Difference between revisions of "Winter Milestone 5 Templates"

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We're going to borrow introduction section from this paper as an example: [[:Media:Twitch Crowdsourcing (private).pdf | Vaish R, Wyngarden K, Chen J, et al. Twitch crowdsourcing: crowd contributions in short bursts of time. Proceedings of the 32nd annual ACM conference on Human factors in computing systems. ACM, 2014: 3645-3654.]] Please note how this section was divided into different parts. Please follow the same template.  
 
We're going to borrow introduction section from this paper as an example: [[:Media:Twitch Crowdsourcing (private).pdf | Vaish R, Wyngarden K, Chen J, et al. Twitch crowdsourcing: crowd contributions in short bursts of time. Proceedings of the 32nd annual ACM conference on Human factors in computing systems. ACM, 2014: 3645-3654.]] Please note how this section was divided into different parts. Please follow the same template.  
  
 +
=== Brief introduction of the system ===
 +
Twitch is an Android application that appears when the user
 +
presses the phone’s power/lock button (Figures 1 and 3).
 +
When the user completes the twitch crowdsourcing task, the
 +
phone unlocks normally. Each task involves a choice
 +
between two to six options through a single motion such as
 +
a tap or swipe.
  
=== Specific problem being solved (not just crowdsourcing, but getting into specifics) ===  
+
=== How is the system solving critical problems ===  
 +
To motivate continued participation, Twitch provides both
 +
instant and aggregated feedback to the user. An instant feedback display shows how many other users agreed via a
 +
fadeout as the lock screen disappears (Figure 4) or how the
 +
user’s contributions apply to the whole (Figure 5).
 +
Aggregated data is also available via a web application,
 +
allowing the user to explore all data that the system has
 +
collected. For example, Figure 2 shows a human generated
 +
map from the Census application.
 +
To address security concerns, users are allowed to either
 +
disable or keep their existing Android passcode while using
 +
Twitch. If users do not wish to answer a question, they may
 +
skip Twitch by selecting ‘Exit’ via the options menu. This
 +
design decision has been made to encourage the user to give
 +
Twitch an answer, which is usually faster than exiting.
 +
Future designs could make it easier to skip a task, for
 +
example through a swipe-up.
  
Mobilizing participation is a central challenge for every
+
=== Introducing modules of the system ===
crowdsourcing campaign. Campaigns that cannot motivate
+
Below, we introduce the three main crowdsourcing
enough participants will fail. Unfortunately, many
+
applications that Twitch supports. The first, Census,
interested contributors simply cannot find enough time:
+
attempts to capture local knowledge. The following two,
lack of time is the top reason that subject experts do not
+
Image Voting and Structuring the Web, draw on creative
contribute to Wikipedia. Those who do participate in
+
and topical expertise. These three applications are bundled
crowdsourcing campaigns often drop out when life
+
into one Android package, and each can be accessed
becomes busy. Even seemingly small time
+
interchangeably through Twitch's settings menu.
requirements can dissuade users: psychologists define
+
channel factors as the small but critical barriers to action
+
that have a disproportionate effect on whether people
+
complete a goal.  
+
  
=== Motivation ===
 
Despite this constraint, many
 
crowdsourcing campaigns assume that participants will
 
work for minutes or hours at once, leading to a granularity
 
problem where task size is poorly matched to
 
contributors’ opportunities. We speculate that a great
 
number of crowdsourcing campaigns will struggle to
 
succeed as long as potential contributors are deterred by the
 
time commitment.
 
  
=== Introducing the concept, the high level insight ===  
+
=== Module 1: Census ===  
To engage a wider set of crowdsourcing contributors, we
+
 
introduce twitch crowdsourcing: interfaces that encourage
+
==== Problem ====
contributions of a few seconds at a time. Taking advantage
+
Despite progress in producing effective understanding of
of the common habit of turning to mobile phones in spare
+
static elements of our physical world — routes, businesses
moments, we replace the mobile phone unlock screen
+
and points of interest — we lack an understanding of
with a brief crowdsourcing task, allowing each user to make
+
human activity. How busy is the corner cafe at 2pm on
small, compounded volunteer contributions over time. In
+
Fridays? What time of day do businesspeople clear out of
contrast, existing mobile crowdsourcing platforms (e.g.,
+
the downtown district and get replaced by socializers?
[12,16,22]) tend to assume long, focused runs of work. Our
+
Which neighborhoods keep high-energy activities going
design challenge is thus to create crowdsourcing tasks that
+
until 11pm, and which ones become sleepy by 6pm? Users
operate in very short time periods and at low cognitive load.
+
could take advantage of this information to plan their
 +
commutes, their social lives and their work.
 +
Existing crowdsourced techniques such as Foursquare are
 +
too sparse to answer these kinds of questions: the answers
 +
require at-the-moment, distributed human knowledge. We
 +
envision that twitch crowdsourcing can help create a
 +
human-centered equivalent of Google Street View, where a
 +
user could browse typical crowd activity in an area. To do
 +
so, we ask users to answer one of several questions about the world around them each time they unlock their phone.
 +
Users can then browse the map they are helping create.
 +
Census is the default crowdsourcing task in Twitch. It
 +
collects structured information about what people
 +
experience around them. Each Census unlock screen
 +
consists of four to six tiles (Figures 1 and 3), each task
 +
centered around questions such as:
 +
• How many people are around you?
 +
• What kinds of attire are nearby people wearing?
 +
• What are you currently doing?
 +
• How much energy do you have right now?
 +
While not exhaustive, these questions cover several types of
 +
information that a local census might seek to provide. Two
 +
of the four questions ask users about the people around
 +
them, while the other two ask about users themselves; both
 +
of which they are uniquely equipped to answer. Each
 +
answer is represented graphically; for example, in case of
 +
activities, users have icons for working, at home, eating,
 +
travelling, socializing, or exercising.
 +
To motivate continued engagement, Census provides two
 +
modes of feedback. Instant feedback (Figure 4) is a brief
 +
Android popup message that appears immediately after the
 +
user makes a selection. It reports the percentage of
 +
responses in the current time bin and location that agreed
 +
with the user, then fades out within two seconds. It is
 +
transparent to user input, so the user can begin interacting
 +
with the phone even while it is visible. Aggregated report
 +
allows Twitch users to see the cumulative effect of all
 +
users’ behavior. The data is bucketed and visualized on a
 +
map (Figure 2) on the Twitch homepage. Users can filter
 +
the data based on activity type or time of day.
 +
Photo Ranking
 +
Beyond harnessing local observations via Census, we
 +
wanted to demonstrate that twitch crowdsourcing could
 +
support traditional crowdsourcing tasks such as image ranking (e.g., Matchin [17]). Needfinding interviews and
 +
prototyping sessions with ten product design students at
 +
Stanford University indicated that product designers not
 +
only need photographs for their design mockups, but they
 +
also enjoy looking at the photographs. Twitch harnesses
 +
this interest to help rank photos and encourage contribution
 +
of new photos.
 +
Photo Ranking crowdsources a ranking of stock photos for
 +
themes from a Creative Commons-licensed image library.
 +
The Twitch task displays two images related to a theme
 +
(e.g., Nature Panorama) per unlock and asks the user to
 +
slide to select the one they prefer (Figure 1). Pairwise
 +
ranking is considered faster and more accurate than rating
 +
[17]. The application regularly updates with new photos.
 +
Users can optionally contribute new photos to the database
 +
by taking a photo instead of rating one. Contributed photos
 +
must be relevant to the day’s photo theme, such as Nature
 +
Panorama, Soccer, or Beautiful Trash. Contributing a photo
 +
takes longer than the average Twitch task, but provides an
 +
opportunity for motivated individuals to enter the
 +
competition and get their photos rated.
 +
Like with Census, users receive instant feedback through a
 +
popup message to display how many other users agreed
 +
with their selection. We envision a web interface where all
 +
uploaded images can be browsed, downloaded and ranked.
 +
This data can also connect to computer vision research by
 +
providing high-quality images of object categories and
 +
scenes to create better classifiers.
 +
Structuring the Web
 +
Search engines no longer only return documents — they
 +
now aim to return direct answers [6,9]. However, despite
 +
massive undertakings such as the Google Knowledge Graph
 +
[36], Bing Satori [37] and Freebase [7], much of the
 +
knowledge on the web remains unstructured and unavailable for interactive applications. For example,
 +
searching for ‘Weird Al Yankovic born’ in a search engine
 +
such as Google returns a direct result ‘1959’ drawn from
 +
the knowledge base; however, searching for the equally
 +
relevant ‘Weird Al Yankovic first song’, ‘Weird Al
 +
Yankovic band members’, or ‘Weird Al Yankovic
 +
bestselling album’ returns a long string of documents but no
 +
direct answer, even though the answers are readily available
 +
on the performer’s Wikipedia page.
 +
To enable direct answers, we need structured data that is
 +
computer-readable. While crowdsourced undertakings such
 +
as Freebase and dbPedia have captured much structured
 +
data, they tend to only acquire high-level information and
 +
do not have enough contributors to achieve significant
 +
depth on any single entity. Likewise, while information
 +
extraction systems such as ReVerb [14] automatically draw
 +
such information from the text of the Wikipedia page, their
 +
error rates are currently too high to trust. Crowdsourcing
 +
can help such systems identify errors to improve future
 +
accuracy [18]. Therefore, we apply twitch crowdsourcing to
 +
produce both structured data for interactive applications and
 +
training data for information extraction systems.
 +
Contributors to online efforts are drawn to goals that allow
 +
them to exhibit their unique expertise [2]. Thus, we allow
 +
users to help create structured data for topics of interest.
 +
The user can specify any topic on Wikipedia that they are
 +
interested in or want to learn about, for example HCI, the
 +
Godfather films, or their local city. To do so within a oneto-two
 +
second time limit, we draw on mixed-initiative
 +
information extraction systems (e.g., [18]) and ask users to
 +
help vet automatic extractions.
 +
When a user unlocks his or her phone, Structuring the Web
 +
displays a high-confidence extraction generated using
 +
ReVerb, and its source statement from the selected
 +
Wikipedia page (Figure 1). The user indicates with one
 +
swipe whether the extraction is correct with respect to the
 +
statement. ReVerb produces an extraction in SubjectRelationship-Object
 +
format: for example, if the source
 +
statement is “Stanford University was founded in 1885 by
 +
Leland Stanford as a memorial to their son”, ReVerb
 +
returns {Stanford University}, {was founded in}, {1885}
 +
and Twitch displays this structure. To minimize cognitive
 +
load and time requirements, the application filters only
 +
include short source sentences and uses color coding to
 +
match extractions with the source text.
 +
In Structuring the Web, the instant feedback upon accepting
 +
an extraction shows the user their progress growing a
 +
knowledge tree of verified facts (Figure 5). Rejecting an
 +
extraction instead scrolls the user down the article as far as
 +
their most recent extraction source, demonstrating the
 +
user’s progress in processing the article. In the future, we
 +
envision that search engines can utilize this data to answer a
 +
wider range of factual queries.
  
=== The system ===
 
To demonstrate the opportunities of twitch crowdsourcing,
 
we present Twitch, a crowdsourcing platform for Android
 
devices that augments the unlock screen with 1–3 second
 
volunteer crowdsourcing tasks (Figure 1). Rather than a
 
typical slide-to-unlock mechanism, the user unlocks their
 
phone by completing a brief crowdsourcing task. Twitch is
 
publicly deployed and has collected over eleven thousand
 
volunteer contributions to date. The system sits aside any
 
existing security passcodes on the phone.
 
  
=== System details ===
 
Twitch crowdsourcing allows designers to tap into local and
 
topical expertise from mobile users. Twitch supports three
 
unlock applications:
 
1) Census envisions a realtime people-centered world
 
census: where people are, what they are doing, and how
 
they are doing it. For example, how busy is the corner café
 
at 2pm on Fridays? Census answers these questions by
 
asking users to share information about their surroundings
 
as they navigate the physical world, for example the size of
 
the crowd or current activities (Figure 1).
 
2) Photo Ranking captures users’ opinion between two
 
photographs. In formative work with product designers, we
 
found that they require stock photos for mockups, but stock
 
photo sites have sparse ratings. Likewise, computer vision
 
needs more data to identify high-quality images from the
 
web. Photo Ranking (Figure 1) asks users to swipe to
 
choose the better of two stock photos on a theme, or
 
contribute their own through their cell phone camera.
 
  
3) Structuring the Web helps transform the written web into
 
a format that computers can understand. Users specify an
 
area of expertise — HCI, the Doctor Who television series,
 
or anything else of interest on Wikipedia — and help verify
 
web extractions relevant to that topic. Each unlock involves
 
confirming or rejecting a short extraction. In doing so, users
 
could power a fact-oriented search engine that would
 
directly answer queries like “heuristic evaluation creator”.
 
After making a selection, Twitch users can see whether
 
their peers agreed with their selection. In addition, they can
 
see how their contribution is contributing to the larger
 
whole, for example aggregate responses on a map (Figure
 
2) or in a fact database (Figure 5).
 
  
=== Evaluation method and results ===
 
We deployed Twitch publicly on the web and attracted 82
 
users to install Twitch on their primary phones. Over three
 
weeks, the average user unlocked their phone using Twitch
 
19 times per day. Users contributed over 11,000 items to
 
our crowdsourced database, covering several cities with
 
local census information. The median Census task unlock
 
took 1.6 seconds, compared to 1.4 seconds for a standard
 
slide-to-unlock gesture. Secondary task studies
 
demonstrated that Twitch unlocks added minimal cognitive
 
load to the user.
 
Our work indicates that it may be possible to engage a
 
broad set of new participants in crowdsourcing campaigns
 
as they go about their day or have a few spare moments. In
 
the following sections, we introduce twitch crowdsourcing
 
in more detail and report on our public deployment and
 
field experiments.
 
  
 
== Science ==
 
== Science ==

Revision as of 11:31, 10 February 2016

Please use the following template to write up your introduction section this week.

System

We're going to borrow introduction section from this paper as an example: Vaish R, Wyngarden K, Chen J, et al. Twitch crowdsourcing: crowd contributions in short bursts of time. Proceedings of the 32nd annual ACM conference on Human factors in computing systems. ACM, 2014: 3645-3654. Please note how this section was divided into different parts. Please follow the same template.

Brief introduction of the system

Twitch is an Android application that appears when the user presses the phone’s power/lock button (Figures 1 and 3). When the user completes the twitch crowdsourcing task, the phone unlocks normally. Each task involves a choice between two to six options through a single motion such as a tap or swipe.

How is the system solving critical problems

To motivate continued participation, Twitch provides both instant and aggregated feedback to the user. An instant feedback display shows how many other users agreed via a fadeout as the lock screen disappears (Figure 4) or how the user’s contributions apply to the whole (Figure 5). Aggregated data is also available via a web application, allowing the user to explore all data that the system has collected. For example, Figure 2 shows a human generated map from the Census application. To address security concerns, users are allowed to either disable or keep their existing Android passcode while using Twitch. If users do not wish to answer a question, they may skip Twitch by selecting ‘Exit’ via the options menu. This design decision has been made to encourage the user to give Twitch an answer, which is usually faster than exiting. Future designs could make it easier to skip a task, for example through a swipe-up.

Introducing modules of the system

Below, we introduce the three main crowdsourcing applications that Twitch supports. The first, Census, attempts to capture local knowledge. The following two, Image Voting and Structuring the Web, draw on creative and topical expertise. These three applications are bundled into one Android package, and each can be accessed interchangeably through Twitch's settings menu.


Module 1: Census

Problem

Despite progress in producing effective understanding of static elements of our physical world — routes, businesses and points of interest — we lack an understanding of human activity. How busy is the corner cafe at 2pm on Fridays? What time of day do businesspeople clear out of the downtown district and get replaced by socializers? Which neighborhoods keep high-energy activities going until 11pm, and which ones become sleepy by 6pm? Users could take advantage of this information to plan their commutes, their social lives and their work. Existing crowdsourced techniques such as Foursquare are too sparse to answer these kinds of questions: the answers require at-the-moment, distributed human knowledge. We envision that twitch crowdsourcing can help create a human-centered equivalent of Google Street View, where a user could browse typical crowd activity in an area. To do so, we ask users to answer one of several questions about the world around them each time they unlock their phone. Users can then browse the map they are helping create. Census is the default crowdsourcing task in Twitch. It collects structured information about what people experience around them. Each Census unlock screen consists of four to six tiles (Figures 1 and 3), each task centered around questions such as: • How many people are around you? • What kinds of attire are nearby people wearing? • What are you currently doing? • How much energy do you have right now? While not exhaustive, these questions cover several types of information that a local census might seek to provide. Two of the four questions ask users about the people around them, while the other two ask about users themselves; both of which they are uniquely equipped to answer. Each answer is represented graphically; for example, in case of activities, users have icons for working, at home, eating, travelling, socializing, or exercising. To motivate continued engagement, Census provides two modes of feedback. Instant feedback (Figure 4) is a brief Android popup message that appears immediately after the user makes a selection. It reports the percentage of responses in the current time bin and location that agreed with the user, then fades out within two seconds. It is transparent to user input, so the user can begin interacting with the phone even while it is visible. Aggregated report allows Twitch users to see the cumulative effect of all users’ behavior. The data is bucketed and visualized on a map (Figure 2) on the Twitch homepage. Users can filter the data based on activity type or time of day. Photo Ranking Beyond harnessing local observations via Census, we wanted to demonstrate that twitch crowdsourcing could support traditional crowdsourcing tasks such as image ranking (e.g., Matchin [17]). Needfinding interviews and prototyping sessions with ten product design students at Stanford University indicated that product designers not only need photographs for their design mockups, but they also enjoy looking at the photographs. Twitch harnesses this interest to help rank photos and encourage contribution of new photos. Photo Ranking crowdsources a ranking of stock photos for themes from a Creative Commons-licensed image library. The Twitch task displays two images related to a theme (e.g., Nature Panorama) per unlock and asks the user to slide to select the one they prefer (Figure 1). Pairwise ranking is considered faster and more accurate than rating [17]. The application regularly updates with new photos. Users can optionally contribute new photos to the database by taking a photo instead of rating one. Contributed photos must be relevant to the day’s photo theme, such as Nature Panorama, Soccer, or Beautiful Trash. Contributing a photo takes longer than the average Twitch task, but provides an opportunity for motivated individuals to enter the competition and get their photos rated. Like with Census, users receive instant feedback through a popup message to display how many other users agreed with their selection. We envision a web interface where all uploaded images can be browsed, downloaded and ranked. This data can also connect to computer vision research by providing high-quality images of object categories and scenes to create better classifiers. Structuring the Web Search engines no longer only return documents — they now aim to return direct answers [6,9]. However, despite massive undertakings such as the Google Knowledge Graph [36], Bing Satori [37] and Freebase [7], much of the knowledge on the web remains unstructured and unavailable for interactive applications. For example, searching for ‘Weird Al Yankovic born’ in a search engine such as Google returns a direct result ‘1959’ drawn from the knowledge base; however, searching for the equally relevant ‘Weird Al Yankovic first song’, ‘Weird Al Yankovic band members’, or ‘Weird Al Yankovic bestselling album’ returns a long string of documents but no direct answer, even though the answers are readily available on the performer’s Wikipedia page. To enable direct answers, we need structured data that is computer-readable. While crowdsourced undertakings such as Freebase and dbPedia have captured much structured data, they tend to only acquire high-level information and do not have enough contributors to achieve significant depth on any single entity. Likewise, while information extraction systems such as ReVerb [14] automatically draw such information from the text of the Wikipedia page, their error rates are currently too high to trust. Crowdsourcing can help such systems identify errors to improve future accuracy [18]. Therefore, we apply twitch crowdsourcing to produce both structured data for interactive applications and training data for information extraction systems. Contributors to online efforts are drawn to goals that allow them to exhibit their unique expertise [2]. Thus, we allow users to help create structured data for topics of interest. The user can specify any topic on Wikipedia that they are interested in or want to learn about, for example HCI, the Godfather films, or their local city. To do so within a oneto-two second time limit, we draw on mixed-initiative information extraction systems (e.g., [18]) and ask users to help vet automatic extractions. When a user unlocks his or her phone, Structuring the Web displays a high-confidence extraction generated using ReVerb, and its source statement from the selected Wikipedia page (Figure 1). The user indicates with one swipe whether the extraction is correct with respect to the statement. ReVerb produces an extraction in SubjectRelationship-Object format: for example, if the source statement is “Stanford University was founded in 1885 by Leland Stanford as a memorial to their son”, ReVerb returns {Stanford University}, {was founded in}, {1885} and Twitch displays this structure. To minimize cognitive load and time requirements, the application filters only include short source sentences and uses color coding to match extractions with the source text. In Structuring the Web, the instant feedback upon accepting an extraction shows the user their progress growing a knowledge tree of verified facts (Figure 5). Rejecting an extraction instead scrolls the user down the article as far as their most recent extraction source, demonstrating the user’s progress in processing the article. In the future, we envision that search engines can utilize this data to answer a wider range of factual queries.



Science

We're going to borrow introduction section from this paper as an example: Cheng, J., Teevan, J. & Bernstein, M.S. (2015). Measuring Crowdsourcing Effort with Error-Time Curves. CHI 2015.. Please note how this section was divided into different parts. Please follow the same template.


Phenomenon you're interested in

Imagine that a requester wants to use Amazon Mechanical Turk to label 10,000 images with a fixed set of tags. How much should workers be paid to label each image? Would labeling an image with twice as many tags result in a task that is twice as much effort? Should the tags be provided in a drop down list or with radio buttons? Answering these questions requires a fine-grained understanding of the amount of effort the task requires. This process today involves trial and error: requesters observe the wait time and quality on test tasks, guess what might have been causing any problems, tweak the task, and repeat. An accurate measure of the effort required to complete a crowdsourced task would enable requesters to

compare different approaches to their tasks, iterate toward a better design, and price their tasks objectively. It could also help workers decide whether to accept a task, or even allow systems to offer tasks based on difficulty or time availability. However, despite its potential value, task effort is challenging to estimate. Workers face cognitive biases in assessing diffi- culty [21], while requesters cannot easily observe the process and, as experts, categorically underestimate completion times [12]. These limits suggest the need for a behavioral approach to measure effort. One approach might be to let the market identify hard tasks by reacting to the posted price [30].

The puzzle (observations we can't account for yet)

However, prices cannot easily make fine distinctions in an inelastic market such as Mechanical Turk [14]. Another approach might be to use task duration as a signal of difficulty, but this is unreliable because workers regularly accept multiple tasks simultaneously and interleave work [29]. Measures such as reaction time [32] are not easy to apply to typical crowd tasks: reaction time metrics tend to use simplistic tasks (e.g., shape or color recognition), while others may be too involved for crowd work (e.g., [9]).

Experimental design

In this paper, we propose a data-driven behavioral measure of effort that can be easily and cheaply calculated using the crowd. Our metric, the error time area (ETA), draws on cognitive psychology literature on speed-accuracy tradeoff curves [32], and represents the effort required for a worker to accurately complete a task. To create it, we first recruit workers to complete the task under different time limits. Next, we fit a curve to the collected data relating the error rate and time limit (Figure 1). Last, we compute ETA by taking the area under this error-time curve. Because ETA is calculated using a data-driven approach, task difficulty can be determined with minimal effort and without analytical modeling. Rather than measuring average duration independent of work quality, ETA computes quality as a function of duration and thus can be used to estimate a wage for a task. ETA also allows requesters to compare multiple task designs; for example, we find that tagging an image with an open textbox is less effort than choosing between a fixed list of 16 options, but more effort than choosing between a fixed list of 8 options.

Evaluation methods

After describing ETA, we explore the metric via four studies: – Study 1: ETA vs. other measures of effort. For ten common microtasking primitives (e.g., multiple choice questions, long-form text entry), we show that the ETA metric represents effort better than existing measures. – Study 2: ETA vs. market price. We then compare ETA as well as other measures to the market prices of these primitives on a crowdsourcing platform. – Study 3: Modeling perceptual costs. By augmenting ETA with measures of perceptual effort, we find we can better model a worker’s perceived difficulty of a task. – Study 4: Tasks without ground truth. In order to capture how well people do a task, ETA requires ground truth. We extend the metric to also work for subjective tasks.

The Result

We then demonstrate how ETA can be used for rapidly prototyping tasks. ETA makes it possible to characterize tasks in terms of their monetary cost and human effort, and paves the way for better task design, payment, and allocation.