Mining citizen emotions to estimate the urgency of urban issues

Crowdsourcing technology offers exciting possibilities for local governments. Specifically, citizens are increasingly taking part in reporting and discussing issues related to their neighborhood and problems they encounter on a daily basis, such as overflowing trash-bins, broken footpaths and lifts, illegal graffiti, and potholes. Pervasive citizen participation enables local governments to respond more efficiently to these urban issues. This interaction between citizens and municipalities is largely promoted by civic engagement platforms, such as See-Click-Fix, FixMyStreet, CitySourced, and OpenIDEO, which allow citizens to report urban issues by entering free text describing what needs to be done, fixed or changed. In order to develop appropriate action plans and priorities, government officials need to figure out how urgent are the reported issues. In this paper we propose to estimate the urgency of urban issues by mining different emotions that are implicit in the text describing the issue. More specifically, a reported issue is first categorized according to the emotions expressed in it, and then the corresponding emotion scores are combined in order to produce a final urgency level for the reported issue. Our experiments use the SeeClickFix hackathon data and diverse emotion classification algorithms. They indicate that (i) emotions can be categorized efficiently with supervised learning algorithms, and (ii) the use of citizen emotions leads to accurate urgency estimates. Further, using additional features such as the type of issue or its author leads to no further accuracy gains.     

Crowdsourcing the perceived urban built environment via social media: The case of underutilized land

Crowdsourcing the public’s perceptions of the built environment in real time enables more responsive and agile infrastructure and land use planning. Social media has emerged to be an effective platform for citizens, engineers, and planners to communicate opinions and feelings transparently. However, a comprehensive terminological resource of the perceived built environment (BE) for consistent data collection and a specified analytical framework are still lacking, particularly for different underutilized land issues. To fill this knowledge gap, we demonstrate a BE-specific term construction and expansion method specifically for collecting Twitter data and propose a Geo-Topic-Sentiment analytical framework for retrieving and analyzing relevant tweets. We conduct a demonstrative study on un(der)utilized land-related BE terms across ten metropolitan statistical areas in the U.S. Findings reveal spatial variations in contents and sentiments about underutilized land environments, and more localized efforts may be required to address specific land use issues across different urban contexts. The research demonstrates Twitter as a useful platform in crowdsourcing perceived BE and sentiments at fine temporal and spatial scales in a timely manner. It contributes to engineering informatics by investigating the role of social media in environmental planning and proposing integrated domain-specific data analytic approaches for engineering practices.     

Factors affecting detection of a bimodal sour-savory mixture and inter-individual umami taste perception

While basic taste interactions have been the subject of many research studies, there is one combination where data is limited in the literature: sour and umami. This combination is universal in culinary preparations and of key interest to the food industry. Therefore, the primary goal of the present study is to assess how increasing concentrations of acidity (citric acid) affect, if at all, the intensity of a constant concentration of umami (monosodium glutamate, MSG). The secondary goal is to investigate other possible factors in umami taste perception. Here, a crowdsourced cohort of 734 individuals (age range 8–81) tasted and rated the intensity of 50 mM MSG alone, and in combination with citric acid at varying concentrations (1.25 mM, 6.25 mM, 31.25 mM). Participants were also genotyped for the single nucleotide polymorphism rs34160967 in the TAS1R1 gene. The results show a significant decrease in the intensity perception of umami as sour concentration increases (low: p = 0.005, medium: p < 0.001, high: p < 0.001). Situational factors such as participant hunger level and time since last eating also have a significant effect on umami intensity perception. Neither the biological factors of sex, age, and ancestry appear to play a role in umami perception, nor does variation in gene TAS1R1 at rs34160967. These new data contribute to the growing field of taste and sensory interaction by giving evidence that sour suppresses umami taste perception in bi-model samples.     

Crowdsourcing with online quantitative design analysis

Design is a balancing act between people’s competing concerns, design options and design performance. Recently collecting data on such concerns such as sustainability or aesthetics has become possible through online crowdsourcing, particularly in 3d. However, such systems rarely present more than a single design alternative or allow users to change the design and seldom provide quantitative design analysis to gauge design performance. This precludes a more participatory approach including a wider audience and their insight in the design process.To improve the design process we propose a system to assist the design team in exploring the balance of concerns, design options and their performance. We augment a 3d visualisation crowdsourcing environment with quantitative on-demand assessment of design variants run in the cloud. This enables crowdsourced exploration of the design space and its performance. Automated participant tracking and explicit submitted feedback on design options are collated and presented to aid the design team in balancing the demands of urban master planning. We report application of this system to an urban masterplan with Arup.     

Android fine-grained permission control system with real-time expert recommendations

In current Android architecture design, users have to decide whether an app is safe to use or not. Expert users can make savvy decisions to prevent unnecessary privacy breach. However, inexperienced users may not be able to decide correctly. To assist inexperienced users to make a right permission granting decisions, we propose RecDroid. RecDroid is a crowdsourcing recommendation framework that facilitates a user-help-user environment regarding smartphone permission control. In this framework, the responses from expert users are aggregated and recommended to other users. We implement our prototype on Android platform and evaluated the system through simulation and real user study.     

Strategic effort allocation in online innovation tournaments

Online innovation tournaments, such as those hosted by crowdsourcing platforms (e.g., Kaggle), have been widely adopted by firms to evolve creative solutions to various problems. Solvers compete in these tournaments to earn rewards. In such competitive environments, it is imperative that solvers provide creative solutions with minimum effort. This article explores the factors that influence the solvers’ effort allocation decisions in a dynamic tournament setting. Specifically, comprehensive time variant data of teams that participated in crowdsourcing competitions on Kaggle were analyzed to gain insight into how solvers continually formulate strategies in light of performance feedback obtained through interim ranking. The results suggest that solvers strategically allocate their efforts throughout the contest to dynamically optimize their payoffs through balancing the probability of winning and the cost of expending effort. In particular, solvers tend to increase their efforts toward the end of tournaments or when they get closer to winning positions. Furthermore, our findings indicate that a last-minute surge in effort is more prevalent among high-skill solvers than in those with lower skill levels. In addition to providing insights that may help solvers develop strategies to improve their performance, the study has implications for the design of online crowdsourcing platforms, particularly in terms of incentivizing solvers to put forth their best effort.     

Publication trends in gamification: A systematic mapping study

The term gamification and gamified systems are a trending area of research. However, gamification can indicate several different things, such as applying the game-like elements into the design of the user interface of a software, but not all gamification is necessarily associated with software products. Overall, it is unclear what different aspects are studied under the umbrella of ‘gamification’, and what is the current state of the art in the gamification research. In this paper, 1164 gamification studies are analyzed and classified based on their focus areas and the research topics to establish what the research trends in gamification are. Based on the results, e-learning and proof-of-concept studies in the ecological lifestyle and sustainability, assisting computer science studies and improving motivation are the trendiest areas of gamification research. Currently, the most common types of research are the proof-of-concept studies, and theoretical works on the different concepts and elements of gamification.     

A crowd enabled approach for processing nearest neighbor and range queries in incomplete databases with accuracy guarantee

With the proliferation of mobile devices and wireless technologies, location based services (LBSs) are becoming popular in smart cities. Two important classes of LBSs are Nearest Neighbor (NN) queries and range queries that provide user information about the locations of point of interests (POIs) such as hospitals or restaurants. Answers of these queries are more reliable and satisfiable if they come from trustworthy crowd instead of traditional location service providers (LSPs). We introduce an approach to evaluate NN and range queries with crowdsourced data and computation that eliminates the role of an LSP. In our crowdsourced approach, a user evaluates LBSs in a group. It may happen that group members do not have knowledge of all POIs in a certain area. We present efficient algorithms to evaluate queries with accuracy guarantee in incomplete databases. Experiments show that our approach is scalable and incurs less computational overhead.     

A Crowdsourcing Recommendation that Considers the Influence of Workers

In the context of the continuous development of the Internet, crowdsourcing has received continuous attention as a new cooperation model based on the relationship between enterprises, the public and society. Among them, a reasonably designed recommendation algorithm can recommend a batch of suitable workers for crowdsourcing tasks to improve the final task completion quality. Therefore, this paper proposes a crowdsourcing recommendation framework based on workers’ influence (CRBI). This crowdsourcing framework completes the entire process design from task distribution, worker recommendation, and result return through processes such as worker behavior analysis, task characteristics construction, and cost optimization. In this paper, a calculation model of workers’ influence characteristics based on the ablation method is designed to evaluate the comprehensive performance of workers. At the same time, the CRBI framework combines the traditional open-call task selection mode, builds a new task characteristics model by sensing the influence of the requesting worker and its task performance. In the end, accurate worker recommendation and task cost optimization are carried out by calculating model familiarity. In addition, for recommending workers to submit task answers, this paper also proposes an aggregation algorithm based on weighted influence to ensure the accuracy of task results. This paper conducts simulation experiments on some public datasets of AMT, and the experimental results show that the CRBI framework proposed in this paper has a high comprehensive performance. Moreover, CRBI has better usability, more in line with commercial needs, and can well reflect the wisdom of group intelligence.