events, which enables the development of topics that represent frequent information needs of Twitter users for which rich content exists. That inherently facilitates the support of multiple tasks that generally revolve around events, namely event detection, ad-hoc search, timeline generation, and real-time summarization. The key highlights of the approach include diversifying the judgment pool via interactive search and multiple manually-crafted queries per topic, collecting high-quality annotations via crowd-workers for relevancy and in-house annotators for novelty, filtering out low-agreement topics and inaccessible tweets, and providing multiple subsets of the collection for better availability. Applying our methodology on Arabic tweets resulted in EveTAR , the first freely-available tweet test collection for multiple IR tasks. EveTAR includes a crawl of 355M Arabic tweets and covers 50 significant events for which about 62K tweets were judged with substantial average inter-annotator agreement (Kappa value of 0.71). We demonstrate the usability of EveTAR by evaluating existing algorithms in the respective tasks. Results indicate that the new collection can support reliable ranking of IR systems that is comparable to similar TREC collections, while providing strong baseline results for future studies over Arabic tweets