On quality of event localization from social network feeds

Giridhar, Prasanna; Abdelzaher, Tarek; George, Jemin; Kaplan, Lance

doi:10.1109/percomw.2015.7133997

Cited by 29 publications

(14 citation statements)

References 4 publications

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“…Hence, the list of location keywords for each sensor can be populated without manual intervention, allowing the service to scale to large numbers of sensors. Our prior work also addressed the challenge of translating text references in tweets to location coordinates, which can also be used in matching location of sensors to tweets [19].…”

Section: Discussion and Lessons Learnedmentioning

confidence: 99%

ClariSense+: An enhanced traffic anomaly explanation service using social network feeds

Giridhar

Amin

Abdelzaher

et al. 2016

Pervasive and Mobile Computing

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The explosive growth in social networks that publish real-time content begs the question of whether their feeds can complement traditional sensors to achieve augmented sensing capabilities. One such capability is to explain anomalous sensor readings. In our previous conference paper, we built an automated anomaly clarification service, called ClariSense, with the ability to explain sensor anomalies using social network feeds (from Twitter). In this extended work, we present an enhanced anomaly explanation system that augments our base algorithm by considering both (i) the credibility of social feeds and (ii) the spatial locality of detected anomalies. The work is geared specifically for describing small-footprint anomalies, such as vehicular traffic accidents. The original system used information gain to select more informative microblog items to explain physical sensor anomalies. In this paper, we show that significant improvements are achieved in our ability to explain small-footprint anomalies by accounting for information credibility and further discriminating among highinformation-gain items according to the size of their spatial footprint. Hence, items that lack sufficient corroboration and items whose spatial footprint in the blogosphere is not specific to the approximate location of the physical anomaly receive less consideration. We briefly demonstrate the workings of such a system by considering a variety of realworld anomalous events, and comparing their causes, as identified by ClariSense+, to ground truth for validation. A more systematic evaluation of this work is done using vehicular traffic anomalies. Specifically, we consider real-time traffic flow feeds shared by the California traffic system. When flow anomalies are detected, our system automatically diagnoses their root cause by correlating the anomaly with feeds on Twitter. For evaluation purposes, the identified cause is then retroactively compared to official traffic and incident reports that we take as ground truth. Results show a great correspondence between our automatically selected explanations and ground-truth data.

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Section: Discussion and Lessons Learnedmentioning

confidence: 99%

ClariSense+: An enhanced traffic anomaly explanation service using social network feeds

Giridhar

Amin

Abdelzaher

et al. 2016

Pervasive and Mobile Computing

Self Cite

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“…The tagged GPS coordinates have relatively high location accuracy, especially for mobile phones, and the estimated median horizontal error ranges from 5 to 8.5 m (Zandbergen & Barbeau, ). However, Giridhar, Abdelzaher, George, and Kaplan () found that that only 2–3% of all tweets were geo‐located, which poses certain challenges for researchers adopting Twitter as a valid data source for experiments due to data sparsity. In fact, the way Twitter data are collected acts as a major indicator for the geo‐tagged percentage.…”

Section: Methodsmentioning

confidence: 99%

A spatial‐temporal‐semantic approach for detecting local events using geo‐social media data

Huang

2019

Transactions in GIS

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“…This approach uses a hybrid NER procedure that has an annotation step in order to annotate the locations and events in the tweet text. Giridhar et al identify possible events, by clustering apparent locations, via POS tagging and grammar-based rules [20]. They also analyse the quality of the extracted locations.…”

Section: Related Workmentioning

confidence: 99%

An Approach for Detecting Traffic Events Using Social Media

Gutierrez

Figueiras

Oliveira

et al. 2016

Emerging Trends and Advanced Technologies for Computational Intelligence

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Nowadays almost everyone has access to mobile devices that offer better processing capabilities and access to new information and services, the Web is undoubtedly the best tool for sharing content, especially through social networks. Web content enhanced by mobile capabilities, enable the gathering and aggregation of information that can be useful for our everyday lives as, for example, in urban mobility where personalized real-time traffic information, can heavily influence users' travel habits, thus contributing for a better way of living. Current navigation systems fall short in several ways in order to satisfy the need to process and reason upon such volumes of data, namely, to accurately provide information about urban traffic in real-time and the possibility to personalize the information presented to users. The work presented here describes an approach to integrate, fuse and process tweet messages from traffic agencies, with the objective of detecting the geographical span of traffic events, such as accidents or road works. Tweet messages are considered in this work given their uniqueness, their real time nature, which may be used to quickly detect a traffic event, and their simplicity. We also address some imprecisions ranging from lack of geographical information, imprecise and ambiguous toponyms, overlaps and repetitions as well as visualization to our data set in the UK, and a qualitative study on the use of the approach using tweets in other languages, such as Greek. Finally, we present an application scenario, where traffic information is processed from tweets massages, triggering personalized notifications to users through Google Cloud Messaging on Android smartphones. The work presented here is still part of on-going work. Results achieved so far do not address the final conclusions but form the basis for the formalization of a domain knowledge along with the urban mobility services.

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On quality of event localization from social network feeds

Cited by 29 publications

References 4 publications

ClariSense+: An enhanced traffic anomaly explanation service using social network feeds

ClariSense+: An enhanced traffic anomaly explanation service using social network feeds

A spatial‐temporal‐semantic approach for detecting local events using geo‐social media data

An Approach for Detecting Traffic Events Using Social Media

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