Friday, November 16, 2018

Distribution of paper citations over time

A few weeks ago we had a discussion about citations, and how we can compare the citation impact of papers that were published in different years. Obviously, older papers have an advantage as they have more time to accumulate citations.

To compare papers, just for fun, we ended up opening the profile page of each paper in Google Scholar, and we analyzed the paper citations years by year to find the "winner." (They were both great papers, by great authors, fyi. It was more of a "Lebron vs. Jordan" discussion, as opposed to anything serious.)

This process got me curious though. Can we tell how a paper is doing at any given point in time? How can we compare a 2-year-old article, published in 2016, with 100 citations against a 10-year-old document, published in 2008, with 500 citations?

To settle the question, we started with the profiles of faculty members in the top-10 US universities and downloaded about 1.5M publications, across all fields, and their citation histories over time.

We then analyzed the citation histories of these publications, and, for each year, we ranked the papers based on the number of citations received over time. Finally, we computed the citation numbers corresponding to different percentiles of performance.

Cumulative percentiles

The plot below shows the number of citations that a paper needs to have at different stages to be placed in a given percentile.

A few data points, focusing on certain age milestones: 5-years after publication, 10-years after publication, and lifetime.

  • 50% line: The performance of a "median" paper. The median paper gets around 20 citations 5 years after publication, 50 citations within 10 years, and around  100 citations in its lifetime. Milestone scores: 20,50,90
  • 75% line: These papers perform "better," citation-wise than 75% of the remaining papers with the same age. Such papers get around 50 citations within 5 years, 100 citations within 10 years of publication, and around 200 citations in their lifetime. Milestone scores: 50,100,200
  • 90% line: These papers perform better than 90% of the papers in their cohort. Around 90 citations within 5 years, 200 citations within 10 years, and 500 citations in their lifetime. Milestones scores: 90,200,500


Yearly percentiles and peak years

We also wanted to check at which point papers reach their peak, and start collecting fewer citations. The plot below shows the percentiles based on the yearly numbers of accumulated citations. The vast majority of papers tend to reach their peak 5-10 years after publication; the number of yearly citations starts declining after 5-10 years.


Below is the plot of the peak year for a paper based on the paper percentile:


There is an interesting effect around the 97.5% percentile: After that level, it seems that a 'rich-gets-richer' effect kicks in, and we effectively do not observe a peak year. The number of citations per year keeps increasing. You could call these papers the "classics".

What does it take to be a "classic"? 200 citations at 5 years or 500 citations at 10 years.

Monday, January 29, 2018

How many Mechanical Turk workers are there?

TL;DR: There are about 100K-200K unique workers on Amazon. On average, there are 2K-5K workers active on Amazon at any given time, which is equivalent to having 10K-25K full-time employees. On average, 50% of the worker population changes within 12-18 months. Workers exhibit widely different patterns of activity, with most workers being active only occasionally, and few workers being very active. Combining our results with the results from Hara et al, we see that MTurk has a yearly transaction volume of a few hundreds of millions of dollars.

For more details read below, or take a look at our WSDM 2018 paper.

--

Question

A topic that frequently comes up when discussing Mechanical Turk is "how many workers are there on the platform"?

In general, this is a question that is very easy for Amazon to answer, but much harder for outsiders. Amazon claims that there are 500,000 workers on the platform. How can we check the validity of this statement?

--

Basic capture-recapture model

A common technique for this problem is the capture-recapture technique, that is widely used in the field of ecology, to measure the population of a species.

The simplest possible technique is the following:
  • Capture/marking phase: Capture $n_1$ animals, mark them, and release them back. 
  • Recapture phase: A few days later, capture $n_2$ animals. Assuming there are $N$ animals overall, $n_1/N$ of them are marked. So, for each of the $n_2$ captured animals, the probability that the animal is marked is $n_1/N$ (from the capture/marking phase).
  • Calculation: On expectation,  we expect to see $n_2 \cdot \frac{n_1}{N}$ marked animals in the recapture phase. (Notice that we do not know $N$.) So, if we actually see $m$ marked animals during the recapture phase, we set $m = n_2 \cdot \frac{n_1}{N}$ and we get the estimate that:

     $N = \frac{n_1 \cdot n_2}{m}$.
In our setting we adapted the same idea, where "capture" and "recapture" correspond to participating in a demographics survey. In other words, we "capture/mark" MTurk users that complete the survey in one day. Then, in another day, we also "recapture" by surveying more workers and we see how many workers overlap in the two surveys.

--

First (naive) attempt

We decided to apply this technique to estimate the size of the Mechanical Turk population. We considered as "capture" period the set of surveys running over a period of 30 days. Then we considered as "recapture" period, the surveys that we ran on another 30-day period. The plot below shows the results.


The x-axis shows the beginning of the recapture period, and the y-axis the estimate of the number of workers. The color of each dot corresponds to the difference in time between the capture-recapture periods: black is a short time, and red is a long time.

If we focus on the black-color dots (~60 days between the surveys), we get a (naive) estimate of around 10K-15K workers. (Warning: this is incorrect.)

While we could stop here, we see some results that are not consistent with our model. Remember, that color encodes time between samples: black is for short time (~2 months) between samples, red is for long time (~2yrs) between samples. Notice that, as the time between the two periods increases, the estimates are becoming higher, and we get the "rainbow cake" effect in the plot. For example, for July 2017, our estimate is 12K workers if we compare with a capture from May 2017, but the estimate goes up to 45K workers if we compare with a sample from May 2015. Our model, though, says that the time between captures should not affect the population estimates. This indicates that there is something wrong with the model.

--

Assumptions of basic model

The basic capture-recapture estimation described above relies on a couple of assumptions. Both of these assumptions are violated when applying this technique to an online environment.
  • Assumption of no arrivals / departures ("closed population"): The vanilla capture-recapture scheme assumes that there are no arrivals or departures of workers between the capture and recapture phase.
  • Assumption of no selection bias ("equal catchability"): The vanilla capture-recapture scheme assumes that every worker in the population is equally likely to be captured.
In ecology, the issue of closed population has been examined under many different settings (birth-death of animals, immigration, spatial patterns of movement, etc.) and there are many research papers on the topic. Catchability, by comparison, has received comparatively less attention. This is reasonable, as in ecology the assumption of closed population is problematic in many settings. By comparison, assuming that the probability of capturing an animal is uniform among similar animals is reasonable. Typically the focus is on segmenting the animals into groups (e.g., nesting females vs hunting males) and assign different catchability heterogeneity to groups (but not to individuals). 

In online settings though, the assumption of equal catchability is more problematic. First we have the activity bias: Workers exhibit very different levels of activity: A worker who works every day is much more likely to see and complete a task, compared to someone who works once a month. Similarly, we have a selection bias: Some workers may like to complete surveys, while others may avoid such tasks.

So, to improve our estimates, we need to use models that alleviate these assumptions.

--

Endowing workers with survival probabilities 

We can extend the model, allowing each worker to have a certain survival probability, to allow workers to "disappear" from the platform. If we see the plot above, we can see that the population estimate increases as the time between two samples increases. This hints that workers leave the platform, and the intersection of capture-recapture becomes smaller over time. 

If we account for that, we can get an estimate that the "half-life" of a Mechanical Turk worker is between 12-18 months. In other words, approximately 50% of the Mechanical Turk population changes every 12-18 months. 

--

Endowing workers with propensity to participate

We can also extend the model by associating a certain propensity for each worker. The propensity is the probability that a worker is active and willing to participate in a task, at any given time.

In our work, we assumed that the underlying "propensity to participate" follows a Beta distribution across the worker population, and the parameters of the Beta distribution are unknown. When we assume that follow a Beta distribution, then the probability that a worker participates in the survey k times, follows a Beta Binomial distribution. Since we know how many workers participated k times in our surveys, it is then easy to estimate the underlying parameters of the Beta distribution.

Notice that we had to depart from the simple "two occasion" model above, and instead use multiple capturing periods over time. Intuitively, workers that have high propensity to participate will appear many times in our results, while inactive workers will appear only a few times.

By doing this analysis, we can observe that (as expected) the distribution of activity is highly skewed: A few workers are very active in the platform, while others are largely inactive. A nice property of the Beta distribution is its flexibility: Its shape can be pretty much anything: uniform, Gaussian-like, bimodal, heavy-tailed... you name it. 




In our analysis, we estimated that the propensity distribution follows a Beta(0.3,20) distribution. We plot above the "inverse CDF" of the distribution (Inverse CDF: "what percentage of the workers have propensity higher than x").

As you can see, the propensity follows a familiar (and expected) pattern. Only 0.1% of the workers have propensity higher than 0.2, and only 10% have propensity higher than 0.05.

Intuitively, a propensity of 0.2 means that the worker is active and willing to participate 20% of their time (this is roughly equivalent to full-time level of activity; full-timer employees work around 2000 hrs per year, out of 24*365 available hours in a year). A propensity of 0.05 means that the worker is active and available approximately 24 hr * 0.05 ~ 1 hour per day.

--

How big is the platform?

So, how many workers are there? Under such highly skewed distributions, giving an exact number for the number of workers is rather futile. The best that you can do is give a ballpark estimate, and hope to be roughly correct on the order of magnitude. What our estimates are showing is that there are round 180K distinct workers in the MTurk platform. This is good news for anyone who is trying to reach a large number of distinct workers through the platform. 

Our analysis also allows us to estimate how many workers are active and willing to participate in our task at any given time. For that, we estimate that around 2K to 5K workers are available, at any given time. If we want to convert this number to full-time employee equivalence, this is equivalent to 10K-25K full-time workers.

The latter part also allows us to give some low and high estimates on the transaction volume of MTurk. 
  • Lower bound: Assuming 2K workers active at any given time, this is 2000*24*365=17,520,000 work hours in a year. If we assume that the median wage is \$2/hr, this is roughly \$35M/yr transaction volume on Amazon Mechanical Turk (with Amazon netting ~\$7M in fees).
  • Upper bound: Assuming 5K workers active at any given time, this is 5000*24*365=43,800,000 work hours in a year. If we assume average wage of \$12/hr, this is around \$525M/yr transaction volume (with Amazon netting ~$100M in fees).
I understand that a range of \$35M to \$500M may not be very helpful, but these are very rough estimates. If someone wanted my own educated guess, I would put it somewhere in the middle of the two, i.e., transaction volume of a few hundreds of millions of dollars.



Tuesday, January 17, 2017

Why was my Amazon Mechanical Turk registration denied?

(This is my answer to a question posted on Quora)

Mechanical Turk is a platform for work. Workers get paid, which makes now Amazon a payment processor. Payment processors are moving money on behalf of other people, and therefore are under heavy scrutiny from the US government for issues related to money laundering (AML), counter-terrorism, tax compliance, etc.

One of the key things that is required from financial institutions is to have a “Customer Identification Program” (CIP), also known as “Know Your Customer” (KYC) process. The CIP/KYC is a set of procedures that the financial institution needs to follow to establish that they know the true identity of a customer. The processes that each financial institutions follows vary, and the exact processes are rarely available to the public, as they are considered security measures. Furthermore, the practices are regularly monitored by regulators (OCC, Fed, FinCEN, etc) and change over time to follow best practices.

In your particular case, the most likely reason is that Amazon was not able to verify your identity.

If you are in the US, Amazon most probably can get your SSN and other personal details and verify whether you are a real person. However, even if you live in the US, if you have no credit history, no bank accounts, and so on, the verification will come back with low confidence. Following standard risk management processes, Amazon could plausibly reject such applications, as part of their CIP processes: it is better to have a false negative (rejecting a normal account) than having a false positive (e.g., accepting an account that will be involved in money laundering or tax-evasion schemes).

For other countries, the ability of Amazon, to follow CIP/KYC processes that conform to the US regulations, varies. I assume, for example, that the cooperation of US with UK or Australian authorities is much smoother compared to, say, Chinese authorities. So, if you live outside the US, the probability of having your account approved depends on how robust is the ability of Amazon to verify individual identities in your country.

Given that Amazon gets paid by requesters, I assume their focus is to establish CIP processes first in regions where potential requesters reside, which is not always the place where workers reside. This also means that you are more likely to be approved if you first register as a requester (assuming this is an option for you), and then try to create the worker account.

Sunday, March 13, 2016

AlphaGo, Beat the Machine, and the Unknown Unknowns

In Game 4, of the 5-game series between AlphaGo and Lee Sedol, the human Go champion, Lee Sedol managed to get his first win. According to the NY Times article:

Lee had said earlier in the series, which began last week, that he was unable to beat AlphaGo because he could not find any weaknesses in the software's strategy. But after Sunday's match, the 33­ year­ old South Korean Go grandmaster, who has won 18 international championships, said he found two weaknesses in the artificial intelligence program. Lee said that when he made an unexpected move, AlphaGo responded with a move as if the program had a bug, indicating that the machine lacked the ability to deal with surprises.



This part reminded me of one of my favorite papers:  Beat the Machine: Challenging Humans to Find a Predictive Model’s “Unknown Unknowns”

In the paper, we tried to use humans to "beat the machine" and identify vulnerabilities in a machine learning system. The key idea was to reward humans whenever they identify cases where the machine fails, while also being confident that it provides the correct answer. In other words, we encouraged humans to find "unexpected" errors, not just cases where naturally the machine was going to be uncertain.



As an example case, consider a system that detects adult content on the web. Our baseline machine learning system had an accuracy of ~99%. Then, we asked Mechanical Turk workers to do the following task: Find web pages with adult content that the machine learning system classifies as non-adult with high confidence. The humans had no information about the system, and the only thing they can do was to submit a URL and get back an answer.

The reward structure was the following: Humans get \$1 for each URL that the machine misses, otherwise they get \$0.001. In other words, we provided a strong incentive to find problematic cases.

After some probing, humans were quick to uncover underlying vulnerabilities: For example, adult pages in Japanese, Arabic, etc., were classified by our system as non-adult, despite their obvious adult content. Similarly for other categories, such as hate speech, violence, etc. Humans were quickly able to "beat the machine" and identify the "unknown unknowns".



Simply told, humans were able to figure out what are the likely cases that the system may have missed during training. At the end of the day, the training data is provided by humans, and no system has access to all possible training data. We operate in an "open world" while training data implicitly assume a "closed world".

As we see from the AlphaGo example, since most machine learning systems rely on existence of training data (or some immediate feedback for their actions), machines may get into problems when they have to face examples that are unlike any examples they have processed their training data.

We designed our Beat The Machine system to encourage humans to discover such vulnerabilities early.

In a sense, our BTM system is s like hiring hackers to break into your network, to identify security vulnerabilities before they become a real problem. The BTM system applies this principle for machine learning systems, encouraging a period of intense probing for vulnerabilities, before deploying the system in practice.

Well, perhaps Google hired Lee Sedol with the same idea: Get the human to identify cases where the machine will fail, and reward the human for doing so. Only in that case, AlphaGo managed to eat its cake (figure out a vulnerability) and have it too (beat Lee Sedol, and not pay the \$1M prize) :-)

Monday, February 29, 2016

A Cohort Analysis of Mechanical Turk Requesters

In my last post, I examined the number of "active requesters" on Mechanical Turk, and concluded that there is a significant decline in the numbers over the last year. The definition of "active requester" was: "A requester is active at time X if he has a HIT running at time X". A potential issue with this definition is that an improvement in the speed of HIT completion (e.g., due to increased labor supply) could drive down that number.

For this reason, I decided to perform a proper cohort analysis for the requesters on Mechanical Turk.  In the cohort analysis that follows, we will examine how many requesters that have first appeared in the platform on a given month (say September 2015), are still posting tasks in the subsequent months.

Here is the resulting "layer cake plot" that indicates that happens in each cohort. Each of the layers corresponds to requesters that were first seen on a given month. (code, data) (Read this post, if want a  little bit more background on how the plot should "look like".)


For example, the bottom layer corresponds to all the requesters that were first seen on May 2014 (the first month that the new version of MTurk Tracker started collecting data). We can see that we had ~2700 "new" requesters on that month. (The May-2014 cohort obviously contains all prior cohorts in our dataset, as we do not know when these requesters really started posting.) Out of these requesters, approximately 1700 also posted a task on June 2014 or later, approximately 1000 of these have posted a task on March 2015 or later, and approximately 500 have posted a task on February 2016.

The layer on top (slightly darker blue) illustrates the evolution of the June 2014 cohort. By stacking them on top of each other, we can see the composition of the requesters that have been active in every single month.

As the plot makes obvious, until March 2015, the acquisition of new requesters every month was compensating for the requesters that were lost from the prior cohorts. However, starting March 2015, we start seeing a decline in the overall numbers, as the total decline in requesters from prior cohorts dominates the acquisition of new requesters. So, the cohort analysis supports the conclusions of the prior post, as the trends and conclusions are very similar (always good to have a few robustness checks).

Of course, a more comprehensive cohort analysis would also analyze the revenue generated by each cohort, and not just the number of active users. That requires a little bit more digging in the data, but I will do that in a subsequent post.