The detection and location estimation of disasters using Twitter and the identification of Non-Governmental Organisations using crowdsourcing

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Evidential estimation of event locations in microblogs using the Dempster-Shafer theory

Detecting real-world events by following posts in microblogs has been the motivation of numerous recent studies. In this work, we focus on the spatio-temporal characteristics of events detected in microblogs, and propose a method to estimate their locations using the Dempster-Shafer theory. We utilize three basic location-related features of the posts, namely the latitude-longitude metadata provided by the GPS sensor of the user's device, the textual content of the post, and the location attribute in the user profile, as three independent sources of evidence. Considering this evidence in a complementary way, we apply combination rules in the Dempster-Shafer theory to fuse them into a single model, and estimate the whereabouts of a detected event. Locations are treated at two levels of granularity, namely, city and town. Using the Dempster-Shafer theory to solve this problem allows uncertainty and missing data to be tolerated, and estimations to be made for sets of locations in terms of upper and lower probabilities. We demonstrate our solution using public tweets on Twitter posted in Turkey. The experimental evaluations conducted on a wide range of events including earthquakes, sports, weather, and street protests indicate higher success rates than the existing state of the art methods

A survey on location estimation techniques for events detected in Twitter

Detection of events using voluntarily generated content in microblogs has been the objective of numerous recent studies. One essential challenge tackled in these studies is estimating the locations of events. In this paper, we review the state-of-the-art location estimation techniques used in the localization of events detected in microblogs, particularly in Twitter, which is one of the most popular microblogging platforms worldwide. We analyze these techniques with respect to the targeted event type, granularity of estimated locations, location-related features selected as sources of spatial evidence, and the method used to make aggregate decisions based on the extracted evidence. We discuss the strengths and advantages of alternative solutions to various problems related to location estimation, as well as their preconditions and limitations. We examine the most widely used evaluation methods to analyze the accuracy of estimations and present the results reported in the literature. We also discuss our findings and highlight important research challenges that may need further attention

Semantic Expansion of Tweet Contents for Enhanced Event Detection in Twitter

This paper aims to enhance event detection methods in a micro-blogging platform, namely Twitter. The enhancement technique we propose is based on lexico-semantic expansion of tweet contents while applying document similarity and clustering algorithms. Considering the length limitations and idiosyncratic spelling in Twitter environment, it is possible to take advantage of word similarities and to enrich texts with similar words. The semantic expansion technique we implement is based on syntagmatic and paradigmatic relationships between words, extracted from their co-occurrence statistics. As our technique does not depend on an existing ontology or a lexicon database such as WordNet, it should be applicable for any language. The proposed technique is applied on a tweet set collected for three days from the users in Turkey. The results indicate earlier detection of events and improvements in accuracy

Incremental clustering with vector expansion for online event detection in microblogs

Identifying similarities in microblog posts for event detection poses challenges due to short texts with idiosyncratic spellings, irregular writing styles, abbreviations and synonyms. In order to overcome these challenges, we present an enhancement to the incremental clustering techniques by detecting similar terms in microblog posts in a temporal context. We devise an unsupervised method to measure the similarities online using co-occurrence-based techniques and use them in a vector expansion process. The results of our evaluation performed on a tweet set indicate that the proposed vector expansion method helps identify similarities in tweets despite differences in their content. This facilitates the clustering of tweets and detection of events with higher accuracy without incurring a high execution cost

Towards interoperable and composable trajectory simulations: an ontology-based approach

Trajectory simulation is a software module that computes the flight path and flight parameters of munitions. It is used throughout the engineering process, including simulations for studying the design trade-offs, to mission simulations for defended area analysis. In this wide application domain, reuse has always been one of the challenges of the trajectory simulation community. We apply an ontology-based simulation development methodology to fulfil the functional requirements of a trajectory simulation while targeting reuse through interoperability and composability. Trajectory Simulation ONTology (TSONT) has been constructed as a simulation conceptual model for trajectory simulations. Based on the knowledge captured in TSONT, a domain-oriented reuse methodology has been leveraged to develop HLA-compliant trajectory simulations. A trajectory simulation federate was developed by conforming to the simulation object model based on TSONT. This paper demonstrates our approach to achieve composable and interoperable simulations over a case study in which a trajectory simulation federate serves in a variety of federations that have been constructed

A platform for agent behavior design and multi agent orchestration

Agents show considerable promise as a new paradigm for software development. However for wider adoption and deployment of agent technology, powerful design and development tools are needed. Such tools should empower software developers to cater agent solutions more efficiently and at a lower cost for their customers with rapidly changing requirements and differing application specifications

Topic Detection Approaches in Identifying Topics and Events from Arabic Corpora

© 2018 The Authors. Published by Elsevier B.V. How can we know what is going on in the world with a click of a button? With the increase of digital data everywhere, it is becoming difficult to categorize and retrieve information from such huge data. Topic detection is considered a powerful way to mine data and relate similar documents together. Although the Arabic content on the web is increasing every day, the application of topic detection on Arabic text is not up to this increase. In this paper we are investigating famous topic detection techniques, and latest significant scholarly articles related to topic detection in general and in the Arabic domain in specific. This survey paper will help researchers interested in the domain of topic detection to be familiar with commonly used techniques and updated with the latest technologies in this area