Social Signal Processing for Real-Time Situational Understanding: A Vision and Approach

Jayarajah, Kasthuri; Yao, Shuochao; Mutharaju, Raghava; Misra, Archan; Mel, Geeth de; Skipper, J.; Abdelzaher, Tarek; Kolodny, Michael A.

doi:10.1109/mass.2015.89

Cited by 3 publications

(2 citation statements)

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“…Besides social media, Volunteered Geographic Information (VGI) (Goodchild 2007) frameworks is also used to collect data from volunteers and map the impacts or aftermath of natural disaster events through a developed web or mobile application (Longueville et al 2010). VGI frameworks can also assist military missions or emergency responses to obtain real-time situational awareness of group behaviors and physical events (Jayarajah et al 2015), such as the Federal Emergency Management Agency (FEMA) app (FEMA 2019).…”

Section: Social Sensingmentioning

confidence: 99%

Spatiotemporal event detection: a review

Bambacus

Cervone

et al. 2020

International Journal of Digital Earth

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The advancements of sensing technologies, including remote sensing, in situ sensing, social sensing, and health sensing, have tremendously improved our capability to observe and record natural and social phenomena, such as natural disasters, presidential elections, and infectious diseases. The observations have provided an unprecedented opportunity to better understand and respond to the spatiotemporal dynamics of the environment, urban settings, health and disease propagation, business decisions, and crisis and crime. Spatiotemporal event detection serves as a gateway to enable a better understanding by detecting events that represent the abnormal status of relevant phenomena. This paper reviews the literature for different sensing capabilities, spatiotemporal event extraction methods, and categories of applications for the detected events. The novelty of this review is to revisit the definition and requirements of event detection and to layout the overall workflow (from sensing and event extraction methods to the operations and decision-supporting processes based on the extracted events) as an agenda for future event detection research. Guidance is presented on the current challenges to this research agenda, and future directions are discussed for conducting spatiotemporal event detection in the era of big data, advanced sensing, and artificial intelligence.

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Section: Social Sensingmentioning

confidence: 99%

Spatiotemporal event detection: a review

Bambacus

Cervone

et al. 2020

International Journal of Digital Earth

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“…Detecting events using social media is a particularly active area and has been since the initial work of Sakaki et al 23 on detecting and tracking Earthquakes from user posted information on Twitter. Due to the low fraction of geocoded posts from Twitter, recent works have studied Location-based Social Networks (e.g., Georgiev et al 4 ) and image-rich platforms that are better geotagged (e.g., Jayarajah et al 24,25 ).…”

Section: Related Workmentioning

confidence: 99%

Can multimodal sensing detect and localize transient events?

Jayarajah

Subbaraju

Athaide

et al. 2018

Ground/Air Multisensor Interoperability, Integration, and Networking for Persistent ISR IX

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With the increased focus on making cities "smarter", we see an upsurge in investment in sensing technologies embedded in the urban infrastructure. The deployment of GPS sensors aboard taxis and buses, smartcards replacing paper tickets, and other similar initiatives have led to an abundance of data on human mobility, generated at scale and available real-time. Further still, users of social media platforms such as Twitter and LBSNs continue to voluntarily share multimedia content revealing in-situ information on their respective localities. The availability of such longitudinal multimodal data not only allows for both the characterization of the dynamics of the city, but also, in detecting anomalies, resulting from events (e.g., concerts) that disrupt such dynamics, transiently. In this work, we investigate the capabilities of such urban sensor modalities, both physical and social, in detecting a variety of local events of varying intensities (e.g., concerts) using statistical outlier detection techniques. We look at loading levels on arriving bus stops, telecommunication records and taxi trips, accrued via the public APIs made available through the local transport authorities from Singapore and New York City, and Twitter/Foursquare check-ins collected during the same period, and evaluate against a set of events assimilated from multiple event websites. In particular, we report on our early findings on (1) the spatial impact evident via each modality (i.e., how far from the event venue is the anomaly still present), and (2) the utility in combining decisions from the collection of sensors using rudimentary fusion techniques. † Work carried out while a Senior Research Engineer at Singapore Management University. Further author information:(Send correspondence to A. M.) A.

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