Trend sensing via Twitter

Yilmaz, Yavuz Selim; Bulut, Muhammed Fatih; Akcora, Cüneyt Gürcan; Bayır, Murat Ali; Demirbaş, Murat

doi:10.1504/ijahuc.2013.056271

Cited by 9 publications

(6 citation statements)

References 25 publications

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“…Natural language analysis highlights are to understand the emotions, directions and understand crow-sourcing with social network information. Some previous work analyzes this information from different amounts of users used in crisis management [1], [5], [15], [36], [37]. Although those research propose new frameworks to manage this information of the works, and there exists different SDK service to analyze natural language, where is a pre-trained model which can detect emotions in the thread of a phrase, and also important to detect addresses and descriptions of the situation where the crisis are.…”

Section: Natural Languagementioning

confidence: 99%

“…Koontanakulvong et al [14] analyze the lessons learned from floods in Bangkok in 2011, and related how the study in the university helps to the people look for a place safe determining the elevation of the water in the city. In Texas was implemented a framework to identify trends in tweeter looking for sensing the emotion of people about some crisis situation [15], this is important for determinate fake news or also tracking the emotion of a certain country in tweets. This work test different situations like the trends in Boston for arts and entertainment or the trends in Dallas for shops.…”

Section: Introductionmentioning

confidence: 99%

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Towards Smart-City Implementation for Crisis Management in Fast-Growing and Unplanned Cities: the Colombian Scenario

et al. 2020

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Natural or human-made disasters could do huge damage in urban areas and eventually could take lives. It is fundamental to get knowledge of the event’s characteristics to dispose of hasty information to help affected people or to prevent all the citizens from the danger zone, and then it will get time to respond to the crisis. Internet of Things (IoT) has a big impact on this kind of situation because a large amount of data through different devices could provide information about the situation, and about the people that are involved in the crisis. In a disaster, one of the big problems adding to the principal crisis is the disinformation, for that reason is necessary to have available and trusty data in case of disaster, also to know the data that provided the information system. To inform the affected people around the crisis event, there is exist some previous works that have used data from sensors, social networks text, or images, to finally be processed [1],[2],[3],[4],[5],[6],[7],[8]. This paper aims to review study-cases where cities implement crisis management platforms, focus on IoT environment where applications use hybrid data to be processed to help citizens in a crisis situation.

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Section: Natural Languagementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Towards Smart-City Implementation for Crisis Management in Fast-Growing and Unplanned Cities: the Colombian Scenario

et al. 2020

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“…As most smart phones have social networking applications installed and enabled access to the data stored in the smart phone, those applications can aide publishing sensing data, as well as retrieving users locations who are (or will be) within the vicinity of a group of sensing tasks. Besides, social networking applications have been pointed as useful enablers in previous studies [10] whereas integration of IoT and social networks requires more investigation [11]. Although it is not mandatory, our framework also considers data publishing layer utilizing social networking applications.…”

Section: A System Architecturementioning

confidence: 99%

“…In each simulation scenario, smart city management authority sends λ task requests per minute to the social networking service where sensing tasks are also uniformly distributed over the terrain. The value of a task is uniformly distributed over [1,5] while a user bid is uniformly distributed over [1,10]. A user shows interest to a task within his/her 30m range [9].…”

Section: A Simulation Settingsmentioning

confidence: 99%

Mobility-aware trustworthy crowdsourcing in cloud-centric Internet of Things

Kantarcı

Mouftah

2014

2014 IEEE Symposium on Computers and Communications (ISCC)

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In the Internet of Things (IoT) era, smart devices that are equipped with various types of sensors can enable access to the IoT architecture through a cloud-inspired service model, namely Sensing-as-a-Service (S 2 aaS). S 2 aaS can provide crowdsourced data to an application running on a cloud platform. The crowdsourced data can be used for several purposes such as public safety. One of the biggest challenges here is the incentive mechanisms for the users who are requested to provide S 2 aaS. In this paper, we propose mobility-aware trustworthy crowdsourcing (MATCS) framework in a cloud-centric IoT architecture which adopts and extends a previous scheme, Trustworthy Sensing for Crowd Management (TSCM) [1] by incorporating user mobilityawareness in the presence of maliciously altered sensing data. MATCS employs a user-centric incentive mechanism which collects sensing data based on an auction procedure. In the auction procedure, MATCS uses users' reputations, bids, current location and their estimated dislocation during crowdsourcing process. Furthermore, in order to investigate the benefits of reputationawareness, we also propose reputation-unaware Mobility-Aware Crowdsourcing (MACS). Performance of MATCS is evaluated via simulations, and it is compared to MACS and a benchmark scheme, which aims at making a compromise between the utilities of the users and the platform by considering neither mobility nor trustworthiness. Simulation results confirm that mobilityawareness improves the utility of the platform significantly whereas combining reputation-awareness and mobility-awareness by MATCS can triple the improvement. Besides, user incomes are not significantly impacted by MACS or MATCS when users are mobile. Furthermore, maliciously altered data ratio can be degraded by 20%∼55% by reputation-awareness in MATCS.

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“…In [5], data collected from vehicular sensor network has been published over social networks with the purpose of environment monitoring. Furthermore, weather information and noise mapping as S 2 aaS applications over Twitter have been proposed in [13].…”

Section: Related Work and Motivationmentioning

confidence: 99%

Trustworthy crowdsourcing via mobile social networks

Kantarcı

Mouftah

2014

2014 IEEE Global Communications Conference

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Use of social network services has been more widespread as mobile social network applications have been developed for smart phones. Besides, smart phone sensing, namely Sensing-as-a-Service (S 2 aaS) provides the front-edge access to the cloud-centric Internet of Things. S 2 aaS can provide crowdsourced data for several purposes such as public safety, crowd management and environment monitoring. Trustworthiness of crowdsourced data is an important challenge in S 2 aaS as maliciously altered data can be misleading for the S 2 aaS customer. Reputation-aware crowdsourcing schemes can address trustworthiness problem however smart phone users moving based on a social network mobility (SNM) model introduce further challenges. In this paper, we propose reputation-and-SNM-aware crowdsourcing scheme which is based on an auction executed at a cloud platform. The cloud platform aims at maximum utility by estimating users' future locations based on their interactions over the social network service so that efficient user-sensing task matching can be done. Furthermore, past and current reputation of the users are taken into account while recruiting smart phone users for given sensing tasks. Our simulations show that reputation-awareness accompanied with SNM-awareness can significantly increase the platform utility by up to 55%. Furthermore, incorporation of reputation-awareness can degrade disinformation probability by more than 70%.

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Trend sensing via Twitter

Cited by 9 publications

References 25 publications

Towards Smart-City Implementation for Crisis Management in Fast-Growing and Unplanned Cities: the Colombian Scenario

Towards Smart-City Implementation for Crisis Management in Fast-Growing and Unplanned Cities: the Colombian Scenario

Mobility-aware trustworthy crowdsourcing in cloud-centric Internet of Things

Trustworthy crowdsourcing via mobile social networks

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