Browsing by Subject "crowdsourcing"

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  • Goldman, Jean-Philippe; Scherrer, Yves; Glikman, Julie; Avanzi, Mathieu; Benzitoun, Christophe; Boula de Mareüil, Philippe (European Language Resources Association (ELRA), 2019)
    We present the crowdsourcing platform Donnez Votre Français à la Science (DFS, or “Give your French to Science”), which aims to collect linguistic data and document language use, with a special focus on regional variation in European French. The activities not only gather data that is useful for scientific studies, but they also provide feedback to the general public; this is important in order to reward participants, to encourage them to follow future surveys, and to foster interaction with the scientific community. The two main activities described here are 1) a linguistic survey on lexical variation with immediate feedback and 2) a speaker geolocalisation system; i.e., a quiz that guesses the linguistic origin of the participant by comparing their answers with previously gathered linguistic data. For the geolocalisation activity, we set up a simulation framework to optimise predictions. Three classification algorithms are compared: the first one uses clustering and shibboleth detection, whereas the other two rely on feature elimination techniques with Support Vector Machines and Maximum Entropy models as underlying base classifiers. The best-performing system uses a selection of 17 questions and reaches a localisation accuracy of 66%, extending the prediction from the one-best area (one among 109 base areas) to its first-order and second-order neighbouring areas.
  • Davis, Keith III (Helsingin yliopisto, 2020)
    We study the use of data collected via electroencephalography (EEG) to classify stimuli presented to subjects using a variety of mathematical approaches. We report an experiment with three objectives: 1) To train individual classifiers that reliably infer the class labels of visual stimuli using EEG data collected from subjects; 2) To demonstrate brainsourcing, a technique to combine brain responses from a group of human contributors each performing a recognition task to determine classes of stimuli; 3) To explore collaborative filtering techniques applied to data produced by individual classifiers to predict subject responses for stimuli in which data is unavailable or otherwise missing. We reveal that all individual classifier models perform better than a random baseline, while a brainsourcing model using data from as few as four participants achieves performance superior to any individual classifier. We also show that matrix factorization applied to classifier outputs as a collaborative filtering approach achieves predictive results that perform better than random. Although the technique is fairly sensitive to the sparsity of the dataset, it nonetheless demonstrates a viable proof-of-concept and warrants further investigation.
  • Laaksonen, Maija; Sajanti, Eeva; Sormunen, Jani J.; Penttinen, Ritva; Hanninen, Jari; Ruohomaki, Kai; Saaksjarvi, Ilari; Vesterinen, Eero J.; Vuorinen, Ilppo; Hytonen, Jukka; Klemola, Tero (2017)
    A national crowdsourcing-based tick collection campaign was organized in 2015 with the objective of producing novel data on tick distribution and tick-borne pathogens in Finland. Nearly 20 000 Ixodes ticks were collected. The collected material revealed the nationwide distribution of I. persulcatus for the first time and a shift northwards in the distribution of I. ricinus in Finland. A subset of 2038 tick samples containing both species was screened for Borrelia burgdorferi sensu lato (the prevalence was 14.2% for I. ricinus and 19.8% for I. persulcatus), B. miyamotoi (0.2% and 0.4%, respectively) and tick-borne encephalitis virus (TBEV; 0.2% and 3.0%, respectively). We also report new risk areas for TBEV in Finland and, for the first time, the presence of B. miyamotoi in ticks from mainland Finland. Most importantly, our study demonstrates the overwhelming power of citizen science in accomplishing a collection effort that would have been impossible with the scientific community alone.
  • Pirttinen, Nea (Helsingin yliopisto, 2020)
    Crowdsourcing has been used in computer science education to alleviate the teachers’ workload in creating course content, and as a learning and revision method for students through its use in educational systems. Tools that utilize crowdsourcing can act as a great way for students to further familiarize themselves with the course concepts, all while creating new content for their peers and future course iterations. In this study, student-created programming assignments from the second week of an introductory Java programming course are examined alongside the peer reviews these assignments received. The quality of the assignments and the peer reviews is inspected, for example, through comparing the peer reviews with expert reviews using inter-rater reliability. The purpose of this study is to inspect what kinds of programming assignments novice students create, and whether the same novice students can act as reliable reviewers. While it is not possible to draw definite conclusions from the results of this study due to limitations concerning the usability of the tool, the results seem to indicate that novice students are able to recognise differences in programming assignment quality, especially with sufficient guidance and well thought-out instructions.
  • Pesonen, Anu-Katriina; Lipsanen, Jari; Halonen, Risto; Elovainio, Marko; Sandman, Nils; Makelä, Juha-Matti; Antila, Minea; Bechard, Deni; Ollila, Hanna M.; Kuula, Liisa (2020)
    We used crowdsourcing (CS) to examine how COVID-19 lockdown affects the content of dreams and nightmares. The CS took place on the sixth week of the lockdown. Over the course of 1 week, 4,275 respondents (mean age 43, SD = 14 years) assessed their sleep, and 811 reported their dream content. Overall, respondents slept substantially more (54.2%) but reported an average increase of awakenings (28.6%) and nightmares (26%) from the pre-pandemic situation. We transcribed the content of the dreams into word lists and performed unsupervised computational network and cluster analysis of word associations, which suggested 33 dream clusters including 20 bad dream clusters, of which 55% were pandemic-specific (e.g., Disease Management, Disregard of Distancing, Elderly in Trouble). The dream-association networks were more accentuated for those who reported an increase in perceived stress. This CS survey on dream-association networks and pandemic stress introduces novel, collectively shared COVID-19 bad dream contents
  • Haarasilta, Teemu (Helsingfors universitet, 2013)
    The concept of social media includes a wide range of online, word-of-mouth forums including blogs, company sponsored discussion boards, chat rooms, consumer product or service ratings websites, and social networking sites. Social media is top of the agenda for many business executives today. Decision makers and consultants try to identify ways in which firms can make profitable use of applications such as YouTube, Facebook and Twitter. One of the main goals of an industrial company is to execute its new product development process in a way where new technological opportunities can be identified and commercialized before its competitors. In this study it was investigated how wood processing companies, log house builders and hardware stores currently exploit social media in their commercial efforts such as marketing and sales. Further it was also examined do wood processing companies, log house builders and hardware stores use social media in their new product development (NPD) process. The empirical part of the study was carried out by interviewing a small number of companies and conducting a desk research of the social media activity of a larger number of companies. Most of the companies studied were present in social media. The type and the intensity of social media presence varied depending on the type of the company. E.g. hardware stores used social media merely for marketing and sales related purposes whereas in wood processing companies the focus was on communication and PR. Besides the normal customer feedback systems there was no evidence that the companies of this study would actively use social media in collecting end user driven ideas for R&D purposes. Based on the theoretical background, framework of the study, desk research and company interviews a model for collecting feedback and product development ideas from end users is created. Furthermore some ideas for more effective usage of social media regarding the topic of the study are offered as well.