Role of crowdsourcing
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Slides presented by Anna Noel-Storr at UK Cochrane Centre on 16th March 2015 at ContentMinw workshop on Clinical Trials.
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Role of crowdsourcing
- 1. Data and text mining workshop The role of crowdsourcing Anna Noel-Storr Wellcome Trust, London, Friday 6th March 2015
- 2. What is crowdsourcing? “…the practice of obtaining needed services, ideas, or content by soliciting contributions from a large group of people, and especially from an online community, rather than from traditional employees…” Image credit: DesignCareer
- 3. What is crowdsourcing? Knowledge discovery and management Brabham’s problem focused crowdsourcing typology: 4 types
- 4. What is crowdsourcing? Knowledge discovery and management Broadcast search Brabham’s problem focused crowdsourcing typology: 4 types
- 5. What is crowdsourcing? Knowledge discovery and management Broadcast search Peer-vetted creative production Brabham’s problem focused crowdsourcing typology: 4 types
- 6. What is crowdsourcing? Knowledge discovery and management Broadcast search Peer-vetted creative production Distributed human intelligence tasking Brabham’s problem focused crowdsourcing typology: 4 types
- 7. What is crowdsourcing? Knowledge discovery and management Broadcast search Peer-vetted creative production Distributed human intelligence tasking Brabham’s problem focused crowdsourcing typology: 4 types
- 8. Micro-tasking: process Breaking down large corpus of data into smaller units and distributing those units to a large online crowd “the distribution of small parts of a problem”
- 9. Human computation Humans remain better than machines at certain tasks: e.g. Identifying pizza toppings from a picture of a pizza e.g. “preventing obesity without eating like a rabbit”.ti. – autotag: Animal study
- 10. Tools and platforms What platforms and tools exist and how do they work? Image credit: ThinkStock
- 11. The Zooniverse “each project uses the efforts and ability of volunteers to help scientists and researchers deal with the flood of data that confronts them”
- 12. Classification and annotation Galaxy Zoo Operation War Diary
- 13. Health related evidence production Can we use crowdsourcing to identify the evidence in a more timely way? - Known pressure point within the review production - Between 2000 and 5000 citations per new review, but can be much more - A not much loved task Trial identification
- 14. The Embase project Cochrane’s Central Register of Controlled Trials: CENTRAL Embase Crowd Embase auto Step 2: Use a crowd to screen thousands of search results from Embase and feed the identified reports of RCTs into CENTRAL Howwill the crowd do this? Step 1: run a very sensitive search in the largest biomedical database for studies
- 15. The screening tool Three choice s You are not alone! (and you can’t go back) Progress bar Yellow highlights to indicate a likely RCT Red highlights
- 16. The Embase project: recruitment - 900+ people have signed-up to screen citations in 12 months - 110,000+ citations have been collectively screened - 4,000 RCTs/q-RCTs identified by the crowd 0 100 200 300 400 500 600 700 800 900 1000 Feb-14 Mar-14 Apr-14 May-14 Jun-14 Jul-14 Aug-14 Sep-14 Oct-14 Nov-14 Dec-14 Jan-15 Feb-15 Mar-15 Number of Participants Participants
- 17. Why do people do it? Made it very easy to participate (and equally easy to stop!) Gain experience (bulk up the CV) Provide feedback: both to the individual and to the community Wanting to do something to contribute (healthcare is a strong hook) (people are more likely to come back)
- 18. RCT RCT RCT Reject Reject Reject Unsure CENTRAL Bin Resolver How accurate is the crowd? RCTReject Resolver 5%
- 19. Crowd accuracy TP 1565 FP 9 FN 2 TN 2888 TP 415 FP 5 FN 1 TN 2649 The Crowd: INDEX TEST The Crowd: INDEX TEST The Info specialist: REFERENCE STANDARD The Info specialists: REFERENCE STANDARD Validation 1 Validation 2 Sensitivity: 99.9% Specificity: 99.7% Sensitivity: 99.8% Specificity: 99.8% Enriched sample; blinded to crowd decision; dual independent screeners as reference standard Enriched sample; blinded to crowd decision; single independent expert screener (me!) as reference standard; possibility of incorporation bias Individual screener accuracy is also carefully monitored
- 20. How fast is the crowd? Number of weeks Jan 2014 Jul 2014 Jan 2015 6 weeks 5 weeks 2 weeks More screeners and more screeners screening more quickly Length of time to screen one month’s worth of records
- 21. More of the same, and more tasks As the crowd becomes more efficient, we plan to do two things: 1. Increase the databases we search – feed in more citations 2. Offer other ‘micro-tasks’ Feed in more citations – from other databases Bin Y N Screen Annotate, appraise And in these tasks the machine plays a vital and complementary role… e.g. is the healthcare condition Alzheimer’s disease? Y, N, Unsure
- 22. Perfect partnership Machine driven probability + Collective human decision-making It’s not one or the other, the ideal is both
- 23. In summary • Effective method in large scale study identification • Identify more studies, more quickly • No compromise on quality or accuracy • Offers meaningful ways to contribute • Feasible to recruit a crowd • Highly functional tool • Complements data and text mining And enables the move towards the living review Crowdsourcing:
Notes de l'éditeur
- I’m going to talk about the role that crowdsourcing can play in the evidence synthesis process and importantly in the move towards the ‘living review’.
- But first: What is crowdsourcing? Broadly speaking it’s: “…”
- There are different types/and therefore different approaches and tools needed, depending on what it is you need or want from the crowd. Brabham’s problem focused crowdsourcing typology is made up of four types: 1. Knowledge discovery and management – where you get your crowd tasked with finding and collecting information into a common location and format.
- 2. Broadcast search: where the organisation tasks the crowd with solving an empirical problem
- 3. Peer vetted creative production where the organisation tasks a crowd with creating and selecting creative ideas, and…
- 4. Distributed human-intelligence tasking which is where the organisation tasks a crowd with analysing large amounts of information
- And it’s this forth type I primarily want to focus on today.
- It’s about taking a large corpus of data and breaking it down into much smaller units which are then distributed via the internet to a community of willing volunteers to process. The distribution of small parts of a problem
- Some call this kind of work: human computation or human intelligence tasking, because these are tasks where human intelligence is still needed, and where humans still out perform the machine. Such as identifying pizza toppings from an image of a pizza, or, of more relevance to us perhaps: recognizing very quickly that an article is not actually about rabbits just because it has rabbit in the title…
- So what tools and platforms exist and what data does the crowd help to process?
- The Zooniverse, maintained and developed by the Citizen Science Alliance – largest, most successful citizen science platform. It began with one project, Galaxy Zoo, over eight years ago. The platform now hosts over 30 projects and has a world wide community of almost 1.3 million people. In their words, each project uses the “efforts and ability of volunteers to help scientists and researchers deal with the flood of data that confronts them”. Their focus began on all things space related but they have branched out into the humanities and into aspects of healthcare research.
- Here are two examples from two different projects hosted on the Zooniverse. The first, Galaxy Zoo, shows the volunteer an image of a galaxy and then asks a series of questions about that image, such as is it in a spiral shape? The second, Operation War Diary, gets volunteers to tag pages of war diaries.
- In our field, that of evidence appraisal, production and dissemination, we face similar challenges in keeping up with the amount of data produced. And within Cochrane we have been exploring the role of crowdsourcing in helping us to process the flood of data. Our efforts so far have largely focused on one well known pressure point within the review production process: that of trial identification. Our traditional model is under increasing strain as research is exponentially produced. The identified ‘micro-task’ within this broader task of trial identification, is citation screening. It is estimated that the average new systematic review identifies between 2000 and 5000 citations, but this can be much higher for reviews in certain domains or considering certain types of intervention. What if we could find a reliable and fast way to feed all reports of randomised trials into one central repository thereby removing the need for individual, and often small and under resourced review teams to create and run complex searches across multiple databases and then spend months screening those results for that one single review?
- So I’m part of team managing a project rather uninspiringly called: the Embase project. Our aim is use the crowd to help us keep up with the deluge of publications. We run one very sensitive search in Embase (the largest biomedical database in the world) for trials. This search identifies thousands of citations, as you would expect. Some of the results of this search we feed directly into Cochrane central database of controlled trials. What’s left, needs human intervention. It’s these records that we send out to the crowd to classify.
- We do this using a citation screening tool. This tool is fundamental to the crowd’s ability to perform the task. We wanted to develop something that focused almost entirely on the task in hand – that of screening a citation – as you can see the screen is mostly taken up with the citation which is stripped down to just title and abstract. There are some built in pre-defined highlighted words and phrases which are to help guide screeners to the most relevant parts of a citation. Yellow highlighted words and phrases indicate that the record is likely to be describing an RCT and Red highlights indicate that the record is likely to be a Reject. Screeners can also add their own highlights. There are three decision buttons: RCT/CCT, Reject or Unsure and screeners have to make a decision on a record; two other features I just want to quickly point out are the all important progress bar, and the feature which tells you how many others are online screening citations at the same time…
- We have a task, we have a tool, we just need a crowd. We’ve not found this difficult. In a year since going live we’ve had over 900 people sign up to take part. The crowd have screened over 110,000 citations and identified 4,000 reports of RCTs.
- We’ve been really pleased with those metrics; personally, I’m not surprised by them but I do get a lot of people asking me: why do people do it? I don’t think there’s one answer; I think many come to it knowing quite a bit about evidence based medicine and the pressures/challenges of producing timely and robust evidence, and they therefore want to help in this effort (and this provides a very real and immediate way they can help); related to that point I think having made it very easy to contribute has played a significant part in our success. We’ve adopted a rapid onboarding approach; we also offer rapid disembarking – you can stop doing this whenever you want, you are under no obligation and no pressure – this is to fit around you, not the other way round; and then two others factors for which I’m well aware we haven’t realised to their full potential, (more related to keeping people doing it) – and that’s around gaining experience, getting feedback on your performance, and perhaps being able to offer some progression or more tailored rewards.
- So we’ve managed to recruit a crowd and they collectively screened well over 100,000 citations. How do we ensure quality? How to ensure that the records are ending up in the right place (CENTRAL for RCTs), the ‘bin’ for Rejects? We use a simple, yet robust algorithm which goes like this: three consecutive agreements on a record sends that record off to Central or the bin without further intervention. Any disagreements or any records classified as Unsure go into a pot for a Resolver level screener to resolve. Happily this constitutes only about 5% of all records screened.
- So how well has this algorithm performed? We’ve performed two validation studies so far and two more are underway. Each of the those involved taking a random sample of crowd screened records and performing a re-screen on those records by ‘experts screeners’ blind to the crowd decisions. In both validation studies, crowd sensitivity (so the crowds ability to identify all the RCTs) and the crowd’s specificity (the crowd’s ability to identify records not eligible for central) has come out at over 99%. We’re happy with that.
- So we are pleased with accuracy. What about volume? Are the crowd screening enough? Collectively we are speeding up. [explain graph] This is an exciting place to be….
- It means we can: Look to increase the number of databases we search and screen in this way (ie. using crowd), and We can provide the crowd with more tasks aimed at contributing significantly to helping identify trials in a much more timely way, with no compromise on quality or accuracy. And in any such task, the machine plays a vital and complementary role..
- as we discover more about the very real role that text mining has to play, we can start to see further efficiencies reached by using both crowd and machine in a way that plays to each one’s strengths: the machine generating the probabilities and the crowd making the accurate collective decisions.