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The CrowdFlower Blog

Real Time Foto Moderator – The Rambling Anti-Elevator Pitch

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I never really understood the need for photo moderation until Chris and I built our first webapp together. This webapp was called FaceStat, a website where you could upload photos of yourself and get “feedback” from other people on things like whether or not you looked friendly and if you could win a fight with a medium-sized dog. It was a surprisingly popular, extremely viral website with horrible retention and no prayer of a viable business model.

At first we just let people upload whatever photos they wanted and comment however they wanted. Not surprisingly, things quickly got out of hand. Hours after we released the website we started seeing extremely disturbing uploads. We tried to use a user-moderation system but there were lots of false positives and it pained me to subject unsuspecting users to some of the things people uploaded. We tried automated systems but they were extremely unreliable.

This experience caused us to build CrowdSifter, a content moderation crowdsourcing tool, where our FaceStat app was the first customer. It worked really well and we got a few early customers. But our customers seemed interested in lots of tasks besides content moderation, so we decided to build a general purpose crowdsourcing system that we called CrowdFlower. Eventually CrowdSifter fell into disrepair and we shut it down and moved the existing customers over to our general crowdsourcing platform. CrowdFlower became so popular that we renamed our company CrowdFlower and we got in the business of helping companies get very high quality work done through crowdsourcing. We signed up over 50 channels, over 3 million contributors and hundreds of name-brand customers. We built out an enterprise sales team and learned how to deal with procurement departments. We even did some work for the US Government.

But as our engagement sizes rose, our sales costs rose too, and we stopped being able to service anyone except customers with the largest crowdsourcing jobs. Which in some ways completely contradicts the promise of crowdsourcing – a super-flexible workforce. We have a self-service site with some happy customers, but the learning curve is steep and while we’re working on improvements, I’m not sure if it’s possible to make a general-purpose crowdsourcing tool that’s also easy for everyone to use.

So we decided to build specific self-service products that would be easy to use, reliably fast and high-quality. I wanted to start with an application that we had lots of experience with and one where we felt like we really understood our customer’s needs. So I chose content moderation. The customers have expanded in the past few years with iPhone developers getting lots and lots of user generated images and living in constant fear of apple shutting them down for objectionable content. I’ve seen several indie app developers sign up for our beta and in particular I hope that this is a great product for them.

We plan on releasing several of these self-service applications over the next few months. If there are any crowdsourcing applications you would especially like to see, please let me know.

TL;DR We’ve offered crowdsourced photo moderation as a service for years, but now you can buy it with clear pricing, no sign-up fee and without ever talking to a sales person. And you should! Go to crowdflower.com/rtfm.


What Motivates Crowdsource Workers on CrowdFlower & Kaggle – Highlights of SXSW 2012′s “Pay or Play” Crowdsourcing Talk

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This is a summary of South by Southwest’s talk “Getting a Crowd to Work for You: For Pay or Play?” featuring CrowdFlower founder Lukas Biewald and Kaggle founder Anthony Goldsbloom. They discussed how crowdsource work is transforming traditional business and research, provided background on their companies, and offered insights on the motivations of people involved with crowdsourcing.

The Rise of Crowdsourcing

CrowdFlower’s company name was inspired by a 2006 article by Wired author Jeff Howe, who coined the term “crowdsourcing”. Since then, the concept has rapidly gained mainstream recognition.

These graphs below show the growth of Google searches of the term, and the rising number of academic journal articles which also reference crowdsourcing:

The crowdsourcing industry has grown in size and diversity, and now involves hundreds of companies in at least eight sub-sectors, as this chart by crowdsourcing.org shows. CrowdFlower and Kaggle fit within the Cloud Labor segment, providing on-demand services from a virtual pool of workers. In line with broader industry trends, both CrowdFlower and Kaggle have enjoyed very strong growth in recent years: The number of judgements performed for enterprise and self-service clients by CrowdFlower’s workers  is now over 300 million, and the growth of registered members on Kaggle now stands at about 30,000.

The two companies have very different approaches to cloud labor:

CrowdFlower deploys massive groups of workers to complete complex but simple jobs, while Kaggle leverages smaller (and competing) groups of specialists to solve difficult technical challenges.

More on this below:
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REPOST: Training the Cloud with the Crowd: Training A Google Prediction API Model Using CrowdFlower’s Workforce

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NOTE: The following post, by Kevin Cocco, was reposted from the Dialogue Earth blog.

 

kcocco_twitter_data_google_prediction_api

Can a machine be taught to determine the sentiment of a Twitter message about weather? With the data from over 1 million crowd sourced human judgements the goal was to use this data to train a predictive model and use this machine learning system to make judgements. Below are the highlights from the research and development of a machine learning model in the cloud that predicts the sentiment of text regarding the weather. The following are the major technologies used in this research: Google Prediction API, CrowdFlower, Twitter, Google Maps.

The only person that can really determine the true sentiment of a tweet is the person who wrote it. When the human crowd worker makes tweet sentiment judgements only 44% of the time do all 5 humans make the same judgement. CrowdFlower’s crowd sourcing processes are great for managing the art and science of sentiment analysis. You can scale up CrowdFlower’s number of crowd workers per record to increase accuracy, of course at a scaled up cost.

Continue reading »


Ray Kurzweil’s SXSW Keynote Speech Draws Mixed Reaction, According to CrowdFlower’s Sentiment Analysis of 1100+ Tweets

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On Monday we conducted a sentiment analysis of futurist Ray Kurzweil’s SXSW 2012 keynote address, based on Tweets about his talk sent out during and immediately after his talk. In roughly two-three hours, our crowdsource-powered workforce analyzed 1,188 such Tweets, determining the sub-topic of each Tweet and its general sentiment (negative, positive, or neutral). While Kurzweil spoke to a capacity crowd of thousands, the reaction (according to our sentiment analysis) was decidedly mixed.

Here’s the results:

These answers were selected by our contributors from a number of options we put to them, including:

    It’s about expanding our intelligence or IQ
    It’s about Ray Kurzweil’s current projects
    It’s about the singularity
    It’s about artificial intelligence
    It’s about life extension
    It’s about Ray Kurzweil’s thoughts and predictions on the future
    It’s about virtual reality
    It’s about Transhumanism
    It’s about nanotechnology
    It’s about leaving his SXSW talk
    It’s about SOMETHING ELSE (Other)

34% of the Tweets fell into the Other category, 34% were about AI, 10% were related to Kurzweil’s predictions of the future and current projects, and 2% about virtual reality. Interestingly, though Kurzweil’s talk was entitled “Expanding Our Intelligence Without Limit”, only a fraction of Tweets were related to that theme. We saw a similar pattern with Rainn Wilson’s SXSW speech, which was meant to promote his new site SoulPancake, but which also garnered minimal Tweets.

Having selected the sub-topic of each Tweet, our contributors then judged the sentiment — in other words, the author’s emotional tone regarding these Kurzweil topics. Notably, 38% were neutral, while 32% were positive, and 30% negative:

It’s interesting that both Kurzweil’s keynote and Rainn Wilson’s keynote drew such a significant negative reaction, despite the popularity of both celebrities in their respective fields. Perhaps if both speakers directly responded to the Twitter back channel throughout their talk (which typically happens in many SXSW panels and presentations), the overall response might have been more positive. It’s certainly worth considering for future SXSW keynotes.

How did we get this data? As we explained in a post last year, CrowdFlower’s crowdsource-driven sentiment analyses are fairly unique, in that they aggregate human judgements of Tweets, as opposed to an automated, computerized sampling, which is more likely to miss nuances of expression.

Again, check out our sentiment analysis of Rainn Wilson’s SXSW 2012 talk using the same methodology. Speaking of Tweets, don’t miss our top twelve Tweets from the SXSW talk of CrowdFlower founder Lukas Biewald.

Thanks to CrowdFlower’s Josh Eveleth for all the help putting this project together!


Top Twelve Tweets from Kaggle & CrowdFlower’s SXSW Talk on Crowdsourcing

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Thanks to everyone who came to yesterday’s SXSW talk, “Getting a Crowd to Work for You: For Pay or Play?” featuring Lukas Biewald of CrowdFlower and Anthony Goldbloom of Kaggle. It was great to get such a solid and engaged crowd; no mean feat, as our talk overlapped with one by Al Gore and Sean Parker. In case you couldn’t catch it, click here to read the Tweet feed for the talk, hashtagged under #sxsw #wrkrmotive. And below, read our selected top twelve Tweets from the talk:

    @kaggle specializes in high level projects, @crowdflower specializes in jobs that anyone can do.

    Crowdsourcing was once unknown and now it’s become a buzzword. See Google Trends.

    A metereologist used @crowdflower to measure weather by tracking people’s sentiment of weather on Twitter.

    A PhD student in glaciology solved NASA’s dark matter algorithm challenge via a @kaggle competition.

    Leaderboard competition led to leapfrogging improvement of NASA’s dark matter challenge @kaggle

    More than 50% of @crowdflower’s crowdsourcing workforce do real work for virtual goods.

    @CrowdFlower is the largest buyer of virtual goods. Pays for real work crowdsourced jobs.

    Paying $ for a crowdsourced job makes it special & shows workers the company cares enough to pay for it.

    @kaggle aims for its workforce members to be making a real life living from competitions.

    @CrowdFlower crowdsource workforce has a slightly higher median income than average Americans.

    @CrowdFlower has many more women in its workforce — maybe because a lack of gender bias in doing tasks.

    @crowdflower has a sandbox-style self-service site where people can create their own crowdsourcing jobs.

Read all of Kaggle and CrowdFlower’s SXSW talk Tweets here. We’ll be blogging more about some of these topics in future posts, and plan to post the slide deck from the SXSW talk soon.



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