Cross-Lingual Classification of Crisis Data

Many citizens nowadays flock to social media during crises to share or acquire the latest information about the event. Due to the sheer volume of data typically circulated during such events, it is necessary to be able to efficiently filter out irrelevant posts, thus focusing attention on the posts that are truly relevant to the crisis. Current methods for classifying the relevance of posts to a crisis or set of crises typically struggle to deal with posts in different languages, and it is not viable during rapidly evolving crisis situations to train new models for each language. In this paper we test statistical and semantic classification approaches on cross-lingual datasets from 30 crisis events, consisting of posts written mainly in English, Spanish, and Italian. We experiment with scenarios where the model is trained on one language and tested on another, and where the data is translated to a single language. We show that the addition of semantic features extracted from external knowledge bases improve accuracy over a purely statistical model

Classifying Crises-Information Relevancy with Semantics

Social media platforms have become key portals for sharing and consuming information during crisis situations. However, humanitarian organisations and affected communities often struggle to sieve through the large volumes of data that are typically shared on such platforms during crises to determine which posts are truly relevant to the crisis, and which are not. Previous work on automatically classifying crisis information was mostly focused on using statistical features. However, such approaches tend to be inappropriate when processing data on a type of crisis that the model was not trained on, such as processing information about a train crash, whereas the classifier was trained on floods, earthquakes, and typhoons. In such cases, the model will need to be retrained, which is costly and time-consuming. In this paper, we explore the impact of semantics in classifying Twitter posts across same, and different, types of crises. We experiment with 26 crisis events, using a hybrid system that combines statistical features with various semantic features extracted from external knowledge bases. We show that adding semantic features has no noticeable benefit over statistical features when classifying same-type crises, whereas it enhances the classifier performance by up to 7.2% when classifying information about a new type of crisis

Identifying and Processing Crisis Information from Social Media

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

Communicating Uncertainty During Public Health Emergency Events:A Systematic Review

To answer the question, "What are the best ways to communicate uncertainties to public audiences, at-risk communities, and stakeholders during public health emergency events?" we conducted a systematic review of published studies, grey literature, and media reports in English and other United Nations (UN) languages: Arabic, Chinese, French, Russian, and Spanish. Almost 11,500 titles and abstracts were scanned of which 46 data-based primary studies were selected, which were classified into four methodological streams: Quantitative-comparison groups; Quantitative-descriptive survey; Qualitative; and Mixed-method and case-study. Study characteristics (study method, country, emergency type, emergency phase, at-risk population) and study findings (in narrative form) were extracted from individual studies. The findings were synthesized within methodological streams and evaluated for certainty and confidence. These within-method findings were next synthesized across methodological streams to develop an overarching synthesis of findings. The findings showed that country coverage focused on high and middle-income countries in Asia, Europe, North America, and Oceania, and the event most covered was infectious disease followed by flood and earthquake. The findings also showed that uncertainty during public health emergency events is a multi-faceted concept with multiple components (e.g., event occurrence, personal and family safety, recovery efforts). There is universal agreement, with some exceptions, that communication to the public should include explicit information about event uncertainties, and this information must be consistent and presented in an easy to understand format. Additionally, uncertainty related to events requires a distinction between uncertainty information and uncertainty experience. At-risk populations experience event uncertainty in the context of many other uncertainties they are already experiencing in their lives due to poverty. Experts, policymakers, healthcare workers, and other stakeholders experience event uncertainty and misunderstand some uncertainty information (e.g., event probabilities) similar to the public. Media professionals provide event coverage under conditions of contradictory and inconsistent event information that can heighten uncertainty experience for all

Cultural Appropriation and the Plains\u27 Indian Headdress

“Cultural appropriation” can be defined as the borrowing from someone else’s culture without their permission and without acknowledgement to the victim culture’s past. Recently there has been a conversation taking place between Native American communities and non-Indian communities over cases of cultural appropriation, specifically the misuse of the Plains’ Indian headdress, which Natives compare to the Medal of Honor. The “hipster subculture”, which can be defined as a generally pro-consumerist, anti-capitalist group of middle-to-upper class non-Indian Americans, has selectively appropriated aspects of many minority cultures; this action has heavily trended toward aspects of Native American culture. As a result, Native Americans have reacted with outrage as they perceive the offenses to be products of insensitivity, ignorance and prejudice. Although there are many justifications behind the actions of the hipster subculture, ultimately, studies suggest that the reasons for appropriation have been subconscious and unknown even to the subculture itself. Because they do not have a consistent body of rites and cultural traditions, middle-to-upper class non-Indian Americans who belong to the hipster subculture selectively appropriate aspects of minority culture such as the Plains’ Indian headdresses, not to offend its significance, but in order to subconsciously make it, and all they believe it stands for, a part of their own culture

The Corporate Signature Program: A Custom Approach to Philanthropy

The issues the developing world faces are complex; problems such as poverty, food security, illiteracy and malnutrition require multifaceted solutions with involvement from government, international institutions, nonprofits and the private sector. Whereas public sector funding was the major player in this field, private funding is becoming increasingly prevalent. U.S. corporations are relatively new players on the international development scene, but they are taking on an important role