On Identifying Hashtags in Disaster Twitter Data

Tweet hashtags have the potential to improve the search for information during disaster events. However, there is a large number of disaster-related tweets that do not have any user-provided hashtags. Moreover, only a small number of tweets that contain actionable hashtags are useful for disaster response. To facilitate progress on automatic identification (or extraction) of disaster hashtags for Twitter data, we construct a unique dataset of disaster-related tweets annotated with hashtags useful for filtering actionable information. Using this dataset, we further investigate Long Short Term Memory-based models within a Multi-Task Learning framework. The best performing model achieves an F1-score as high as 92.22%. The dataset, code, and other resources are available on Github

CrisisMatch: Semi-Supervised Few-Shot Learning for Fine-Grained Disaster Tweet Classification

The shared real-time information about natural disasters on social media platforms like Twitter and Facebook plays a critical role in informing volunteers, emergency managers, and response organizations. However, supervised learning models for monitoring disaster events require large amounts of annotated data, making them unrealistic for real-time use in disaster events. To address this challenge, we present a fine-grained disaster tweet classification model under the semi-supervised, few-shot learning setting where only a small number of annotated data is required. Our model, CrisisMatch, effectively classifies tweets into fine-grained classes of interest using few labeled data and large amounts of unlabeled data, mimicking the early stage of a disaster. Through integrating effective semi-supervised learning ideas and incorporating TextMixUp, CrisisMatch achieves performance improvement on two disaster datasets of 11.2\% on average. Further analyses are also provided for the influence of the number of labeled data and out-of-domain results.Comment: Accepted by ISCRAM 202

Keyphrase Extraction from Disaster-related Tweets

While keyphrase extraction has received considerable attention in recent years, relatively few studies exist on extracting keyphrases from social media platforms such as Twitter, and even fewer for extracting disaster-related keyphrases from such sources. During a disaster, keyphrases can be extremely useful for filtering relevant tweets that can enhance situational awareness. Previously, joint training of two different layers of a stacked Recurrent Neural Network for keyword discovery and keyphrase extraction had been shown to be effective in extracting keyphrases from general Twitter data. We improve the model's performance on both general Twitter data and disaster-related Twitter data by incorporating contextual word embeddings, POS-tags, phonetics, and phonological features. Moreover, we discuss the shortcomings of the often used F1-measure for evaluating the quality of predicted keyphrases with respect to the ground truth annotations. Instead of the F1-measure, we propose the use of embedding-based metrics to better capture the correctness of the predicted keyphrases. In addition, we also present a novel extension of an embedding-based metric. The extension allows one to better control the penalty for the difference in the number of ground-truth and predicted keyphrasesComment: 12 pages, 7 figure

Comparative analysis of expressed sequence tags from three castes and two life stages of the termite Reticulitermes flavipes

<p>Abstract</p> <p>Background</p> <p>Termites (Isoptera) are eusocial insects whose colonies consist of morphologically and behaviorally specialized castes of sterile workers and soldiers, and reproductive alates. Previous studies on eusocial insects have indicated that caste differentiation and behavior are underlain by differential gene expression. Although much is known about gene expression in the honey bee, <it>Apis mellifera</it>, termites remain relatively understudied in this regard. Therefore, our objective was to assemble an expressed sequence tag (EST) data base for the eastern subterranean termite, <it>Reticulitermes flavipes</it>, for future gene expression studies.</p> <p>Results</p> <p>Soldier, worker, and alate caste and two larval cDNA libraries were constructed, and approximately 15,000 randomly chosen clones were sequenced to compile an EST data base. Putative gene functions were assigned based on a BLASTX Swissprot search. Categorical <it>in silico </it>expression patterns for each library were compared using the R-statistic. A significant proportion of the ESTs of each caste and life stages had no significant similarity to those in existing data bases. All cDNA libraries, including those of non-reproductive worker and soldier castes, contained sequences with putative reproductive functions. Genes that showed a potential expression bias among castes included a putative antibacterial humoral response and translation elongation protein in soldiers and a chemosensory protein in alates.</p> <p>Conclusions</p> <p>We have expanded upon the available sequences for <it>R. flavipes </it>and utilized an <it>in silico </it>method to compare gene expression in different castes of an eusocial insect. The <it>in silico </it>analysis allowed us to identify several genes which may be differentially expressed and involved in caste differences. These include a gene overrepresented in the alate cDNA library with a predicted function of neurotransmitter secretion or cholesterol absorption and a gene predicted to be involved in protein biosynthesis and ligase activity that was overrepresented in the late larval stage cDNA library. The EST data base and analyses reported here will be a valuable resource for future studies on the genomics of <it>R. flavipes </it>and other termites.</p

Classifying Scientific Publications Using Abstract Features

Article discussing classifying scientific publications using abstract features

Competition and innovation in the financial sector:Evidence from the rise of FinTech start-ups

This paper provides new evidence on the effects of entry on incumbents’ incentives to innovate by examining the rise of FinTech innovations over the period 2000-2016. We employ machine learning algorithms to classify a large sample of patent applications into five types of FinTech innovations. We then show that greater competition from innovators outside the financial sector increases the probability that incumbent financial firms will innovate. Our identification strategy exploits the variation over time in the share of FinTech patent applications by non-financial start-ups relative to incumbent financial firms, as a proxy for competitive pressures from outside the financial industry. We also find that this increased competition results in a higher number of FinTech patent applications by financial incumbents relative to non-financial ones, especially when the FinTech innovations are more important, as proxied by the number of their future patent citations.Irish Research CouncilOpen Access funding provided by the IReL ConsortiumTo check citing and date details in 6