Crowdsourced network measurements: Benefits and best practices

Hirth, Matthias; Hoßfeld, Tobias; Mellia, Marco; Schwartz, Christian; Lehrieder, Frank

doi:10.1016/j.comnet.2015.07.003

Cited by 24 publications

(13 citation statements)

References 29 publications

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“…Similar mobile measurements are carried out within an app in the background without the active involvement of endusers, but also involve the mass of end users in a crowdsourced sense. Crowdsourced measurements take into account a special type of crowdsourcing: the collection and processing of data that are measured by the crowd [3].…”

Section: Introductionmentioning

confidence: 99%

Valid Statements by the Crowd: Statistical Measures for Precision in Crowdsourced Mobile Measurements

Wamser

Seufert

Hall³

et al. 2021

Network

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Crowdsourced network measurements (CNMs) are becoming increasingly popular as they assess the performance of a mobile network from the end user’s perspective on a large scale. Here, network measurements are performed directly on the end-users’ devices, thus taking advantage of the real-world conditions end-users encounter. However, this type of uncontrolled measurement raises questions about its validity and reliability. The problem lies in the nature of this type of data collection. In CNMs, mobile network subscribers are involved to a large extent in the measurement process, and collect data themselves for the operator. The collection of data on user devices in arbitrary locations and at uncontrolled times requires means to ensure validity and reliability. To address this issue, our paper defines concepts and guidelines for analyzing the precision of CNMs; specifically, the number of measurements required to make valid statements. In addition to the formal definition of the aspect, we illustrate the problem and use an extensive sample data set to show possible assessment approaches. This data set consists of more than 20.4 million crowdsourced mobile measurements from across France, measured by a commercial data provider.

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Section: Introductionmentioning

confidence: 99%

Valid Statements by the Crowd: Statistical Measures for Precision in Crowdsourced Mobile Measurements

Wamser

Seufert

Hall³

et al. 2021

Network

Self Cite

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“…To exploratively investigate which features of a tweet are influencing credibility assessments, we set up an online survey using Figure Eight (https://make.figure-eight.com), a crowdsourcing platform for data annotations and ratings. By using Figure Eight we were able to recruit a large sample consisting of older and more diverse participants compared to common undergraduate samples [42], [43]. Crowd working platforms like Figure Eight are widely and successfully used, especially for tasks with rating or labelling content [44], [45], [46].…”

Section: Methodsmentioning

confidence: 99%

The Impact of Twitter Features on Credibility Ratings - An Explorative Examination Combining Psychological Measurements and Feature Based Selection Methods

Meinert¹,

Aker²,

Krämer³

2019

Proceedings of the Annual Hawaii International Conference on System Sciences

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In a post-truth age determined by Social Media channels providing large amounts of information of questionable credibility while at the same time people increasingly tend to rely on online information, the ability to detect whether content is believable is developing into an important challenge. Most of the work in that field suggested automated approaches to perform binary classification to determine information veracity. Recipients´ perspectives and multidimensional psychological credibility measurements have rarely been considered. To fill this gap and gain more insights into the impact of a tweet´s features on perceived credibility, we conducted a survey asking participants (N=2626) to rate the credibility of crisis-related tweets. The resulting 24.823 ratings were used for an explorative feature selection analysis revealing that mostly meta-related features like the number of followers of the author, the count of tweets produced and the ratio of tweet number and days since account creation affect credibility judgments.

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“…The term crowd engineering is borrowed from crowd sourcing: Howe [3] defines crowd sourcing as a composition of 'crowd' and 'outsourcing', i.e., the task of distributing a function previously assigned internally to employees to a heterogeneous network. In our context, crowd sourcing does not equal outsourcing a function to an undefined network, but to a network of acknowledged participantsan important practice in today's design and engineering contexts [4], [5]. The concept of crowd engineering was implemented by VPE for the first time in 2016 [1].…”

Section: Introductionmentioning

confidence: 99%

Combining Collaborative User Experience Design With Crowd Engineering: A Problem-Based Lab Course for (Under-)Graduate Students

Kerpen¹,

Juresa²,

Forte³

et al. 2021

DS 110: Proceedings of the 23rd International Conference on Engineering and Product Design Education (EPDE 2021)

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This paper proposes a university course centred around a problem-based laboratory called "Crowd Engineering (CE)". As a result of the COVID-19 pandemic, collaborative work had to be moved predominantly into the virtual space. Thus, the need for virtual collaborative work in teaching increased significantly. CE is a joint remote collaboration by two academic institutions working together in a transfer-oriented, publicly funded project. In the course, design and product engineering students learnt to cope with today's complex problems. They acquire skills to think beyond subject and system boundaries and are equipped with these job-related qualities already during university education by solving a crowd-sourced real-world task. Students worked with various tools to arrive at an initial design proposal that is supposed to be implemented in a future physical prototype. This paper describes the collaboration context of both university partners, concept development and implementation of the laboratory course and lessons learned from the collaboration.

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Crowdsourced network measurements: Benefits and best practices

Cited by 24 publications

References 29 publications

Valid Statements by the Crowd: Statistical Measures for Precision in Crowdsourced Mobile Measurements

Valid Statements by the Crowd: Statistical Measures for Precision in Crowdsourced Mobile Measurements

The Impact of Twitter Features on Credibility Ratings - An Explorative Examination Combining Psychological Measurements and Feature Based Selection Methods

Combining Collaborative User Experience Design With Crowd Engineering: A Problem-Based Lab Course for (Under-)Graduate Students

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