Using semantic clustering to support situation awareness on Twitter: the case of world views

Kingston, Charlie; Nurse, Jason R. C.; Agrafiotis, Ioannis; Milich, Andrew

doi:10.1186/s13673-018-0145-6

Cited by 5 publications

(3 citation statements)

References 38 publications

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“…Another feature that may be worth experimenting with is improving the situational awareness presented in the tool. There has been research in this area (e.g., [12,10]) which may be incorporated, to provide the user with a more immersive experience.…”

Section: Discussionmentioning

confidence: 99%

ToARist: An Augmented Reality Tourism App created through User-Centred Design

Williams¹,

Yao²,

Nurse³

2017

Electronic Workshops in Computing

Self Cite

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Through Augmented Reality (AR), virtual graphics can transform the physical world. This offers benefits to mobile tourism, where points of interest (POIs) can be annotated on a smartphone screen. Although several of these applications exist, usability issues can discourage adoption. User-centred design (UCD) solicits frequent feedback, often contributing to usable products. While AR mock-ups have been constructed through UCD, we develop a novel and functional tourism app. We solicit requirements through a synthesis of domain analysis, tourist observation and semi-structured interviews. Through four rounds of iterative development, users test and refine the app. The final product, dubbed ToARist, is evaluated by 20 participants, who engage in a tourism task around a UK city. Users regard the system as usable, but find technical issues can disrupt AR. We finish by reflecting on our design and critiquing the challenges of a strict user-centred methodology.

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Section: Discussionmentioning

confidence: 99%

ToARist: An Augmented Reality Tourism App created through User-Centred Design

Williams¹,

Yao²,

Nurse³

2017

Electronic Workshops in Computing

Self Cite

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“…The primary contribution of the proposed model is an attention executive module, which is responsible to detect changes in attention on specific control loops based on changes in priorities. The authors of [30] develop a model that uses social media posts and process them, by clustering consistent posts, in the way that a user can gain more better insights by reading different views (or world view) that the system has generated. This approach is not particular to model situation awareness for agents; however, people can assess a situation, described through posts, better by reading the world views about the posts on tweeter or any other social media platform that exploits the proposed technique.…”

Section: Previous Workmentioning

confidence: 99%

Situation awareness modeling for emergency management on offshore platforms

Danial

Smith

Khan

et al. 2019

Hum. Cent. Comput. Inf. Sci.

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IntroductionThe present work proposes a model based on Markov Logic Network (MLN) [16] for representing emergency situations involving smoke and fire on offshore petroleum platforms. The model is tested for two important situations, FIRE and EVACUATE. In the FIRE situation, fire is observed due to smoke at some place on the platform, and all workers need to muster to their primary muster station. In the EVACUATE situation, the fire is escalated so that some escape routes to the primary muster station are blocked and all personnel needs to muster at the lifeboat or alternative muster station. The purpose of this work is to have a model that can be used by a software agent so that the agent can exhibit human-like situation awareness (SA). Such agents can subsequently be used, for example, in training simulators to enrich trainees' experience by showing them various scenarios in which the agent shows recognition of different situations (to makes various decisions). A participant can learn from the agent what information is important in a given scenario for correct SA.Representing the emergency response of agents operating in a virtual environment (VE) is a challenging and active research area. Emergencies on board can arise from Abstract Situation awareness is the first and most important step in emergency management. It is a dynamic step involving evolving conditions and environments. It is an area of active research. This study presents a Markov Logic Network to model SA focusing on fire accidents and emergency evacuation. The model has been trained using empirical data obtained from case studies. The case studies involved human participants who were trained for responding to emergencies involving fire and smoke using a virtual environment. The simulated (queried) and empirical findings are reasonably consistent. The proposed model enables implementing an agent that exploits environmental cues and cognitive states to determine the type of emergency currently being faced. Considering each emergency type as a situation, the model can be used to develop a repertoire of situations for agents so that the repertoire can act as an agent's experience for later decision-making. which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

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“…The computational overhead was not minimized. Subject-Verb-Object Semantic Suffix Tree Clustering (SVOSSTC) was presented in [6] to reduce the time needed for grouping twitter data with higher accuracy. However, the ratio of number of twitter data that are exactly clustered was not enough.…”

Section: Introductionmentioning

confidence: 99%

Information Criteria Based Partitioned Iterative X-means Dice Correlation Clustering Algorithm for Big Geo-social Data

Sreeku¹,

Abraham²,

Premalatha³

et al. 2022

Preprint

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Geo-social data is location-based social media data which is generated by people on social network (i.e. face book, twitter etc.,) that is related to specific locations. There are lot of social users who are generates very large amount of data called “Big Data” that is difficult to be analyzed and make real-time decisions. Few research works have been designed for clustering geo-social data using different techniques. However, clustering performance of conventional algorithms was not higher to exactly find frequently visited location of users in social network when taking big geo-social dataset as input. In order to overcome such drawbacks, a Focused Information Criterion based Partitioned Iterative X-means Dice Correlation Data Clustering (FIC-PIXDCDC) Method is proposed in this work. The FIC-PIXDCDC Method groups the similar geo-social data with higher accuracy and lesser time. In FIC-PIXDCDC method, geo-social data (i.e., user, location and time) from Weeplaces dataset is initially taken as an input. After obtaining input, FIC-PIXDCDC method chooses number of clusters and centroids randomly. Then, FIC-PIXDCDC calculates dice correlation between each input geo-social data and cluster centroids. Subsequently, FIC-PIXDCDC method applies Focused Information Criterion to construct optimal number of clusters for a given big dataset. This process of FIC-PIXDCDC method is repetitive until no deviation in cluster centroids. Appropriately, FIC-PIXDCDC strategy group's interrelated geo-social information along with accuracy at higher rate and lower time to accurately find area's data of regularly visited clients in social network. Trial assessment of FIC-PIXDCDC strategy is completed on elements, for example, clustering time, clustering precision, error rate as for number of geo-social information.

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Using semantic clustering to support situation awareness on Twitter: the case of world views

Cited by 5 publications

References 38 publications

ToARist: An Augmented Reality Tourism App created through User-Centred Design

ToARist: An Augmented Reality Tourism App created through User-Centred Design

Situation awareness modeling for emergency management on offshore platforms

Information Criteria Based Partitioned Iterative X-means Dice Correlation Clustering Algorithm for Big Geo-social Data

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