Tag: scatter plot

  • Part 6: Who’s Talking About The Future of Newspapers?

    Continued on from Part 5, exploring what they are saying using the Phrase Net visualization from Many Eyes.

    Each image is a link to the applet where you can explore the text and interact with it. Change the linking word on the left – I’ve used space, but “and” or “is” in particular could be enlightening.

    I like this visualization because it shows what goes together. The fact that “globe” and “mail” are linked by “and” is perhaps not unexpected, but what does “Google” link to? News? Facebook? Buzz? What do these link to in turn – privacy? Social networking?

    Let me know what you find!

    Alex Howard
    C50006f8-b5bd-11df-a110-000255111976 Blog_this_caption
    Alfred Hermida
    3c32f410-b5be-11df-b20a-000255111976 Blog_this_caption
    Andrew Keen
    6f68e268-b5be-11df-b20a-000255111976 Blog_this_caption
    Cody Brown
    8f5eb00c-b5be-11df-a76f-000255111976 Blog_this_caption
    Dan Gillmor
    B4982ee8-b5be-11df-a76f-000255111976 Blog_this_caption
    Dave Winer
    D760ac34-b5be-11df-947b-000255111976 Blog_this_caption
    David Cohn
    09b1aecc-b5bf-11df-947b-000255111976 Blog_this_caption
    David Eaves
    2f0b4bd8-b5bf-11df-ba1e-000255111976 Blog_this_caption
    Dr. Mark Drapeau
    9884ba04-b5bf-11df-947b-000255111976 Blog_this_caption
    Howard Weaver
    Bd7aafda-b5bf-11df-ba1e-000255111976 Blog_this_caption
    Jay Rosen
    E8315c88-b5bf-11df-8a6b-000255111976 Blog_this_caption
    JD Lasica
    F69e1a04-b5bf-11df-ba1e-000255111976 Blog_this_caption
    Jeff Jarvis
    23f3f186-b5c0-11df-afa4-000255111976 Blog_this_caption
    Jennifer Preston
    2ec13308-b5c0-11df-8a6b-000255111976 Blog_this_caption
    Kirk LaPointe
    6d3fc310-b5c0-11df-9d86-000255111976 Blog_this_caption
    Mark Glaser
    9d5b9790-b5c0-11df-8a6b-000255111976 Blog_this_caption
    Matthew Ingram
    Be0d0186-b5c0-11df-a76f-000255111976 Blog_this_caption
    Steve Buttry
    E8b17034-b5c0-11df-a110-000255111976 Blog_this_caption
    Steve Outing
    F7544f3a-b5c0-11df-9d86-000255111976 Blog_this_caption
    Steve Yelvington
    3023889e-b5c1-11df-8b50-000255111976 Blog_this_caption
  • Part 5: Who’s Talking About The Future Of Newspapers?

    Continued on from Part 4, exploring what they are saying using Word Trees on Many Eyes.

    Each image is a link to the applet where you can explore the text and interact with it. Change the word in the top left corner to change the root of the tree.

    Alex Howard
    E873b62c-b016-11df-a0a3-000255111976 Blog_this_caption
    Alfred Hermida
    1eaf0ff0-b019-11df-a869-000255111976 Blog_this_caption
    Andrew Keen
    70778a10-b019-11df-8612-000255111976 Blog_this_caption
    Cody Brown
    B5b2c7de-b019-11df-8ecc-000255111976 Blog_this_caption
    Dan Gillmor
    F1f5138c-b019-11df-8ecc-000255111976 Blog_this_caption
    Dave Winer
    2a5d942e-b01a-11df-8612-000255111976 Blog_this_caption
    David Cohn
    7c3982f8-b01a-11df-a869-000255111976 Blog_this_caption
    David Eaves
    984d5c3a-b01a-11df-8985-000255111976 Blog_this_caption
    Dr. Mark Drapeau
    F463ce64-b01a-11df-a869-000255111976 Blog_this_caption
    Howard Weaver
    0bf46818-b01b-11df-8985-000255111976 Blog_this_caption
    Jay Rosen
    58524536-b01b-11df-8612-000255111976 Blog_this_caption
    JD Lasica
    90e1db14-b01b-11df-8ecc-000255111976 Blog_this_caption
    Jeff Jarvis
    Ce0596fc-b01b-11df-9ca9-000255111976 Blog_this_caption
    Jennifer Preston
    F44e040c-b01b-11df-b431-000255111976 Blog_this_caption
    Kirk LaPointe
    4d423178-b01c-11df-8985-000255111976 Blog_this_caption
    Mark Glaser
    6900ff20-b01c-11df-b431-000255111976 Blog_this_caption
    Matthew Ingram
    B7fbcd6c-b01c-11df-b431-000255111976 Blog_this_caption
    Steve Buttry
    Cbb1519c-b01c-11df-9ca9-000255111976 Blog_this_caption
    Steve Outing
    28476982-b01d-11df-9ca9-000255111976 Blog_this_caption
    Steve Yelvington
    61ca3aae-b01d-11df-9ca9-000255111976 Blog_this_caption
  • Part 4: Who’s Talking About The Future Of Newspapers?

    Part 4: Who’s Talking About The Future Of Newspapers?

    In which we answer the question – what are they saying?

    I’ve split the tweets up into two types – at replies, and not at replies, and a third which contains all tweets. I’ve created wordles of each one, for each of the 20 people we were following.

    If you haven’t – check out wordle.net. It’s awesome.

    There’s debate as to whether wordles are good ways to analyze text – definitely there are better ways (possibly to be explored in a future post) however I think they’re cool and here they have some utility. Note, though, that sizes of word are relative to the number of words in the data set for that individual, which are of varying size (see Part 1, Part 2, Part 3).

    I don’t want to tread on Caitlin’s analysis (I’m just the data junkie), but some things you can see, aside from topics of discussion:

    • People who make a point of thanking others (most likely for retweets or similar)
    • People who retweet things that others have said about them
    • Where RT is conspicuous by it’s absence
    • Specific websites that get tweeted a lot

    My personal favorite is Dave Winer’s all tweets! Let me know what you think.

    Programming-wise, the code is trivial because wordle accepts free text. But, before I realized that the guy who wrote wordle was much smarter than me, I tried to be clever an optimize it by using a LinkedHashSet. I chose this data structure on the basis that – I wanted O(1) random access (the hash) because I would find the same words repeated, only one instance of each word (the set) and a nice quick iteration (the linked) so I could output a key, value table at the end. And then I discovered that there was no get() or elementAt() method – and stopped trying to be a smart-alec!

  • Part 3: Who’s Talking About The Future Of Newspapers?

    Continued on from Part 2, I’m representing similar data in a different (less exciting) way.

    Before, we looked at how the activity on the twitter streams was spread out over the day and by different types of interaction. Here, I’m using charts to show the breakdown for the day, by user. I’ve also created charts for each type – these are too busy to show much more than users who are way above average in a particular tweet type.

    Like last time, something is either:

    • Directed
    • Not directed, but containing a mention
    • Contains a link, not an @ mention
    • None of the above.

    I’m using the existing code I’ve built up – Apache POI to import and some custom data-structures.

  • Part 2: Who’s Talking About the Future of Newspapers?

    After breaking down the overall types of tweets from people, next step was to create scatter plots of their activity.

    Unfortunately, Excel will only plot 250 data points – how unreasonable! Luckily I love breaking Excel and coding something that will do what I want it to do and look prettier, so voila.

    Color scheme:

    1. Is directed at someone by starting with an @
    2. Contains a mention (@) of someone else
    3. Contains a link

    Otherwise, the point for that tweet is light gray. Note this is done in the order above, so if 1 is true, then it doesn’t matter if both 2 and 3 are true or false – the tweet will be pink. If 2 is true, the tweet may or may not contain a link – it will still be purple.

    I used the Processing core.jar library within Eclipse, along with the data-structures I created originally and the Apache POI code for extracting the data from Excel.

    I’m enclosing the code below, with some comments:

    • This code will not compile even with the Processing core.jar library (requires data-structure code that I have not yet released).
    • There is a horrible hack for calculating the time passed since original date – if you’re doing anything more with time consider Joda Time instead.
    • The code is written to visualize this data and only this data. Whilst I may create a proper ScatterPlot class for Processing at some point, I’ll probably wait until Java 7 because without lambda functions it will require either a standard data format, or some kind of interface hack to create an adapter pattern. I don’t like either of these approaches.
    • Aside from this, if you have some other use for it feel free to ping me with questions!
    package com.catehuston.caitlin.viz;
    
    import java.io.IOException;
    import java.util.Calendar;
    import java.util.Date;
    
    import com.catehuston.caitlin.datastructures.Tweet;
    import com.catehuston.caitlin.datastructures.User;
    import com.catehuston.caitlin.parse.UserList;
    
    import processing.core.PApplet;
    
    @SuppressWarnings("serial")
    public class Scatterplot extends PApplet {
    
    	private static final int w = 1260; // 1160 for graph
    	private static final int h = 600; // 480 for graph
    
    	// spacing at either side
    	private static final int xmargin = 70;
    	private static final int ymargin = 60;
    
    	// axis length
    	private static final int xlen = w-(xmargin*2);
    	private static final int ylen = h-(ymargin*2);
    
    	// increments for day, hour, minute
    	private static final int di = xlen/58;
    	private static final int hi = ylen/24;
    	private static final double mi = hi/60d;
    
    	// user we're graphing
    	private int index = 5;
    	private User user;
    
    	// calendar for date comparison
    	Calendar startDate;
    
    	public void setup() {
    		UserList ul;
    		try {
    			// generate user list from spreadsheet
    			ul = new UserList("../data/data_june16_top20.xls");
    		} catch (IOException e) {
    			// TODO Auto-generated catch block
    			e.printStackTrace();
    			return;
    		}
    
    		// get data just for the user we're interested in
    		user = ul.get(index);
    
    		// set applet size
    		size(w, h);
    
    		// draw() method will be called only once
    		noLoop();
    
    		// set up calendar with base date
    		startDate = Calendar.getInstance();
    		startDate.set(Calendar.YEAR, 2010);
    		startDate.set(Calendar.MONTH, Calendar.FEBRUARY);
    		startDate.set(Calendar.DAY_OF_MONTH, 1);
    		startDate.set(Calendar.HOUR_OF_DAY, 0);
    		startDate.set(Calendar.MINUTE, 0);
    	}
    
    	public void draw() {
    		// set background color - dark grey
    		background(64);
    
    		// set foreground color for text and axes - light grey
    		stroke(238);
    		fill(238);
    
    		// draw user name string top left
    		text(user.getUser(), 5, 15);
    
    		// draw x-axis
    		int ypos = ylen+ymargin;
    		line(xmargin, ypos, xmargin + xlen, ypos);
    		// add major markers
    
    		// initial
    		line(xmargin, ypos, xmargin, ypos+5);
    		text("Feb 1, 2010", xmargin, ypos+20);
    
    		// mid-feb
    		int inc = 13*di;
    		line(xmargin + inc, ypos, xmargin + inc, ypos+5);
    		text("Feb 14, 2010", xmargin + inc, ypos+20);
    
    		// start of march
    		inc = 28*di;
    		line(xmargin + inc, ypos, xmargin + inc, ypos+5);
    		text("Mar 1, 2010", xmargin + inc, ypos+20);
    
    		// mid march
    		inc = inc + 14*di;
    		line(xmargin + inc, ypos, xmargin + inc, ypos+5);
    		text("Mar 15, 2010", xmargin + inc, ypos+20);
    
    		// end of march
    		inc = 58*di;
    		line(xmargin + inc, ypos, xmargin + inc, ypos+5);
    		text("Mar 31, 2010", xmargin + inc - 60, ypos+20);
    
    		// draw y-axis
    		line(xmargin, ymargin, xmargin, ypos);
    		// add markers
    		for (int i = 0; i < 2401; i+=200) {
    			inc = i/100*hi;
    			ypos = ymargin + ylen - inc;
    			line(xmargin-5, ypos, xmargin, ypos);
    			String hrs = i + "h";
    			if (i == 0) {
    				hrs = "0000h";
    			}
    			else if (i < 1000) {
    				hrs = "0" + hrs;
    			}
    			text(hrs, xmargin-50, ypos+10);
    		}
    
    		// go through and plot points, color according to type
    		for (Tweet t : user.getTweets()) {
    			// set color according to tweet type
    			// @ message
    			if (t.isDirected()) {
    				// pink
    				stroke(236, 0, 128);
    				fill(236, 0, 128);
    			}
    			// someone else is mentioned
    			else if (t.isMention()) {
    				// purple
    				stroke(140, 9, 214);
    				fill(140, 9, 214);
    			}
    			// contains link
    			else if (t.hasLink()){
    				// yellow
    				stroke(255, 126, 0);
    				fill(255, 126, 0);
    			}
    			// otherwise
    			else {
    				stroke(238);
    				fill(238);
    			}
    
    			Date d = t.getDate();
    			int x = getXPos(d);
    			int y = getYPos(d);
    			ellipse(x, y, 3, 3);
    		}
    	}
    
    	private int getXPos(Date date) {
    		// make calendar with specified date
    		Calendar newDate = Calendar.getInstance();
    		newDate.setTime(date);
    
    		// count how many days we go back to find start date
    		int count = -1;
    		while(startDate.before(newDate)) {
    			count++;
    			newDate.add(Calendar.DATE, -1);
    		}
    
    		return xmargin + count * di;
    	}
    
    	private int getYPos(Date date) {
    		// put date in calendar so we can manipulate it
    		Calendar time = Calendar.getInstance();
    		time.setTime(date);
    
    		// work out hour increment
    		int hrs = time.get(Calendar.HOUR_OF_DAY) * hi;
    		// wor out minute increment
    		double mins = time.get(Calendar.MINUTE) * mi;
    
    		// return y value
    		return (int) (ylen + ymargin - hrs - mins);
    	}
    }