6,492 research outputs found
Mosaic: Designing Online Creative Communities for Sharing Works-in-Progress
Online creative communities allow creators to share their work with a large
audience, maximizing opportunities to showcase their work and connect with fans
and peers. However, sharing in-progress work can be technically and socially
challenging in environments designed for sharing completed pieces. We propose
an online creative community where sharing process, rather than showcasing
outcomes, is the main method of sharing creative work. Based on this, we
present Mosaic---an online community where illustrators share work-in-progress
snapshots showing how an artwork was completed from start to finish. In an
online deployment and observational study, artists used Mosaic as a vehicle for
reflecting on how they can improve their own creative process, developed a
social norm of detailed feedback, and became less apprehensive of sharing early
versions of artwork. Through Mosaic, we argue that communities oriented around
sharing creative process can create a collaborative environment that is
beneficial for creative growth
Leveraging Crowdsourcing Data For Deep Active Learning - An Application: Learning Intents in Alexa
This paper presents a generic Bayesian framework that enables any deep
learning model to actively learn from targeted crowds. Our framework inherits
from recent advances in Bayesian deep learning, and extends existing work by
considering the targeted crowdsourcing approach, where multiple annotators with
unknown expertise contribute an uncontrolled amount (often limited) of
annotations. Our framework leverages the low-rank structure in annotations to
learn individual annotator expertise, which then helps to infer the true labels
from noisy and sparse annotations. It provides a unified Bayesian model to
simultaneously infer the true labels and train the deep learning model in order
to reach an optimal learning efficacy. Finally, our framework exploits the
uncertainty of the deep learning model during prediction as well as the
annotators' estimated expertise to minimize the number of required annotations
and annotators for optimally training the deep learning model.
We evaluate the effectiveness of our framework for intent classification in
Alexa (Amazon's personal assistant), using both synthetic and real-world
datasets. Experiments show that our framework can accurately learn annotator
expertise, infer true labels, and effectively reduce the amount of annotations
in model training as compared to state-of-the-art approaches. We further
discuss the potential of our proposed framework in bridging machine learning
and crowdsourcing towards improved human-in-the-loop systems
Active learning in annotating micro-blogs dealing with e-reputation
Elections unleash strong political views on Twitter, but what do people
really think about politics? Opinion and trend mining on micro blogs dealing
with politics has recently attracted researchers in several fields including
Information Retrieval and Machine Learning (ML). Since the performance of ML
and Natural Language Processing (NLP) approaches are limited by the amount and
quality of data available, one promising alternative for some tasks is the
automatic propagation of expert annotations. This paper intends to develop a
so-called active learning process for automatically annotating French language
tweets that deal with the image (i.e., representation, web reputation) of
politicians. Our main focus is on the methodology followed to build an original
annotated dataset expressing opinion from two French politicians over time. We
therefore review state of the art NLP-based ML algorithms to automatically
annotate tweets using a manual initiation step as bootstrap. This paper focuses
on key issues about active learning while building a large annotated data set
from noise. This will be introduced by human annotators, abundance of data and
the label distribution across data and entities. In turn, we show that Twitter
characteristics such as the author's name or hashtags can be considered as the
bearing point to not only improve automatic systems for Opinion Mining (OM) and
Topic Classification but also to reduce noise in human annotations. However, a
later thorough analysis shows that reducing noise might induce the loss of
crucial information.Comment: Journal of Interdisciplinary Methodologies and Issues in Science -
Vol 3 - Contextualisation digitale - 201
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Citizen-led Work using Social Computing and Procedural Guidance
Online platforms enable people to interact with friends, family, and the world at large. How might people go beyond sharing stories and ideas to building and testing theories in the real world? While many are motivated to dig deeper into their lived experience, limited expertise and lack of platform support make complex activities like experimentation dauntingly hard. Novices benefit greatly from expert guidance: this thesis advocates baking the guidance into the interface itself.This dissertation introduces procedural guidance to build just-in-time expertise for difficult tasks. Procedural guidance has multiple advantages: it is minimal, leverages teachable moments, and can be ability-specific. This dissertation instantiates this insight of procedural guidance through a sequence of increasingly complex social computing systems: Gut Instinct for curating ideas, Docent for generating hypotheses, and Galileo for citizen-led experiments.Gut Instinct hosts online learning materials and enables people to collaboratively brainstorm potential influences on people’s microbiome. Docent explicitly teaches people to create hypotheses by combining personal insights and online learning with task-specific scaffolding. Finally, Galileo reifies experimentation in the software, provides multiple roles for contribution, and automatically manages interdependencies. Multiple evaluations—controlled experiments and field deployments with online communities including American Gut participants—demonstrate that procedural guidance enables people to transform intuitions to hypotheses and structurally-sound experiments. By enabling people to draw on lived experience, this dissertation harbingers a future where people can convert their intuitions to actionable plans and implement these plans with online communities. This dissertation concludes by discussing opportunities for complex work using social computing platforms
Broadening community engagement in clinical research: Designing and assessing a pilot crowdsourcing project to obtain community feedback on an HIV clinical trial.
BACKGROUND/AIMS:Community engagement is widely acknowledged as an important step in clinical trials. One underexplored method for engagement in clinical trials is crowdsourcing. Crowdsourcing involves having community members attempt to solve a problem and then publicly sharing innovative solutions. We designed and conducted a pilot using a crowdsourcing approach to obtain community feedback on an HIV clinical trial, called the Acceptability of Combined Community Engagement Strategies Study. In this work, we describe and assess the Acceptability of Combined Community Engagement Strategies Study's crowdsourcing activities in order to examine the opportunities of crowdsourcing as a clinical trial community engagement strategy. METHODS:The crowdsourcing engagement activities involved in the Acceptability of Combined Community Engagement Strategies Study were conducted in the context of a phase 1 HIV antibody trial (ClinicalTrials.gov identifier: NCT03803605). We designed a series of crowdsourcing activities to collect feedback on three aspects of this clinical trial: the informed consent process, the experience of participating in the trial, and fairness/reciprocity in HIV clinical trials. All crowdsourcing activities were open to members of the general public 18 years of age or older, and participation was solicited from the local community. A group discussion was held with representatives of the clinical trial team to obtain feedback on the utility of crowdsourcing as a community engagement strategy for informing future clinical trials. RESULTS:Crowdsourcing activities made use of innovative tools and a combination of in-person and online participation opportunities to engage community members in the clinical trial feedback process. Community feedback on informed consent was collected by transforming the clinical trial's informed consent form into a series of interactive video modules, which were screened at an open public discussion. Feedback on the experience of trial participation involved designing three fictional vignettes which were then transformed into animated videos and screened at an open public discussion. Finally, feedback on fairness/reciprocity in HIV clinical trials was collected using a crowdsourcing idea contest with online and in-person submission opportunities. Our public discussion events were attended by 38 participants in total; our idea contest received 43 submissions (27 in-person, 16 online). Facebook and Twitter metrics demonstrated substantial engagement in the project. The clinical team found crowdsourcing primarily useful for enhancing informed consent and trial recruitment. CONCLUSION:There is sufficient lay community interest in open calls for feedback on the design and conduct of clinical trials, making crowdsourcing both a novel and feasible engagement strategy. Clinical trial researchers are encouraged to consider the opportunities of implementing crowdsourcing to inform trial processes from a community perspective
Improving User Involvement Through Live Collaborative Creation
Creating an artifact - such as writing a book, developing software, or performing a piece of music - is often limited to those with domain-specific experience or training.
As a consequence, effectively involving non-expert end users in such creative processes is challenging.
This work explores how computational systems can facilitate collaboration, communication, and participation in the context of involving users in the process of creating artifacts while mitigating the challenges inherent to such processes.
In particular, the interactive systems presented in this work support live collaborative creation, in which artifact users collaboratively participate in the artifact creation process with creators in real time.
In the systems that I have created, I explored liveness, the extent to which the process of creating artifacts and the state of the artifacts are immediately and continuously perceptible, for applications such as programming, writing, music performance, and UI design.
Liveness helps preserve natural expressivity, supports real-time communication, and facilitates participation in the creative process.
Live collaboration is beneficial for users and creators alike: making the process of creation visible encourages users to engage in the process and better understand the final artifact.
Additionally, creators can receive immediate feedback in a continuous, closed loop with users.
Through these interactive systems, non-expert participants help create such artifacts as GUI prototypes, software, and musical performances.
This dissertation explores three topics: (1) the challenges inherent to collaborative creation in live settings, and computational tools that address them; (2) methods for reducing the barriers of entry to live collaboration; and (3) approaches to preserving liveness in the creative process, affording creators more expressivity in making artifacts and affording users access to information traditionally only available in real-time processes.
In this work, I showed that enabling collaborative, expressive, and live interactions in computational systems allow the broader population to take part in various creative practices.PHDComputer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/145810/1/snaglee_1.pd
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