Assignment 7: Named Entity Recognition

For this assignment you will evaluate the performance of OpenCalais, a commercial entity extraction service, against your hand annotations.

1. Pick five random news stories and hand-annotate them. Pick an English-language news site with many stories on the home page, or a section of such a site (business, sports, etc.) Then generate five random numbers from 1 to the number of stories on the page. Cut and paste the text of each article into a separate  file, and save as plain text (no HTML, no formatting.)

2. Detect entities by hand in each article. Paste the text of each article into an RTF or Word document and go through it, underlining every entity. Count every mention of a person, place, or organization, including alternate names (“the president”) and pronoun references. Count how many entity references appear in each document (multiple mentions of the same entity all count.)

3. Now run each document through OpenCalais. You can paste the text into the demo page here. Now compare the results to your hand-annotations to produce a confusion matrix:

  • True Positives: entities marked by you and found by OC
  • False Negatives: entities marked by you and not found by OC
  • False Positives: entities found by OC but not marked by you

4. Turn in:

  • Your hand-marked documents.
  • A spreadsheet. Please turn in a spread sheet with one row per document, and four columns: total entities marked by you, true positives, false negatives, false positives.
  • Make sure your spreadsheet includes totals of these four columns across all documents
  • Your analysis. Report on any patterns in the failures that you see. Where is OpenCalais most accurate? Where is it least accurate? Are there predictable patterns to the errors? Are there ambiguities as to what is really an entity?
This assignment is due before class on Wednesday,  December 5.

Assignment 6: Social Network Analysis

For this assignment you will analyze a social network using three different centrality algorithms, and compare the results.

1. Download and install Gephi, a free graph analysis package. It is open source and runs on any OS.

2. Download the data file lesmis.gml from the UCI Network Data Repository.  This is a network extracted from the famous French novel Les Miserables — you may also be familiar with the musical and the recent movie. Each node is a character, and there is an edge between two characters if they appear in the same chapter. Les Miserables is written in over 300 short chapters, so two characters that appear in the same chapter are very likely to meet or talk in the plot of the book. The edges are weighted, and the weight is the number of chapters those characters appear together in.

3. Open this file in Gephi, by choosing File->Open. When the dialog box comes up, set the “Graph Type” type to “Undirected.” The graph will be plotted. What do you see? Can you discern any patterns?

4. Now arrange the nodes in a nicer way, by choosing the “Force Atlas 2″ layout algorithm from the Layout menu at left and pressing the “Run” button. When things settle down, hit the “Stop” button. The graph will be arranged nicely, but it will be quite small.  You can zoom in using the mouse wheel (or two fingers on the trackpad on a mac) and pan using the right mouse button.

5. Select the “Edit” tool from the bottom of the toolbar on the left. It looks like a mouse pointer with question mark next to it:

Gephi info pointer

6. Now you can click on any node to see its label, which is the name of the character it represents. This information will appear in the “Edit” menu in the upper left. Here’s the information for the character Gavroche.

Gephi Gavroche properties

Click around the various nodes in the graph. Which characters have been given the most central locations? If you are familiar with the story of Les Miserables, how does this correspond to the plot? Are the most central nodes the most important characters?

7. Make Gephi color nodes by degree. Choose the “Ranking” tab from panel at the upper left, then select the “Nodes” tab, then “Degree” from the drop-down menu. Press the “Apply” button.

screen-shot-2013-01-29-at-12-41-00-pm

Now the nodes with the highest degree will be darker. Do these high degree nodes correspond to the nodes that the layout algorithm put in the center? Are they the main characters in the story?

8. Now make Gephi compute betweenness and closeness centrality by pressing the “Run” button for the Network Diameter option under “Network Overview” in to the right of the screen.

Gephi network overview

You will get a report with some graphs. Just click “Close”. Now betweenness and closeness centrality will appear in the drop-down under “Ranking,” in the same place where you selected degree centrality earlier, and you can assign colors based on either run by clicking the “Apply” button.

Also, the numerical values for betweenness centrality and closeness centrality will now appear in the “Edit” window for each node.

Select “Betweenness Centrality” from the drop-down meny and hit “Apply.” What do you see? Which characters are marked as important? How does it differ from the characters which are marked as important by degree?

Now select “Closeness Centrality” and hit “Apply.” (Note that this metric uses a scale which is the reverse of the others — closeness measures average distance to all other nodes, so small values indicate more central nodes. You may want to swap the black and white endpoints of the color scale to get something which is comparable to the other visualizations.) How does closeness centrality differ from betweeness centrality and degree? Which characters differ between closeness and the other metrics?

9. Which centrality algorithm would you prefer to use to understand the structure of Les Miserables? Why? How would you validate your choice if you didn’t already know the story? That is the situation a journalist is in when they analyze unknown data.

Turn in: your answers to the questions in steps 3, 6, 7, 8 and 9, plus screenshots for the graph plotted with degree, betweenness centrality, and closeness centrality. (To take a screenshot: on Windows, use the Snipping Tool. On Mac, press ⌘ Cmd + ⇧ Shift + 4. If you’re on Linux, you get to tell me)

What I am interested in here is how the values computed by the different algorithms correspond to the plot of Les Miserables (if you are familiar with it), and how they compare to each other. Telling me that “Jean Valjean has a closeness centrality of X” is not a high-enough level interpretation — your couldn’t publish that in a finished story, because your readers won’t know what that means.

Due before class on Wednesday, Nov 28

Assignment 2: Filter Design

For this assignment you will design an information filtering algorithm. You will not implement it, but you will explain your design criteria and provide a filtering algorithm in sufficient technical detail to convince me that it might actually work — including psuedocode.

1. Decide who your users are. Journalists? Professionals? General consumers? Someone else?

2. Decide what you will filter. You can choose:

  • Social network updates, like the Facebook news feed or Twitter trending topics
  • A news organization recommendation engine
  • The whole web, like Google News
  • something else

3. List all available information that you have available as input to your algorithm. If you want to filter Facebook or Twitter, you may pretend that you are the company running the service, and have access to all posts and user data — from every user. You also also assume you have a web crawler or a firehose of every RSS feed or whatever you like, but you must be specific and realistic about what data you are operating with.

4. Argue for the design factors that you would like to influence the filtering, in terms of what is desirable to the user, what is desirable to the publisher (e.g. Facebook or a news organization), and what is desirable socially. Explain as concretely as possible how each of these (probably conflicting) goals might be achieved through in software. You can imagine that you have a UI that supports certain types of interactions (e.g. likes, votes, ratings) or encourages users to act in certain ways (e.g. following) that generate data for you.

5. Write psuedo-code for a function that produces a “top stories” list. This function will be called whenever the user loads your page or opens your app, so it must be fast and frequently updated. You can assume that there are background processes operating on your servers if you like. Your psuedo-code does not have to be executable, but it must be specific and unambiguous, such that I could actually go and implement it. You can assume that you have libraries for classic text analysis and machine learning algorithms. So, you don’t have to spell out algorithms like TF-IDF or item-based collaborative filtering, or anything else you can dig up in the research literature, but simply say how you’re going to use such building blocks. If you use an algorithm we haven’t discussed in class, be sure to provide a reference to it.

6. Write up steps 1-5. The result should be no more than three pages. You must be specific and plausible. You must be clear about what you are trying to accomplish, what your algorithm is, and why you believe your algorithm meets your design goals (though of course it’s impossible to know for sure without testing; but I want something that looks good enough to be worth trying.)

Due before class, October 10

Assignment 1: Topic Modeling

This assignment is designed to help you develop a feel for the way topic modeling works, the connection to the human meanings of documents, and one way of handling the time dimension. First you will analyze a corpus of AP news articles. Then you’ll look at the State of the Union speeches, and report on how the subjects have shifted over time in relation to historical events.

Note: This assignment requires reading the documents, not just running algorithms on them. I am asking you to tell me how well these algorithms capture the meaning of the documents, and you can only determine meaning by reading the documents. So when you see a word scored highly within a document set, go read the documents that contain that word. When a topic ranks high for a document, go read the documents that contain that topic.

1. Load the data. To begin with you’ll try LDA on a homogeneous document set of short clean articles: this collection of AP wire stories. Get this CSV loaded as a list of strings, one per document.

2) Generate TF-IDF vectors. Use the gensim package to generate tf-idf weighted document vectors. Check out the gensim example code here. You will need to go through the file twice: once to generate the dictionary (the code snippet starting with “collect statistics about all tokens”) and then again to convert each document to what gensim calls the bag-of-words representation, which is un-normalized term frequency (the code snippet starting with “class MyCorpus(object)”

Note that there is implicitly another step here, which is to tokenize the document text into individual word features — not as straightforward in practice as it seems at first, but the example code just does the simplest, stupidest thing, which is to lowercase the string and split on spaces. You may want to use a better stopword list, such as this one.

Once you have your Corpus object, tell gensim to generate tf-idf scores for you like so.

3) Do LSI topic modeling. You can apply LSI to the tf-idf vectors, like so. You will have to supply the number of topics to generate. Figuring out a good number is part of the assignment. Print out the resulting topics, each topic as a lists of word coefficients. Now, sample ten topics randomly (not the first ten, a random ten!) from your set for closer analysis. Try to annotate each of these ten topics with a short descriptive name or phrase that captures what it is “about.” You will have to refer to the original documents that contain high proportions of that topic, and you will likely find that some topics have no clear concept.

Turn in: your annotated topics plus a comment on how well you feel each “topic” captured a real human concept.

4) Now do LDA topic modeling. Repeat the exercise of step 3 but with LDA instead, again trying to annotate ten randomly sampled topics. What is different? Did it better capture the meaning of the documents? If so, “better” how?

Turn in: your annotated topics plus a comment on how LDA differed from LSI.

5) Now apply LDA to the State of the Union. Download the source data file state-of-the-union.csv. This is a standard CSV file with one speech per row. There are two columns: the year of the speech, and the text of the speech. You will write a Python program that reads this file and turns it into TF-IDF document vectors, then prints out some information. Here is how to read a CSV in Python. You may need to add the line

csv.field_size_limit(1000000000)

to the top of your program to be able to read this large file. Also, this file is probably too big to open in Excel or to read with Pandas.

The file is a csv with columns year, text. Note: there are some years where there was more than one speech! Design your data structures accordingly.

6) Determine how speeches have changed over the 20th century. We’ll use a very simple algorithm:

  • Generate TF-IDF vectors for the entire corpus
  • Group speeches by decade
  • Sum TF-IDF vectors for all speeches in each decade to get one “summary” vector per decade
  • Print out top 10 most highly ranked words in each decade vector

Turn in: an analysis of how the topics of the State of the Union have changed over the decades of the 20th century. What patterns do you see? Can you connect the terms to major historical events? (wars, the great depression, assassinations, the civil rights movement, Watergate…)

 

Syllabus Fall 2018

Featured

The course is a hands-on, research-level introduction to the areas of computer science that have a direct relevance to journalism, and the broader project of producing an informed and engaged public. We study two big ideas: the application of computation to produce journalism (such as data science for investigative reporting), and journalism about areas that involve computation (such as the analysis of credit scoring algorithms.)

Alon the way we will touch on many topics: information recommendation systems but also filter bubbles, principles of statistical analysis but also the human processes which generate data, network analysis and its role in investigative journalism, visualization techniques and the cognitive effects involved in viewing a visualization.

Assignments will require programming in Python, but the emphasis will be on clearly articulating the connection between the algorithmic and the editorial. Research-level computer science material will be discussed in class, but the emphasis will be on understanding the capabilities and limitations of this technology.

Format of the class, grading and assignments.
This is a fourteen week, six point course for CS & journalism dual degree students. (It is a three point course for cross-listed students, who also do not have to complete the final project.) The class is conducted in a seminar format. Assigned readings and computational techniques will form the basis of class discussion. The course will be graded as follows:

  • Assignments: 40%. There will be five homework assignments.
  • Final project 40%: Dual students will be complete a medium-ish final project (others will have this 40% from assignments)
  • Class participation: 20%

Assignments will involve experimentation with fundamental computational techniques. Some assignments will require intermediate level coding in Python, but the emphasis will be on thoughtful and critical analysis. As this is a journalism course, you will be expected to write clearly. The final project can be either a piece of software (especially a plugin or extension to an existing tool), a data-driven story, or a research paper on a relevant technique.

Dual degree students will also have a final project. This will be either a research paper, a computationally-driven story, or a software project. The class is conducted on pass/fail basis for journalism students, in line with the journalism school’s grading system. Students from other departments will receive a letter grade.

Week 1: High dimensional data – 9/12
CS techniques can help journalism in two main ways: using computation to do journalism, and doing journalism about computation. Either way, we’ll be working a lot with the abstraction of high dimensional vectors. We’ll start with an overview of interpreting high-dimensional data, then jump right into clustering and the document vector space model, which we’ll need to study natural language processing and recommendation engines.

Slides.

References

Viewed in class

Week 2: Text analysis – 9/19
We’ll start by picking up the story of text analysis in journalism, including the development of thew Overview document mining system. Then probabilistic topic modeling (ala LDA), matrix factorization, more general plate-notation graphical models, and word embedding approaches based on deep learning. Then on to fundamental recommendation approaches such as collaborative filtering. Bringing it to practice we will look at Columbia Newsblaster (a precursor to Google News) and the New York Times recommendation engine.

Slides.

Required

References

Discussed in class

Assignment:  LDA analysis of State of the Union speeches.

Week 3: Filter Design
We’ve studied filtering algorithms, but how are they used in practice — and how should they be? We will study the details of several algorithmic filtering approaches used by social networks, and effects such as polarization and filter bubbles.

Slides.

Readings

References

Viewed in class

Assignment 2:  Design a filtering algorithm for an information source of your choosing

Week 4: Quantification and Statistical Inference 
We’ll begin with the most neglected topic in statistics: measurement. We’ll take a detailed look at the question of what to count, and how to “interview the data” to check for data quality. Then we’ll move on to risk ratios, one of the simplest statistical models and a key idea in accountability. We’ll continue with a look at the uses of multi-variable regression in journalism, and study graphical causal models to help untangle the whole correlation/causation thing.

Slides.

Required:

Recommended

Viewed in class

Week 5: Algorithmic Accountability and Discrimination 
Algorithmic accountability is the study of the algorithms that regulate society, from high frequency trading to predictive policing. We’re at their mercy, unless we learn how to investigate them. We’ll review previous work in this area, then start our study of algorithmic discrimination. Analyzing discrimination data is more subtle and complex than it might seem.

Slides.

Required

References

Viewed in class

Week 6: Quantitative Fairness

Most algorithmic accountability and AI fairness work so far has been concerned with “bias,” but what is that? The answer is more complex than it might seem. In this class we’ll discuss the many definitions of fairness and show that they mostly boil down to three different formulations. We’ll also discuss everything around the algorithm, including how the results are used and what the training data means.

Slides.

Required:

References

Week 7: Randomness and Significance
The notion of randomness is crucial to the idea of statistical significance. We’ll talk about determining causality, p-hacking and reproducibility, and the more qualitative, closer-to-real-world method of triangulation.

Slides.

Required

Recommended

Viewed in class

Week 8: Visualization, Network Analysis 
Visualization helps people interpret information. We’ll look at design principles from user experience considerations, graphic design, and the study of the human visual system. Network analysis (aka social network analysis, link analysis) is a promising and popular technique for uncovering relationships between diverse individuals and organizations. It is widely used in intelligence and law enforcement, and inreasingly in journalism.

 Slides.

Readings

References

Examples:

Assignment: Compare different centrality metrics in Gephi.

Week 9: Knowledge representation
How can journalism benefit from encoding knowledge in some formal system? Is journalism in the media business or the data business? And could we use knowledge bases and inferential engines to do journalism better? This gets us deep into the issue of how knowledge is represented in a computer. We’ll look at traditional databases vs. linked data and graph databases, entity and relation detection from unstructured text, and traditional both probabilistic and propositional formalisms. Plus: NLP in investigative journalism, automated fact checking, and more.

Slides.

Readings

References

Viewed in class

Assignment: Text enrichment experiments using OpenCalais entity extraction.

Week 10: Truth and Trust 

Credibility indicators and schema. Information operations. Fake news detection and automated fact checking. Tracking information flows.

Slides.

Readings

References

11: Privacy, Security, and Censorship
Who is watching our online activities? Who gets to access to all of this mass intelligence, and what does the ability to survey everything all the time mean both practically and ethically for journalism? In this lecture we cover both the basics of digital security, and methods to deal with specific journalistic situations — anonymous sources, handling leaks, border crossings, and so on.

Slides.

Readings

  • Digital Security for Journalists, Part 1 and Part 2, Stray

References

Viewed in Class

Week 12: Final Project Presentations