In this paper we study the emotional expression of people who edit the Wikipedia. We classify Wikipedia editors according to administrator status (administrators vs. regular editors) and by gender (male vs. female).

Several patterns emerge from the data:

  • Administrators maintain a rather neutral, impersonal tone, and are more task-oriented than regular editors (who are more relationship-oriented).
  • Female editors communicate more often in a manner that promotes emotional connection.
  • Editors tend to interact more often with editors having similar emotional styles (e.g. editors who often express anger connect more with one another, as shown in the graphic where "angry" editors appear in red).

Paper available (open access): Emotions under Discussion: Gender, Status and Communication in Online Collaboration by Daniela Iosub, David Laniado, Carlos Castillo, Mayo Fuster Morell, and Andreas Kaltenbrunner. PLOS ONE. August 2014. DOI:10.1371/journal.pone.0104880

A friend asked me to explain how does an automatic system for classifying documents, such as AIDR, works.

We are going to do this in three steps, first a preliminary with an example on the risk of having a heart attack, then a little generalities, then the real thing.

Preliminary: predicting heart attack risk

Imagine a doctor with several patients that she has been following for several years. She has a clinical file for each patient in which she has noted the following: whether the patient smokes or not (which she writes as "smokes=y, smokes=n". whether the patient has high blood pressure or not (which she writes as "hypertensive=y, hypertensive=n", and whether the patient practices sports or not (which she writes as "sports=y, sports=n").

Finally, the doctor also notes if the patient has had a heart attack, written as "STROKE=y, STROKE=n":

  • Patient 1: smokes=y, hypertensive=y, sports=n, STROKE=y
  • Patient 2: smokes=y, hypertensive=n, sports=n, STROKE=y
  • Patient 3: smokes=y, hypertensive=n, sports=y, STROKE=n
  • Patient 4: smokes=n, hypertensive=y, sports=y, STROKE=n
  • Patient 5: smokes=n, hypertensive=y, sports=n, STROKE=y

Now, one can extract certain statistics from this data. For instance, patients 3 and 4 practice sports and didn't have a stroke, while patients 1, 2, and 5, don't practice sports and did have a stroke. From this data alone, one could conclude that practicing sports may help prevent a stroke (where the "may help" part doesn't come from this data but just from the recognition that 5 patients is not a lot).

We can also learn that 66% of the patients who smoke had heart strokes in this sample.

Now, if we look for combinations of factors, we can extract more information. For instance, by looking with care at the data, one can realize that, disregarding the practice of sports, everybody in this sample who either smokes or is hypertensive has had a heart attack. Obviously, with more data we can be more certain about how good are the combinations of factors that we learn, in terms of how closely they are related to a certain outcome.

Statistical machine learning

There are so many combinations of factors that even in the small dataset above, with five patients, exploring all the combinations and outcomes is very time consuming. Fortunately, there is where a well established research field, statistical machine learning, that studies precisely this problem.

This research field has studied for years different methods to automatically and quickly find relationships between elements in large-scale data. This process is known as learning, and there are many, many, techniques to do it.

In general, what these methods need in order to be able to learn effectively is: (i) a large amount of data, and (ii) the "right" data. In the example above, the medical doctor who interviewed the patients asked the "right" questions. If she had written instead their eye color or other irrelevant factor, learning something about heart stroke risk would have been much more difficult.

Classifying text

Text classification is not much different. Instead of 3 factors (smokes, hypertensive, and sports), we will have hundreds of thousands of factors, one for each word in the dictionary. The factors will take the form "word=y, word=n" where the "word" can be any word, and we write "y" when the document contains the word and "n" when it doesn't.

The outcomes will be different types of documents. Suppose our documents are tweets and we want to separate those that contain information about damage to infrastructures (DAMAGE=y) from those who don't (DAMAGE=n). Again, you can have the following table, in which for each tweet you have one factor for each word in the tweet, and the outcome has been written by an expert who has looked at the tweet and decided if it contains infrastructure damage or not:

Tweet1: ... building=y, ..., collapsed=y, ..., DAMAGE=y
Tweet2: ... bridge=y, ..., collapsed=y, ..., DAMAGE=y
Tweet3: ... bridge=y, ..., playing=y, ..., DAMAGE=n
Tweet1000: ... bridge=y, ..., hearts=y, ..., DAMAGE=n

Again, we can apply any of the statistical machine learning method to learn what are the combinations of words that indicate the presence of infrastructure damage reports.

That's all. Once we learn those combinations, we can use them automatically to evaluate new tweets. In this case, the learning method will also output a confidence, which you can understand roughly as the percentage of tweets having those factors that were found to have that predicted outcome in the data used to learn (it is more complex than that, but that is a good approximation).

When the data is large, in general it is very difficult for a human to be able to spot a pattern better than what a computer algorithm can do. This is why crafting rules by hand (containing "bridge" implies "DAMAGE" unless the tweet also contains "playing" or "play" or "ace" or "heart" or ...) is not only time-consuming but also tends to yield lower accuracy than automatic methods, and is in general a bad idea.

In the case of text, we also use other factors (we call them "features" or "attributes") in addition to words. For instance, we can take all sequences of two words or three words (which we call "word bi-grams" and "word tri-grams"). We can also look at the position of some words in the phrase, as to whether the word was capitalized or not, how many times it appeared, etc. For the learning method, this is all the same, simply more factors that can be exploited to learn about the data.

Further readings: lots of them, but you can start with the Wikipedia page on decision trees, which is a popular and easy to understand method for statistical machine learning.

A new and exciting dataset is available. It contains the number of visitors, average visit time, "tweets" on Twitter, and "likes" on Facebook, for a set of thousands of web pages. The data is aggregated on windows of 5-minutes, during a period of 48 hours.

We are inviting researchers to participate in a competition: an ECML/PKDD Discovery Challenge that consists on predicting the total activity after 48 hours, by observing only the first hour of life of a web page. This is an important task that has significant practical applications.

Dataset available courtesy of Chartbeat Inc.

Carlos Castillo and Josh Schwartz
Predictive Web Analytics Challenge Co-Chairs

Presentation on November 14th, 2013 at the Tow Center, Columbia Journalism School. (New York, USA).

I had the privilege to work with Wei Chen (Microsoft Research) and Laks V.S. Lakshmanan (University of British Columbia) on a book for the Synthesis Lectures on Data Management series, edited by M. Tamer Özsu and published by Morgan and Claypool.

This book starts with a detailed description of well-established diffusion models, including the independent cascade model and the linear threshold model, that have been successful at explaining propagation phenomena. We describe their properties as well as numerous extensions to them, introducing aspects such as competition, budget, and time-criticality, among many others. We delve deep into the key problem of influence maximization, which selects key individuals to activate in order to influence a large fraction of a network. Influence maximization in classic diffusion models including both the independent cascade and the linear threshold models is computationally intractable, more precisely #P-hard, and we describe several approximation algorithms and scalable heuristics that have been proposed in the literature. Finally, we also deal with key issues that need to be tackled in order to turn this research into practice, such as learning the strength with which individuals in a network influence each other, as well as the practical aspects of this research including the availability of datasets and software tools for facilitating research. We conclude with a discussion of various research problems that remain open, both from a technical perspective and from the viewpoint of transferring the results of research into industry strength applications

The book is available for USD 20 or through many libraries:

Two chapters are available for free:


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