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

Statistical Semantic Classification of Crisis Information

The rise of social media as an information channel during crisis has become key to community response. However, existing crisis awareness applications, often struggle to identify relevant information among the high volume of data that is generated over social platforms. A wide range of statistical features and machine learning methods have been researched in recent years to automatically classify this information. In this paper we aim to complement previous studies by exploring the use of semantics as additional features to identify relevant crisis in- formation. Our assumption is that entities and concepts tend to have a more consistent correlation with relevant and irrelevant information, and therefore can enhance the discrimination power of classifiers. Our results, so far, show that some classification improvements can be obtained when using semantic features, reaching +2.51% when the classifier is applied to a new crisis event (i.e., not in training set)

The Effect of Inlet Pulsations on Primary Atomization of Liquid Jets

Objectives Elucidate the physics underlying the primary atomization of liquid jets. Investigate the effect of inlet pulsations on the atomization process. Identify the reliability of numerical predictions using uncertainty quantification techniques (UQ) and sensitivity analyses

High-Fidelity Simulations of Water Jet in Air Crossflow

Objectives Investigate detailed physics underlying liquid jets in crossflow configurations applicable to various applications such as, gas-turbine, scramjet, and afterburner fuel injection. Develop models to predict the statistical behaviors of resulting droplets

Detection of Alzheimer’s Disease using CNN Architectures

Alzheimer’s disease is a neurological condition that causes some structural alterations in the brain. In this paper we have given an overview of all the available good CNN models used in medical imaging for image classification purpose such asAlexNet, GoogleNet, ResNet 18, ResNet 50, SqueezeNet and DenseNet. Using these CNN models, we have been able to classify three different stages of Alzheimer's disease – Cognitively Normal (NC), Mild Cognitive Impairment (MCI) and Alzheimer’s Disease(AD). The dataset is derived from ADNI and has been preprocessed before applying various CNN models. The experimental results demonstrate that all models performed well and the best accuracy has been acquired by the GoogleNet of 96.81%

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

Co-Spread of Misinformation and Fact-Checking Content during the Covid-19 Pandemic

In the context of the Covid-19 pandemic, the consequences of misinformation are a matter of life and death. Correcting misconceptions and false beliefs are important for injecting reliable information about the outbreak. Fact-checking organisations produce content with the aim of reducing misinformation spread, but our knowledge of its impact on misinformation is limited. In this paper, we explore the relation between misinformation and fact-checking spread during the Covid-19 pandemic. We specifically follow misinformation and fact-checks emerging from December 2019 to early May 2020. Through a combination of spread variance analysis, impulse response modelling and causal analysis, we show similarities in how misinformation and fact-checking information spread and that fact-checking information has a positive impact in reducing misinformation. However, we observe that its efficacy can be reduced, due to the general amount of online misinformation and the short-term spread of fact-checking information compared to misinformation

Antileishmanial potential of a marine sponge Haliclona oculata against experimental visceral leishmaniasis

Objective: To evaluate the antileishmanial activity of a marine sponge Haliclona oculata. Methods: The crude methanol extract was prepared from the freshly collected sponge and its three fractions were also prepared by maceration method. The antileishmanial activity of these extract and fractions was tested against Leishmania donovani. Results: The antileishmanial activity was tested both in vitro and in vivo. The crude methanol extract exerted almost complete inhibition of promastigotes (81.0% ± 6.9%) and 78.8% ± 5.2% inhibition of intracellular amastigotes at 100 μg/mL with IC50 values of 29.5 μg/mL and 40.6 μg/mL, respectively. The treatment of 500 mg/kg (p.o.) of the crude methanol extract for 5 d for Leishmania donovani infected hamsters resulted in 78.35% ± 10.20% inhibition of intracellular amastigotes. At a lower dose (250 mg/kg), it exhibited poor efficacy. Among the fractions, highest in vitro (>75%) and in vivo (84.3% ± 10.2%) antileishmanial activity was observed in n-chloroform fraction with IC50 values of 54.2 μg/mL and 61 μg/mL against promastigotes and intracellular amastigotes, respectively. Hexane fraction and n-butanol (both insoluble and soluble) fractions were found inactive in vitro and in vivo. Conclusions: Our findings indicate that this marine sponge has the potential to provide new insight toward development of an effective antileishmanial agent and, hence, more exhaustive studies are needed for exploiting the vast marine resources of the world to combat the scourge of several parasitic diseases

Impact of oral palonosetron in improving quality of life as compared to other oral 5-HT3 antagonists in delayed chemotherapy induced nausea and vomiting in patients of head and neck cancer

Background: Chemotherapy induced nausea and vomiting (CINV) remains one of the most common and debilitating complications of highly emetogenic chemotherapy (HEC). This study was undertaken to evaluate palanosetron against other 5-HT3 receptor antagonists in preventing delayed CINV with the aim of achieving complete response (CR) and improving quality of life (QoL).Methods: This was a prospective, observational study conducted on 75 histopathologically proven patients of squamous cell carcinoma of Head and Neck (H&N), who came to the Department of Radiation Oncology, Gandhi Medical College and Hamidia Hospital, Bhopal from January to December 2015. Standard protocol based chemotherapy containing highly emetogenic cisplatin based chemotherapy was administered to all the patients. For prevention of delayed chemotherapy induced nausea and vomiting all patients were prescribed oral 5-HT3 antagonists. Oral Ondansetron 4mg TDS was given to cohort 1, oral Granisetron 1 mg BD to cohort2 and oral Palanosetron 0.5mg OD was given to cohort 3. They were graded as complete response when they did not have complains of nausea and vomiting.Results: In Ondansetron, Granisetron and in Palanosetron cohort 29%, 53% and 98% patients had complete response.Conclusions: Palanosetron appears superior. Our study was conducted on handfull of patients and compared palanosetron against only two 5-HT3 receptor antagonists, so a larger study is suggested to establish the efficacy and better response of palanosetron