In this assignment you will implement the TF-IDF formula and use it to study the topics in State of the Union speeches given every year by the U.S. president.

1. 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.

2. Tokenize the text each speech, to turn it into a list of words. As we discussed in class, we’re going to tokenize using a simple scheme:

• convert all characters to lowercase
• remove all punctuation characters
• split the string on spaces

3. Compute a TF (term frequency) vector for each document. This is simply how many times each word appears in that document. You should end up with a Python dictionary from terms (strings) to term counts (numbers) for each document.

4. Count how many documents each word appears in. This can be done after computing how the TF vector by each document, by incrementing the document count of each word that appears in the TF vector. After reading all documents you should now have a dictionary from each term to the number of documents that term appears in.

5. Turn the final document counts into IDF (inverse document frequency) weights by applying the formula IDF(term) = log(total number of documents / number of documents that term appears in.)

6. Now multiply the TF vectors for each document by the IDF weights for each term, to produce TF-IDF vectors for each document.

7. Then normalize each vector, so the sum of squared weights is 1.  You can do this by dividing each component of the document vector by the length of the vector. Use the usual formula for the length of a vector.

8. Congratulations! You have a set of TF-IDF vectors for this corpus. Now it’s time to see what they say. Take the speech you were assigned in class, and print out the highest weighted 20 terms, along with their weights. What do you think this particular speech is about? Write your answer in at most 200 words.

9. Your task now is to see if you can understand how the topics changed since 1900. For each decade since 1900, do the following:

• sum all of the TF-IDF vectors for all speeches in that decade
• print out the top 20 terms in the summed vector, and their weights

Now take a look at the terms for each decade. What patterns do you see? Can you connect the terms to major historical events? (wars, the great depression, assassinations, the civil rights movement, Watergate…) Write up what you see in narrative form, no more than 500 words, referring to the terms for each decade.

10. Hand in by email, before class next week:

• your code
• the printout and analysis from step 7
• the printout and narrative from step 8.

You will be marked on two things: 1) the correctness of your code (but not code style, I don’t care about that in this class) and 2) the quality of your writeup. I am looking for you to connect the patterns in the data with the historical context. The resulting writeup must be interesting and informative to someone who does not know or care about the data.

For this assignment, each of you will pick one of the four reporting scenarios below and design a security plan. More specifically, you will flesh out the scenario, create a threat model, come up with a plausible security plan, and analyze the weaknesses of your plan.

Start by creating a threat model, which must consider:

• What must be kept private? Specify all of the information that must be secret, including notes, documents, files, locations, and identities — and possibly even the fact that someone is working on a story.
• Who is the adversary and what do they want to know? It may be a single person, or an entire organization or state, or multiple entities. They may be very interested in certain types of information, e.g. identities, and uninterested in others. List each adversary and their interests.
• What can they do to find out? List every way they could try to find out what you want secret, including technical, legal, and social methods.
• What is the risk? Explain what happens if an adversary succeeds in breaking your security. What are the consequences, and to whom? Which of these is it absolutely necessary to avoid?

Once you have specified your your threat model, you are ready to design your security plan. The threat model describes the risk, and the goal of the security plan is to reduce that risk as much as possible.

Your plan must specify appropriate software tools, plus how these tools must be used. Pay particular attention to necessary habits: specify who must do what, and in what way, to keep the system secure. Explain how you will educate your sources and collaborators in the proper use of your chosen tools, and how hard you think it will be to make sure everyone does exactly the right thing.

Also document the weaknesses of your plan. What can still go wrong? What are the critical assumptions that will cause failure if it turns out you have guessed wrong? What is going to be difficult or expensive about this plan?

The scenarios you can choose from are:

1. You are a photojournalist in Syria with digital images you wants to get out of the country. Limited internet access is available at a cafe. Some of the images may identify people working with the rebels who could be targeted by the government if their identity is revealed. In addition you would like to remain anonymous until the photographs are published, so that you can continue to work inside the country for a little longer, and leave without difficulty.

2. You are working on an investigative story about the CIA conducting operations in the U.S., in possible violation the law. You have sources inside the CIA who would like to remain anonymous. You will occasionally meet with these sources in but mostly communicate electronically. You would like to keep the story secret until it is published, to avoid pre-emptive legal challenges to publication.

3. You are reporting on insider trading at a large bank, and talking secretly to two whistleblowers. If these sources are identified before the story comes out, at the very least you will lose your sources, but there might also be more serious repercussions — they could lose their jobs, or the bank could attempt to sue. This story involves a large volume of proprietary data and documents which must be analyzed.

4. You are working in Europe, assisting a Chinese human rights activist. The activist is working inside China with other activists, but so far the Chinese government does not know they are an activist and they would like to keep it this way. You have met the activist once before, in person, and have a phone number for them, but need to set up a secure communications channel.

These scenario descriptions are incomplete. Please feel free to expand them, making any reasonable assumptions about the environment or the story — though you must document your assumptions, and you can’t assume that you have unrealistic resources or that your adversary is incompetent.

For this assignment you will analyze global data on the number of homicides versus the number of guns in each country. I’m giving you the data — your job is to tell me what it means. You will interpret a few different plots, and then implement the visual randomization procedure from the paper we discussed in class to examine a tricky case more closely.

The data is from The Guardian Data Blog. I simplified the header names, dropped a few unnecessary columns, and added an OECD column.

1. I’ve written most of the code you will need for this assignment, available from this github repo. (You can git clone if you like, otherwise just click here to download all files as a zip archive).

2. We are going to use the R language for this assignment. This is mostly because it has really nice built in charts (doing this in Python is a real pain), but also because you are likely to encounter R out in the real world of data journalism. Download and install it. To start R, enter R on the command line. To run a program, enter source(‘filename.R’) at the R command prompt A full language manual is here. You will only need to use a few basic concepts, such as random number generation and for loops.

3. Plot the data for all countries’ homicide rate (per 100,000) versus number of privately-owned firearms (per 100) by running source(‘plot-all-countries.R’) at the R prompt. What do you see? Please report on the general patterns here, the outliers, and what this all might mean.

4. Now take a look at only the OECD countries, by uncommenting the indicated line in the source. Re-run the file. What does the chart show now?

5. Now plot only the non-OECD countries, by uncommenting the indicated line in the source (be sure to re-comment the line that selects only OECD countries). What does the chart show now?

6. It looks like there might be a pattern among the OECD countries, but the United States is such an outlier that it’s hard to tell. Is this pattern still significant without the US? To find out, you’re going to apply a randomization test. (We’ll also remove Mexico since it’s not a developed country and thus not really comparable to the other OECD countries.)

Start with the file randomization-test.R. You need to write the code that performs the actual randomization, filling the eight of the columns of charts with random permutations of the original y values (homicide rates), but putting the original data in the realchart column. To prevent sneak peaks, the code is currently set up to use testing data. When your permutations are working right, you should see something like this when you run the file:

After pressing Enter, the program will tell you which chart has the real (un-permuted) data. Here, with fake data, it’s obvious. It won’t always be.

7. Now that your program works, try it on the real data by commenting out the two lines that generate the fake data. Re-run, and look at the plots carefully. Which one do you think is the real data? Write down the number of the chart. Then hit enter, and see if you got it right.

8. This isn’t quite fair, because you were already looking at the data in step 4. So get someone else to look at it fresh. Explain to them that you are charting firearms versus homicides and that one of the charts is real but the rest are fakes, and ask them to spot the real chart.

9. Did you guess right? Did your fresh observer guess right? Did you and your observer guess differently? If so, why do you think that is? Was it difficult for you to choose? Based on all of this, do you think there is a correlation between gun ownership and homicide rate for the OECD countries? If so, how strong is it (effect size) and how strong is the evidence (statistical significance)?

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. Actually, 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:

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.

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.

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.

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 selecte “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. Turn in: your answers to the questions in steps 3, 6, 7 and 8, 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.

For this assignment you will evaluate the performance of OpenCalais, a commercial entity extraction service. You’ll do this by building a text enrichment program, which takes plain text and outputs HTML with links to the detected entities. Then you will take five random articles from your data set, enrich them, and manually count how many entities OpenCalais missed or got wrong.

1. Get an OpenCalais API key, from this page.

2. Install the python-calais module. This will allow you to call OpenCalais from Python easily. First, download the latest version of python-calais. To install it, you just need calais.py in your working directory. You will probably also need to install the simplejson Python module. Download it, then run “python setup.py install.” You may need to execute this as super-user.

3. Call OpenCalais from Python. Make sure you can successfully submit text and get the results back, following these steps. The output you want to look at is in the entities array, which would be accessed as “results.entities” using the variable names in the sample code. In particular you want the list of occurrences for each entity, in the “instances” field.

>>> result.entities[0]['instances']
[{u'suffix': u' is the new President of the United States', u'prefix': u'of the United States of America until 2009.  ', u'detection': u'[of the United States of America until 2009.  ]Barack Obama[ is the new President of the United States]', u'length': 12, u'offset': 75, u'exact': u'Barack Obama'}]
>>> result.entities[0]['instances'][0]['offset']
75
>>>

Each instance has “offset” and “length” fields that indicate where in the input text the entity was referenced. You can use these to determine where to place links in the output HTML.

4. Read a text file, create hyperlinks, and write it out. Your Python program should read text from stdin and write HTML with links on all detected entities to stdout. There are two cases to handle, depending on how much information OpenCalais gives back.

In many cases, like the example in step 3, OpenCalais will not be able to give you any information other than the string corresponding to the entity, result.entities[x][‘name’]. In this case you should construct a Wikipedia link by simply appending to the name to a Wikipedia URL, converting spaces to underscores, e.g.

http://en.wikipedia.org/wiki/Barack_Obama

In other cases, especially companies and places, OpenCalias will supply a link to an RDF document that contains more information about the entity. For example.

>>> result.entities[0]{u'_typeReference': u'http://s.opencalais.com/1/type/em/e/Company', u'_type': u'Company', u'name': u'Starbucks', '__reference': u'http://d.opencalais.com/comphash-1/6b2d9108-7924-3b86-bdba-7410d77d7a79', u'instances': [{u'suffix': u' in Paris.', u'prefix': u'of the United States now and likes to drink at ', u'detection': u'[of the United States now and likes to drink at ]Starbucks[ in Paris.]', u'length': 9, u'offset': 156, u'exact': u'Starbucks'}], u'relevance': 0.314, u'nationality': u'N/A', u'resolutions': [{u'name': u'Starbucks Corporation', u'symbol': u'SBUX.OQ', u'score': 1, u'shortname': u'Starbucks', u'ticker': u'SBUX', u'id': u'http://d.opencalais.com/er/company/ralg-tr1r/f8512d2d-f016-3ad0-8084-a405e59139b3'}]}
>>> result.entities[0]['resolutions'][0]['id']
u'http://d.opencalais.com/er/company/ralg-tr1r/f8512d2d-f016-3ad0-8084-a405e59139b3'
>>>

In this case the resolutions array will contain a hyperlink for each resolved entity, and this is where your link should go. The linked page will contain a series of triples (assertions) about the entity, which you can obtain in machine-readable from by changing the .html at the end of the link to .json. The sameAs: links are particularly important because they tell you that this entity is equivalent to others in dbPedia and elsewhere.

Here is more on OpenCalias’ entity disambiguation and use of linked data.

The final result should look something like below. Note that some links go to OpenCalais entity pages with RDF links on them (“London”), some go to Wikipedia (“politician”) and some are broken links when Wikipedia doesn’t have the topic (“Aarthi Ramachandran”) And of course Mr Gandhi is an entity that was not detected, three times.

The latest effort to “decode” Mr Gandhi comes in the form of a limited yet rather well written biography by a political journalist, Aarthi Ramachandran. Her task is a thankless one. Mr Gandhi is an applicant for a big job: ultimately, to lead India. But whereas any other job applicant will at least offer minimal information about his qualifications, work experience, reasons for wanting a post, Mr Gandhi is so secretive and defensive that he won’t respond to the most basic queries about his studies abroad, his time working for a management consultancy in London, or what he hopes to do as a politician.

Don’t worry about producing a fully valid HTML document with headers and a <body> tag, just wrap each entity with <a href=”…”> and </a>. Your browser will load it fine.

5. Pick five random news stories and enrich them. First pick a news site with many stories on the home page. 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.)

6. Read the enriched documents and count to see how well OpenCalais did. You need to read each output document very carefully and count three things:

• Entity references. Count each time there is a name of a person, place, or organization appears, or other references to these things (e.g. “the president.”)
• Detected references. How many of these references did OpenCalais find?
• Correct references. How many of the links go to the right page? Did our hyperlinking strategy (OpenCalais RDF pages where possible, Wikipedia when not) fail to correctly disambiguate any of the references, or, even worse, disambiguate any to the wrong object? Also, a broken link counts as an incorrect reference.

7. Turn in your work. Please turn in:

• Your code
• The enriched output from your documents
• A brief report describing your results.

The report should include a table of the three numbers — references, detected, correct — for each document, plus the totals of these three numbers across all documents. Also report on any patterns in the failures that your see. Where is OpenCalais most accurate? Where is it least accurate? Are there predictable patterns to the errors?

For this assignment you will design a hybrid 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:

• Facebook status updates, like the Facebook news feed
• Tweets, like Trending Topics or the many Tweet discovery tools
• The whole web, like Prismatic
• something else, but ask me first

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 Prismatic), and what is desirable socially. Explain as concretely as possible how each of these (probably conflicting) goals might be achieved through in software. Since this is a hybrid filter, you can also design social software that asks the user for certain types of information (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 a good programmer 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. However, 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 9