Crowdsourcing

About: Crowdsourcing is a research topic. Over the lifetime, 12889 publications have been published within this topic receiving 230638 citations.

Crowdsourcing and the crisis-affected community

Abstract: This article reports on Mission 4636, a real-time humanitarian crowdsourcing initiative that processed 80,000 text messages (SMS) sent from within Haiti following the 2010 earthquake It was the first time that crowdsourcing (microtasking) had been used for international relief efforts, and is the largest deployment of its kind to date This article presents the first full report and analysis of the initiative looking at the accuracy and timeliness in creating structured data from the messages and the collaborative nature of the process Contrary to all previous papers, studies and media reports about Mission 4636, which have typically chosen to exclude empirical analyses and the involvement of the Haitian population, it is found that the greatest volume, speed and accuracy in information processing was by Haitian nationals, the Haitian diaspora, and those working closest with them, and that no new technologies played a significant role It is concluded that international humanitarian organizations have been wrongly credited for large-scale information processing initiatives (here and elsewhere) and that for the most part they were largely just witnesses to crisis-affected communities bootstrapping their own recovery through communications technologies The particular focus is on the role of the diaspora, an important population that are increasingly able to contribute to response efforts thanks to their increased communication potential It is recommended that future humanitarian deployments of crowdsourcing focus on information processing within the populations they serve, engaging those with crucial local knowledge wherever they happen to be in the world

Crowdsourcing for Affective Annotation of Video: Development of a Viewer-reported Boredom Corpus

Abstract: Predictions of viewer affective response to video are an important source of information that can be used to enhance the performance of multimedia retrieval and recommendation systems The development of algorithms for robust prediction of viewer affective response requires corpora accompanied by appropriate ground truth We report on the development a new corpus to be used to evaluate algorithms for prediction of viewer-reported boredomWe make use of crowdsourcing in order to address two shortcomings of previous affective video corpora: small number of annotators and gap between annotators and target viewer group We describe the design of the Mechanical Turk setup that we used to generate the affective annotations for the corpus We discuss specific issues that arose and how we resolve them and then present an analysis of the annotations collected The paper closes with a list of recommended practices for the collection of self-reported affective annotations using crowdsourcing techniques and an outlook on future work

Practical Attribute-Based Multi-Keyword Search Scheme in Mobile Crowdsourcing

Abstract: Cloud-based mobile crowdsourcing has been an attractive solution to provide data storage and share services for resource-limited mobile devices in a privacy-preserving manner, but how to enable mobile users to issue search queries and achieve fine-grained access control over ciphertexts simultaneously is still a big challenge for various circumstances. Although the ciphertext-policy attribute-based keyword search technology combining attribute-based encryption with searchable encryption has become a hot research topic, it just deals with equivalent attributes rather than more practical attribute comparisons, like “greater than” or “less than.” In this paper, we devise a practical cryptographic primitive called attribute-based multi-keyword search scheme to support comparable attributes through utilizing 0-encoding and 1-encoding. Formal security analysis proves that our scheme is selectively secure against chosen-keyword attack in generic bilinear group model and extensive experiments using real-world dataset demonstrate that our scheme can drastically decrease both computational and storage costs.

FdGars: Fraudster Detection via Graph Convolutional Networks in Online App Review System

Abstract: Online review system enables users to submit reviews about the products. However, the openness of Internet and monetary rewards for crowdsourcing tasks stimulate a large number of fraudulent users to write fake reviews and post advertisements to interfere the rank of apps. Existing methods for detecting spam reviews have been successful but they usually aims at e-commerce (e.g. Amazon, eBay) and recommendation (e.g. Yelp, Dianping) systems. Since the behaviors of fraudulent users are complexity and varying across different review platforms, existing methods are not suitable for fraudster detection in online app review system. To shed light on this question, we are among the first to analyze the intentions of fraudulent users from different review platforms and categorize them by utilizing characteristics of contents (similarity, special symbols) and behaviors (timestamps, device, login status). With a comprehensive analysis of spamming activities and relationships between normal and malicious users, we design and present FdGars, the first graph convolutional network approach for fraudster detection in online app review system. Then we evaluate FdGars on real-world large-scale dataset (with 82,542 nodes and 42,433,134 edges) from Tencent App Store. The result demonstrates that F1-score of FdGars can achieve 0.938+, which outperforms several baselines and state-of-art fraudsters detecting methods. Moreover, we deploy FdGars on Tencent Beacon Anti-Fraud Platform to show its effectiveness and scalability. To the best of our knowledge, this is the first work to use graph convolutional networks for fraudster detection in the large-scale online app review system. It is worth to mention that FdGars can uncover malicious accounts even the data lack of labels in anti-spam tasks.

Modeling brand post popularity dynamics in online social networks

Abstract: Today's social media platforms are excellent vehicles for businesses to build and foster relationship with customers. Companies create official fan pages on social network websites to provide customers with information about their brands, products, promotions, and more. Customers can become fans of these pages, and like, reply, share or mark the brand post as favorite. Marketing departments are using these activities to crowdsource marketing and increase brand awareness and popularity. Understanding how crowdsourcing oriented marketing and promotion evolves would be helpful in managing such campaigns. In this paper, we adopt a multidimensional point process methodology to study crowd engagement activities and interactions. Specifically, we investigate the brand post popularity as a joint probability function of time and number of followers. One-dimensional and two-dimensional Hawkes point process models are calibrated to simulate popularity growth patterns of brand post contents on Twitter. Our results suggest that the two-dimensional point process model provides a good model for understanding such crowdsourcing behavior.