Category Archives: Social

Ranking High Schools Based On Outcomes

High school is arguably the most important phase of your education. Some families will move just to be in the district of the best ranked high school in the area. However, the factors that these rankings are based on, such as test scores, tuition amount, average class size, teacher to student ratio, location, etc. do not measure key outcomes such as what colleges or jobs the students get into.

Unfortunately, measuring outcomes is tough – there’s no data source that I know of that describes how all past high school students ended up. However, I thought it would be a fun experiment to approximate using LinkedIn data. I took eight top high schools in the Bay Area (see the table below) and ran a whole bunch of advanced LinkedIn search queries to find graduates from these high schools while also counting up their key outcomes like what colleges they graduated from, what companies they went on to work for, what industries are they in, what job titles have they earned, etc.

The results are quite interesting. Here are a few statistics:

College Statistics

  • The top 5 high schools that have the largest share of users going to top private schools (Ivy League’s + Stanford + Caltech + MIT) are (1) Harker (2) Gunn (3) Saratoga (4) Lynbrook (5) Bellarmine.
  • The top 5 high schools that have the largest share of users going to the top 3 UC’s (Berkeley, LA, San Diego) are (1) Mission (2) Gunn (3) Saratoga (4) Lynbrook (5) Leland.
  • Although Harker has the highest share of users going to top privates (30%), their share of users going to the top UC’s is below average. It’s worth nothing that Harker’s tuition is the highest at $36K a year.
  • Bellarmine, an all men’s high school with tuition of $15K a year, is below average in its share of users going on to top private universities as well as to the UC system.
  • Gunn has the highest share of users (11%) going on to Stanford. That’s more than 2x the second place high school (Harker).
  • Mission has the highest share of users (31%) going to the top 3 UC’s and to UC Berkeley alone (14%).

Career Statistics

  • In rank order (1) Saratoga (2) Bellarmine (3) Leland have the biggest share of users which hold job titles that allude to leadership positions (CEO, VP, Manager, etc.).
  • The highest share of lawyers come from (1) Bellarmine (2) Lynbrook (3) Leland. Gunn has 0 lawyers and Harker is second lowest at 6%.
  • Saratoga has the best overall balance of users in each industry (median share of users).
  • Hardware is fading – 5 schools (Leland, Gunn,  Harker, Mission, Lynbrook) have zero users in this industry.
  • Harker has the highest share of its users in the Internet, Financial, and Medical industries.
  • Harker has the lowest percentage of Engineers and below average share of users in the Software industry.
  • Gunn has the highest share of users in the Software and Media industries.
  • Harker high school is relatively new (formed in 1998), so its graduates are still early in the workforce. Leadership takes time to earn, so the leadership statistic is unfairly biased against Harker.

You can see all the stats I collected in the table below. Keep in mind that percentages correspond to the share of users from the high school that match that column’s criteria. Yellow highlights correspond to the best score; blue shaded boxes correspond to scores that are above average. There are quite a few caveats which I’ll note in more detail later, so take these results with a grain of salt. However, as someone who grew up in the Bay Area his whole life, I will say that many of these results make sense to me.

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Filed under Blog Stuff, Data Mining, Education, Job Stuff, LinkedIn, Research, Science, Social, Statistics

An Evaluation of Google’s Realtime Search

How timely are the results returned from Google’s Realtime (RT) Search Engine? How often do Twitter results appear in these results? Over the weekend I developed a few basic experiments to find out and published the results below.

Key Findings

  • For location-based queries, there’s nearly a flip of a coin chance (43%) that a Twitter result will be the #1 ranked result.
  • For general knowledge queries, there’s a 23% chance that a Twitter result will be #1.
  • The newest Twitter results are usually 4 seconds old. The newest Web results are 10x older (41 seconds).
  • A top ranking Twitter result for a location-based query is usually 2 minutes old (compared with Web which is 22 minutes old – again nearly 10x older).
  • When Twitter results appear at least one of them is in the top ranked position
Experiment #1 – General Knowledge

I crawled 1,370 article titles from Wikipedia and ran each title as a query into Google RT search.

Market Shares

81% of all queries returned search results that included web page results
23% of all queries returned search results that included Twitter results
7% of all queries returned 0 search results

70% of all queries had a web page result in the #1 ranked position
When Twitter results appeared there was always at least one result in the #1 ranked position (so 23% of queries)

Time Lag

When a web page was the #1 ranked result, that result on average was 6736 seconds (or 1 hr and 52 minutes) old.
When a Tweet was the #1 ranked result, that result on average was 261 seconds (or 4 minutes and 21 seconds) old.

The average age of the top 10% newest web page results (across all queries) is 41 seconds
The average age of the top 10% newest Twitter results (across all queries) is 2 seconds

Tail

Query length was between 1 – 12 words (where 1-2 word long queries are most popular)
Worth noting that no Twitter results appear for queries with greater than 5 words

Experiment #2 – Location

I crawled 265 major populated U.S. cities from the U.S. Census Bureau and ran each city name as a query into Google RT search.

Market Shares

73% of all queries returned search results that included web page results
43% of all queries returned search results that included Twitter results
5% of all queries returned 0 search results

52% of all queries had a web page result in the #1 ranked position
When Twitter results appeared there was always at least one result in the #1 ranked position (so 43% of queries)

Time Lag

When a web page was the #1 ranked result, that result on average was 1341 seconds (or 22 minutes and 21 seconds) old.
When a Tweet was the #1 ranked result, that result on average was 138 seconds (or 2 minutes and 18 seconds) old.

The average age of the top 10% newest web page results (across all queries) is 41 seconds
The average age of the top 10% newest Twitter results (across all queries) is 4 seconds

Tail

Query length was between 1 – 3 words
Worth noting that no Twitter results appear for 3 word long queries

Implementation Details

  • Generated Wiki queries by running “site:en.wikipedia.org” searches on Google and Blekko, and extracting the titles (en.wikipedia.org/{title_is_here}) from the result links. Side point: I tried Bing but the result links had mostly one word long titles (Bing seems to really bias query length in their ranking) and I wanted more diversity to test out tail queries.
  • Crawled cities (for the location-based queries) from http://www.census.gov/popest/cities/tables/SUB-EST2009-01.csv

Caveats

  • I ran these experiments at 2:45a PST on Monday. The location-based queries all relate to U.S., so probably not many people up at that time generating up-to-date information. The time lag stats could vary depending on when these experiments are ran. I did however re-run the experiments in the late morning and didn’t see much difference in the timings.
  • I ran all queries through Google’s normal web search engine with ‘Latest’ on (in the left bar under Search Tools). These results are not exactly the same as those generated from the standalone Google Realtime Search portal, which seems to bias Tweets more while the ‘Latest’ results seems to find middle ground between real-time Twitter results and web page results. I used ‘Latest’ because it seems like it would be the most popular gateway to Google’s Realtime search results.

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Filed under Blog Stuff, Computer Science, Data Mining, Google, Information Retrieval, Research, Search, Social, Statistics, Twitter, Wikipedia

Does Facebook leak what profiles you click on?

Check out Preview My Profile on Facebook:

Account (top right) > Privacy Settings >

Customize Settings > Preview My Profile

Now say you have a friend named Bob. Type ‘Bob’ in the box at the top of Preview My Profile to see how your profile will be seen by him. Take a look at the Mutual Friends section (bottom left in the screenshot above) of your profile (from Bob’s view – so still in Preview My Profile). Notice how these mutual friends seem to bias towards those who are closest to Bob (and perhaps to you as well). This by itself is pretty interesting. I can see who my friends are closer to relative to our other mutual friends. This pattern seems to hold up well in my trials over my friends who I know well (I saw that their closest friends were popping up more often than not in the mutual friends section).

This got me curious about how Facebook determines “closeness” between two people. In particular, does Facebook leverage your clicks on a friend’s profile in determining how close you are to that friend? To experiment, I frequently clicked on my friend’s (say her name is Alice) profile and newsfeed updates over two weeks. She’s someone I rarely communicate with. I then normally browsed profiles of mutual friends I share with Alice and noticed that in the mutual friends section of those profiles Alice frequently showed up (even when the total number of mutual friends was greater than 80 – keep in mind that the mutual friends section only shows 3 friends). Now, there’s definitely randomness at times and I believe multiple ranking features are probably being used here (like perhaps number of exchanged messages) but I have a feeling clicks might be in play here as well based on this result.

If Preview My Profile gives you the same view over mutual friends as what you see normally when you click on a friend’s profile, and if mutual friends uses private information like clicks / messages as features in the ranking, then it may be possible to infer who your friends are communicating with or clicking on more – or at the very least, find who they are closer to relative to your other mutual friends. If I view my profile from Bob’s eyes and frequently see Alice appear in the Mutual Friends section over multiple runs it may imply a strong relationship from Bob to Alice. Also, when the number of mutual friends is high relative to the number of total friends your friend has, then this result may be even more accurate.

This isn’t scientific by any means – I really don’t know how the ranking is done and may be completely wrong – so take it with a grain of salt. Just thought it was an interesting feature and pattern worth sharing …

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Filed under Blog Stuff, Facebook, Non-Technical-Read, Social

pplmatch – Find Like Minded People on LinkedIn

http://www.pplmatch.com

Just provide a link to a public LinkedIn profile and an email address and that’s it. The system will go find other folks on LinkedIn who best match that given profile and email back a summary of the results.

It leverages some very useful IR techniques along with a basic machine learned model to optimize the matching quality.

Some use cases:

  • If I provide a link to a star engineer, I can find a bunch of folks like that person to go try to recruit. One could also use LinkedIn / Google search to find people, but sometimes it can be difficult to formulate the right query and may be easier to just pivot off an ideal candidate.
  • I recently shared it with a colleague of mine who just graduated from college. He really wants to join a startup but doesn’t know of any (he just knows about the big companies like Microsoft, Google, Yahoo!, etc.). With this tool he found people who shared similar backgrounds and saw which small companies they work at.
  • Generally browsing the people graph based on credentials as opposed to relationships. It seems to be a fun way to find like minded people around the world and see where they ended up. I’ve recently been using it to find advisors and customers based on folks I admire.

Anyways, just a fun application I developed on the side. It’s not perfect by any means but I figured it’s worth sharing.

It’s pretty compute intensive, so if you want to try it send mail to [contact at pplmatch dot com] to get your email address added to the list. Also, do make sure that the profiles you supply expose lots of text publicly – the more text the better the results.

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Filed under AI, Blog Stuff, Computer Science, CS, Data Mining, Information Retrieval, Machine Learning, NLP, Research, Science, Search, Social, Uncategorized, Web2.0

anymeme: Breaking News, Tweets in your URLs

A very basic experiment that pads URLs with messages:

or more appropriately http://anymeme.appspot.com/anymeme.appspot.com

Notes

  • This is not related to any work I’ve been pursuing during my EIR gig.
  • It’s kind of like the opposite of bit.ly (there is a shortener available on the site though). It’s better tailored for shorter URLs where there’s enough address bar space to display a message at the end of the URL.
  • I tested this on the top 30 or so sites using a mix of Firefox and Chrome.
  • This could easily be the dumbest thing I’ve ever developed, but then again there are a lot of dumb things on the web. It took longer for me to write these posts describing anymeme than to develop the code for it. This is more of an experiment to see:
    • If users, publishers, and advertisers like it
    • To try to make URLs more interesting and valuable
  • It would be so cool:
    • To generate enough cash via sponsored messages to make meaningful contributions to great causes
    • To see an important breaking news headline or an interesting tweet as you load up hulu to check for new episodes – visible in the previously half empty address bar so there’s no need to frame or change the destination page to show the content.
  • It currently runs on Google App Engine

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Filed under Blog Stuff, Google, News, Social, Trends, Twitter, Uncategorized, Web2.0

Some Stats about Twitter’s Content

Near the end of July, I crawled a sample of ~10M tweets. On my way over from Open Hack Day NYC yesterday I finally got some time to do some preliminary analysis of this data. Several posts have analyzed Twitter’s traffic stats [TechCrunch] [Mashable] [zooie], so I thought I’d focus more on the content here.

Duplication

By compressing the data and comparing the before and after sizes, one can get a pretty decent understanding of the duplication factor. To do this, I extracted just the raw text messages, sorted them, and then ran gzip over the sorted set.

Compression ratio

>>> 284023259 / 739273532 bytes

0.38419238171778614

Typically, for text compression, gzip-like programs can achieve around 50% without the sort (and sorting typically helps), and here we get 38%. A standard text corpus consists of much larger document sizes, so it’s interesting to see a similar or larger duplication factor for tweets.

We can dive even deeper into this area by analyzing the term overlap statistics to measure near duplication, or messages that aren’t necessarily identical but are close enough.

To do this, I first cleaned the text (removed stopwords, stemmed terms, normalized case). Interesting, after cleaning the text, the average number of tokens for a message is just 6.28, or 2.5x the size of a standard web search query.

Then, I employed consistent term sampling to select N representatives for each cleaned message and coalesced the representatives together as a single key. By comparing the total number of unique keys to messages, one can infer the near duplication factor. Also, the higher the N, the higher the threshold is to match (so N >= 6, 6 being the average number of tokens per message, probably means that two messages that generate the same key are exact duplicates).

You’ll notice N >=6 converges around 84%, implying that after cleaning the text, 16% of the messages exactly match some other message. Additionally, when N = 2 (or requiring 2 / 6 tokens or 33% of the text on average) to match, 45% of the messages collide with other messages in the corpus. At N = 2, matching often means the messages discuss the same general topic, but aren’t close near duplicates.

N Term Samples Unique Keys Coverage
8 8548695 0.8356
6 8512672 0.8321
5 8476590 0.8286
4 8366391 0.8177
3 8098400 0.7916
2 5716566 0.5588
1 1013783 0.0991

 

 

 

 

 

 

 

URLs

URLs are present in ~18% of the tweets

Of those, ~65% of the URLs are unique

70K Unique Domains covering 2M URLS

Top Domains:

[‘bit.ly’, ‘tinyurl.com’, ‘twitpic.com’, ‘is.gd’, ‘myloc.me’, ‘ow.ly’, ‘ustre.am’, ‘cli.gs’, ‘tr.im’, ‘plurk.com’, ‘ff.im’, ‘tumblr.com’, ‘yfrog.com’, ‘140mafia.com’, ‘u.mavrev.com’, ‘twurl.nl’, ‘tweeterfollow.com’, ‘mypict.me’, ‘viagracan.com’, ‘vipfollowers.com’, ‘morefollowers.net’, ‘digg.com’, ‘tweeteradder.com’, ‘ping.fm’, ‘tiny.cc’, ‘followersnow.com’, ‘short.to’, ‘twit.ac’, ‘snipr.com’, ‘wefollow.com’, ‘tweet.sg’, ‘url4.eu’, ‘the-twitter-follow-train.info’, ‘fwix.com’, ‘budurl.com’, ‘su.pr’, ‘shar.es’, ‘tinychat.com’, ‘snipurl.com’, ‘loopt.us’, ‘migre.me’, ‘flic.kr’, ‘myspace.com’, ‘snurl.com’, ‘twitgoo.com’, ‘zshare.net’, ‘post.ly’, ‘bkite.com’, ‘yes.com’, ‘flickr.com’, ‘twitter.com’, ‘artistsforschapelle.com’, ‘140army.com’, ‘youtube.com’, ‘x.imeem.com’, ‘pic.gd’, ‘TwitterBackgrounds.com’, ‘raptr.com’, ‘twt.gs’, ‘twitthis.com’, ‘mobypicture.com’, ‘tobtr.com’, ‘ad.vu’, ‘sml.vg’, ‘rubyurl.com’, ‘tinylink.com’, ‘redirx.com’, ‘a2a.me’, ‘eCa.sh’, ‘vimeo.com’, ‘meadd.com’, ‘hotjobs.yahoo.com’, ‘doiop.com’, ‘myurl.in’, ‘urlpire.com’, ‘buzzup.com’, ‘freead.im’, ‘youradder.com’, ‘facebook.com’, ‘adf.ly’, ‘justin.tv’, ‘twitvid.com’, ‘adjix.com’, ‘twcauses.com’, ‘lkbk.nu’, ‘tlre.us’, ‘htxt.it’, ‘stickam.com’, ‘twubs.com’, ‘isy.gs’, ‘reverbnation.com’, ‘news.bbc.co.uk’, ‘sn.im’, ‘twibes.com’, ‘ustream.tv’, ‘trim.su’, ‘hashjobs.com’, ‘blogtv.com’, ‘jobs-cb.de’, ‘xsaimex.com’]

Retweets

~4% of messages are retweets

Replied @Users

~1M total replied-to users in this data set

37% of tweets contain ‘@x’ terms

Most Popular Replied-to Users (almost all celebrities):

[‘@mileycyrus’, ‘@jonasbrothers’, ‘@ddlovato’, ‘@mitchelmusso’, ‘@donniewahlberg’, ‘@souljaboytellem’, ‘@tommcfly’, ‘@addthis’, ‘@officialtila’, ‘@johncmayer’, ‘@shanedawson’, ‘@bowwow614’, ‘@jordanknight’, ‘@ryanseacrest’, ‘@perezhilton’, ‘@jonathanrknight’, ‘@petewentz’, ‘@tweetmeme’, ‘@adamlambert’, ‘@david_henrie’, ‘@dealsplus’, ‘@dwighthoward’, ‘@iamdiddy’, ‘@lancearmstrong’, ‘@songzyuuup’, ‘@imeem’, ‘@blakeshelton’, ‘@dannymcfly’, ‘@lilduval’, ‘@selenagomez’, ‘@markhoppus’, ‘@yelyahwilliams’, ‘@therealpickler’, ‘@stephenfry’, ‘@mrtweet.’, ‘@taylorswift13’, ‘@michaelsarver1’, ‘@davidarchie’, ‘@the_real_shaq’, ‘@tyrese4real’, ‘@britneyspears’, ‘@106andpark’, ‘@ashleytisdale’, ‘@mariahcarey’, ‘@kimkardashian’, ‘@wale’, ‘@mashable’, ‘@programapanico’, ‘@therealjordin’, ‘@listensto’, ‘@misskeribaby’, ‘@alyssa_milano’, ‘@alexalltimelow’, ‘@aplusk’, ‘@thisisdavina’, ‘@breakingnews:’, ‘@peterfacinelli’, ‘@truebloodhbo’, ‘@mgiraudofficial’, ‘@tonyspallelli’, ‘@mtv’, ‘@jackalltimelow’, ‘@dfizzy’, ‘@youngq’, ‘@tomfelton’, ‘@pooch_dog’, ‘@jonaskevin’, ‘@princesammie’, ‘@nkotb’, ‘@christianpior’, ‘@cthagod’, ‘@johnlloydtaylor’, ‘@neilhimself’, ‘@moontweet’, ‘@katyperry’, ‘@danilogentili’, ‘@mchammer’, ‘@rainnwilson’, ‘@joeymcintyre’, ‘@30secondstomars’, ‘@phillyd’, ‘@heidimontag’, ‘@mrpeterandre’, ‘@andyclemmensen’, ‘@crystalchappell’, ‘@kevindurant35’, ‘@huckluciano’, ‘@dannygokey’, ‘@jaketaustin’, ‘@revrunwisdom’, ‘@jamesmoran’, ‘@musewire’, ‘@dannywood’, ‘@nickiminaj’, ‘@akgovsarahpalin’, ‘@terrencej106’, ‘@mashable:’, ‘@drewryanscott’, ‘@mrtweet’, ‘@necolebitchie’, ‘@lilduval:’, ‘@willie_day26’, ‘@kirstiealley’, ‘@betthegame’, ‘@radiomsn’, ‘@alancarr’, ‘@rafinhabastos’, ‘@krisallen4real’, ‘@iamjericho’, ‘@breakingnews’, ‘@babygirlparis’, ‘@ladygaga’, ‘@chris_daughtry’, ‘@hypem’, ‘@danecook’, ‘@imcudi’, ‘@jeepersmedia’, ‘@buckhollywood’, ‘@kimmyt22’, ‘@giulianarancic’, ‘@chrisbrogan’, ‘@nasa’, ‘@addtoany’, ‘@nickcarter’, ‘@debbiefletcher’, ‘@marcoluque’, ‘@shaundiviney’, ‘@ogochocinco’, ‘@twitter’, ‘@eddieizzard’, ‘@youngbillymays’, ‘@real_ron_artest’, ‘@pink’, ‘@laurenconrad’, ‘@rubarrichello’, ‘@ianjamespoulter’, ‘@liltwist’, ‘@teyanataylor’, ‘@dougiemcfly’, ‘@theellenshow’, ‘@robkardashian’, ‘@sherrieshepherd’, ‘@justinbieber’, ‘@paulaabdul’, ‘@jason_manford’, ‘@jaredleto’, ‘@tracecyrus’, ‘@itsonalexa’, ‘@ddlovato:’, ‘@khloekardashian’, ‘@revrunwisdom:’, ‘@solangeknowles’, ‘@allison4realzzz’, ‘@nickjonas’, ‘@reply’, ‘@anarbor’, ‘@donlemoncnn’, ‘@gfalcone601’, ‘@moonfrye’, ‘@symphnysldr’, ‘@iamspectacular’, ‘@honorsociety’, ‘@questlove’, ‘@guykawasaki’, ‘@dawnrichard’, ‘@_maxwell_’, ‘@somaya_reece’, ‘@mandyyjirouxx’, ‘@teemwilliams’, ‘@greggarbo’, ‘@pennjillette’, ‘@mikeyway’, ‘@matthardybrand’, ‘@iamjonwalker’, ‘@andyroddick’, ‘@kohnt01’, ‘@chris_gorham’, ‘@seankingston’, ‘@joshgroban’, ‘@mousebudden’, ‘@misskatieprice’, ‘@spencerpratt’, ‘@wilw’, ‘@jgshock’, ‘@swear_bot’, ‘@joelmadden’, ‘@techcrunch’, ‘@americanwomannn’, ‘@kelly__rowland’, ‘@mionzera’, ‘@astro_127’, ‘@_@’, ‘@spam’, ‘@sookiebontemps’, ‘@drakkardnoir’, ‘@noh8campaign’, ‘@kayako’, ‘@trvsbrkr’, ‘@qbkilla’, ‘@mw55’, ‘@guykawasaki:’, ‘@donttrythis’, ‘@cv31’, ‘@liljjdagreat’, ‘@tiamowry’, ‘@nickensimontwit’, ‘@holdemtalkradio’, ‘@bradiewebbstack’, ‘@nytimes’, ‘@riskybizness23’, ‘@radityadika’, ‘@adrienne_bailon’, ‘@riccklopes’, ‘@jessicasimpson’, ‘@sportsnation’, ‘@jasonbradbury’, ‘@huffingtonpost’, ‘@oceanup’, ‘@gilbirmingham’, ‘@iconic88’, ‘@the’, ‘@thebrandicyrus’, ‘@gordela’, ‘@thedebbyryan’, ‘@jessemccartney’, ‘@?’, ‘@caiquenogueira’, ‘@celsoportiolli’, ‘@shontelle_layne’, ‘@calvinharris’, ‘@chattyman’, ‘@ali_sweeney’, ‘@anamariecox’, ‘@joshthomas87’, ‘@emilyosment’, ‘@nasa:’, ‘@sevinnyne6126’, ‘@thebiggerlights’, ‘@theboygeorge’, ‘@jbarsodmg’, ‘@goldenorckus’, ‘@warrenwhitlock’, ‘@bobbyedner’, ‘@myfabolouslife’, ‘@descargaoficial’, ‘@ochonflcinco85’, ‘@ninabrown’, ‘@billycurrington’, ‘@oprah’, ‘@junior_lima’, ‘@asherroth’, ‘@starbucks’, ‘@jason_pollock’, ‘@intanalwi’, ‘@harrislacewell’, ‘@serenajwilliams’, ‘@kevinruddpm’, ‘@bigbrotherhoh’, ‘@oliviamunn’, ‘@chamillionaire’, ‘@tamekaraymond’, ‘@teamwinnipeg’, ‘@littlefletcher’, ‘@piercethemind’, ‘@brookandthecity’, ‘@iranbaan:’, ‘@tonyrobbins’, ‘@maestro’, ‘@glennbeck’, ‘@1omarion’, ‘@nadhiyamali’, ‘@slimthugga’, ‘@jason_mraz’, ‘@profbrendi’, ‘@djaaries’, ‘@juanestwiter’, ‘@davegorman’, ‘@zackalltimelow’, ‘@mamajonas’, ‘@itschristablack’, ‘@skydiver’, ‘@gigva’, ‘@currensy_spitta’, ‘@paulwallbaby’, ‘@rpattzproject’, ‘@petewentz:’, ‘@rodrigovesgo’, ‘@drdrew’, ‘@sportsguy33’, ‘@cthagod:’, ‘@hollymadison123’, ‘@mjjnews’, ‘@itsbignicholas’, ‘@_supernatural_’, ‘@santoevandro’, ‘@demar_derozan’, ‘@marthastewart’, ‘@billganz62’, ‘@oodle’, ‘@davidleibrandt’]

Hashtags

~7% of messages contain hashtags

Total Unique Hashtags found: ~94k

Top Hashtags:

[‘#lies’, ‘#fb’, ‘#musicmonday’, ‘#truth’, ‘#iranelection’, ‘#moonfruit’, ‘#tendance’, ‘#jobs’, ‘#ihavetoadmit’, ‘#mariomarathon’, ‘#140mafia’, ‘#tcot’, ‘#zyngapirates’, ‘#followfriday’, ‘#spymaster’, ‘#ff’, ‘#1’, ‘#sotomayor’, ‘#turnon’, ‘#notagoodlook’, ‘#tweetmyjobs’, ‘#hiring:’, ‘#iran’, ‘#fun140’, ‘#jesus’, ‘#72b381.’, ‘#quote’, ‘#tinychat’, ‘#neda’, ‘#militarymon’, ‘#gr88’, ‘#trueblood’, ‘#fail’, ‘#news’, ‘#140army’, ‘#livestrong’, ‘#noh8’, ‘#wpc09’, ‘#music’, ‘#turnoff’, ‘#unacceptable’, ‘#twables’, ‘#masterchef’, ‘#noh84kradison’, ‘#writechat’, ‘#job’, ‘#squarespace’, ‘#michaeljackson’, ‘#2’, ‘#nothingpersonal’, ‘#iphone’, ‘#ala2009’, ‘#mj’, ‘#tdf’, ‘#blogtalkradio’, ‘#mlb’, ‘#1stdraftmovielines’, ‘#p2’, ‘#secretagent’, ‘#tlot’, ‘#72b381’, ‘#honduras’, ‘#twitter’, ‘#jtv’, ‘#tehran’, ‘#gorillapenis’, ‘#porn’, ‘#bb11’, ‘#sotoshow’, ‘#brazillovesatl’, ‘#google’, ‘#oneandother’, ‘#bb10’, ‘#chucknorris’, ‘#cmonbrazil’, ‘#agendasource’, ‘#travel’, ‘#ashes’, ‘#dumbledore’, ‘#freeschapelle’, ‘#tl’, ‘#dealsplus’, ‘#nsfw’, ‘#entourage’, ‘#tech’, ‘#hottest100’, ‘#3693dh…’, ‘#torchwood’, ‘#design’, ‘#teaparty’, ‘#love’, ‘#dontyouhate’, ‘#mileycyrus’, ‘#sgp’, ‘#harrypottersequels’, ‘#peteandinvisiblechildren’, ‘#stopretweets’, ‘#tscc’, ‘#wimbledon’, ‘#hive’, ‘#cubs’, ‘#3’, ‘#redsox’, ‘#photography’, ‘#voss’, ‘#snods’, ‘#lol’, ‘#socialmedia’, ‘#gop’, ‘#health’, ‘#esriuc’, ‘#green’, ‘#follow’, ‘#echo!’, ‘#obama’, ‘#digg’, ‘#shazam’, ‘#hhrs’, ‘#video’, ‘#moonfruit.’, ‘#swineflu’, ‘#politics’, ‘#ebuyer683’, ‘#umad’, ‘#quizdostandup’, ‘#thankyoumichael’, ‘#blogchat’, ‘#wordpress’, ‘#3693dh’, ‘#haiku’, ‘#ttparty’, ‘#lastfm:’, ‘#healthcare’, ‘#hcr’, ‘#ecgc’, ‘#seo’, ‘#apple’, ‘#chuck’, ‘#wine’, ‘#sammie’, ‘#h1n1’, ‘#marketing’, ‘#twitition’, ‘#happybirthdaymitchel18’, ‘#cnn’, ‘#lie’, ‘#rt:’, ‘#art’, ‘#nasa’, ‘#blog’, ‘#quotes’, ‘#bruno’, ‘#business’, ‘#palin’, ‘#mw2’, ‘#hcsm’, ‘#harrypotter’, ‘#4’, ‘#lastfm’, ‘#askclegg’, ‘#photo’, ‘#jobfeedr’, ‘#lgbt’, ‘#lies:’, ‘#ihavetoadmit.i’, ‘#jamlegend,’, ‘#truthbetold’, ‘#mcfly’, ‘#microsoft’, ‘#fashion’, ‘#tweetphoto’, ‘#ebuyer167201’, ‘#noh84adison’, ‘#5’, ‘#mets’, ‘#china’, ‘#bigprize’, ‘#whythehell’, ‘#money’, ‘#sophiasheart’, ‘#finance’, ‘#michael’, ‘#f1’, ‘#adamlambert100k’, ‘#web’, ‘#urwashed’, ‘#moonfruit!’, ‘#1:’, ‘#kayako’, ‘#lies.’, ‘#thankyouaaron’, ‘#food’, ‘#wow’, ‘#moonfruit,’, ‘#facebook’, ‘#ebuyer291’, ‘#ecomonday’, ‘#ihave’, ‘#happybdaydenise’, ‘#postcrossing’, ‘#ichc’, ‘#912’, ‘#demilovatolive’, ‘#gijoemoviefan’, ‘#funny’, ‘#media’, ‘#meowmonday’, ‘#israel’, ‘#blogger’, ‘#forasarney’, ‘#tv’, ‘#topgear’, ‘#chrisisadouche’, ‘#stlcards’, ‘#wec09’, ‘#forex’, ‘#aots1000’, ‘#celebrity’, ‘#dwarffilmtitles’, ‘#6’, ‘#yeg’, ‘#slaughterhouse’, ‘#nfl’, ‘#photog’, ‘#ny’, ‘#firstdraftmovies’, ‘#ufc’, ‘#reddit’, ‘#free’, ‘#iwish’, ‘#etsy’, ‘#rulez’, ‘#sports’, ‘#icmillion’, ‘#mmot’, ‘#webdesign’, ‘#deals’, ‘#moonfruit?’, ‘#pawpawty’, ‘#twitterfahndung’, ‘#billymaystribute’, ‘#sytycd’, ‘#runkeeper’, ‘#scotus’, ‘#yoconfieso’, ‘#mariomarathon,’, ‘#musicmondays’, ‘#lies,’, ‘#findbob’, ‘#realestate’, ‘#sohrab’, ‘#sales’, ‘#metal’, ‘#runescape’, ‘#hypem’, ‘#threadless’, ‘#gay’, ‘#isyouserious’, ‘#hollywood,’, ‘#2:’, ‘#ca,’, ‘#golf’, ‘#diadorock’, ‘#newyork,’, ‘#meteor’, ‘#dailyquestion’, ‘#photoshop’, ‘#saveiantojones’, ‘#musicmonday:’, ‘#rock’, ‘#sex’, ‘#mlbfutures’, ‘#ilove’, ‘#mikemozart’, ‘#nascar’, ‘#indico’, ‘#crossfitgames’, ‘#gratitude’, ‘#quote:’, ‘#creativetechs’, ‘#truth:’, ‘#sharepoint’, ‘#mkt’, ‘#why’, ‘#bigbrother’, ‘#tam7’, ‘#ihate’, ‘#futureruby’, ‘#slickrick’, ‘#105.3’, ‘#youareinatl’, ‘#vegan’, ‘#dontletmefindout’, ‘#imustadmit’, ‘#7’, ‘#twitterafterdark’, ‘#sunnyfacts’, ‘#gilad’, ‘#japan’, ‘#iremember’, ‘#97.3’, ‘#puffdaddy’, ‘#blogher’, ‘#ade2009’, ‘#aaliyah’, ‘#alfredosms’, ‘#95.1’, ‘#truth,’, ‘#twine’, ‘#hiring’]

Questions

Hard to infer exactly whether a message is a question or not, so I ran a couple of different filters:

5W’s, H, ? present ANYWHERE in tweet:

0.102789281948 or 10%

5W’s, H first token or ? last token:

0.0238229662219 or 2%

Just ? ANYWHERE in tweet:

0.0040984928533 or 0.4%

Users

Discovered ~2M unique users

Top Sending Users (many bots):

[‘followermonitor’, ‘Tweet_Words’, ‘currentcet’, ‘currentutc’, ‘whattimeisitnow’, ‘ItIsNow’, ‘ThinkingStiff’, ‘otvrecorder’, ‘delicious50’, ‘Porngus’, ‘craigslistjobs’, ‘GorPen’, ‘hashjobs’, ‘TransAlchemy2’, ‘bot_theta’, ‘CHRISVOSS’, ‘bot_iota’, ‘bot_kappa’, ‘TIPAS’, ‘VeolaJBanner’, ‘StacyDWatson’, ‘LMAObot’, ‘SarahJSlonecker’, ‘AllisonMRussell’, ‘bot_eta’, ‘SandraHOakley’, ‘bot_psi’, ‘bot_tau’, ‘LoreleiRMercer’, ‘bot_zeta’, ‘bot_gamma’, ‘bot_sigma’, ‘bot_lambda’, ‘bot_pi’, ‘bot_epsilon’, ‘bot_nu’, ‘bot_rho’, ‘bot_omicron’, ‘bot_khi’, ‘LindaTYoung’, ‘mensrightsindia’, ‘bot_omega’, ‘bot_ksi’, ‘bot_delta’, ‘bot_alpha’, ‘bot_phi’, ‘CindaDJenkins’, ‘bot_mu’, ‘ImogeneDPetit’, ‘bot_upsilon’, ‘OPENLIST_CA’, ‘openlist’, ‘isygs’, ‘dq_jumon’, ‘gamingscoop’, ‘MildredSLogan’, ‘ObiWanKenobi_’, ‘pulseSearch’, ‘MaryEVo’, ‘ImeldaGMcward’, ‘MaryJNewman’, ‘SharonTForde’, ‘LoriJCornelius’, ‘BrandyWPulliam’, ‘RhondaTLopez’, ‘AprilKOropeza’, ‘CarolETrotman’, ‘SusanATouvell’, ‘dinoperna’, ‘buzzurls’, ‘_Freelance_’, ‘DrSnooty’, ‘illstreet’, ‘bibliotaph_eyes’, ‘loc4lhost’, ‘bsiyo’, ‘BOTHOUSE’, ‘post_ads’, ‘qazkm’, ‘frugaldonkey’, ‘free_post’, ‘groovera’, ‘wonkawonkawonka’, ‘ForksGirlBella’, ‘casinopokera’, ‘dermdirectoryny’, ‘Yoowalk_chat’, ‘mstehr’, ‘hashgoogle’, ‘perry1949’, ‘ensiz_news’, ‘Bezplatno_net’, ‘timesmirror’, ‘work_freelance’, ‘cockbot’, ‘pdurham’, ‘bombtter_raw’, ‘ocha1’, ‘AlairAneko24’, ‘HaiIAmDelicious’, ‘Freshestjobs’, ‘fast_followers’, ‘LeadsForFree’, ‘RideOfYourLife’, ‘AlastairBotan30’, ‘helpmefast25’, ‘TheMLMWizard’, ‘uitrukken’, ‘adoptedALICE’, ‘TKATI’, ‘ezadsncash’, ‘tweetshelp’, ‘LAmetro_traffic’, ‘thinkpozzitive’, ‘StarrNeishaa’, ‘AldenCho36’, ‘JobHits’, ‘wootboot’, ‘smacula’, ‘faithclubdotnet’, ‘DmitriyVoronov’, ‘brownthumbgirl’, ‘NYCjobfeed’, ‘hfradiospacewx’, ‘FakeeKristenn’, ‘MLBDAILYTIMES’, ‘wildingp’, ‘JacksonsReview’, ‘EarthTimesPR’, ‘friedretweet’, ‘Wealthy23’, ‘RokpoolFM’, ‘HDOLLAZ’, ‘_MrSpacely’, ‘Bestdocnyc’, ‘Rabidgun’, ‘flygatwick’, ‘live_china’, ‘friendlinks’, ‘retweetinator’, ‘iamamro’, ‘thayferreira’, ‘AldisDai39’, ‘AndersHana60’, ‘nonstopNEWS’, ‘VivaLaCash’, ‘TravelNewsFeeds’, ‘vuelosplus’, ‘threeporcupines’, ‘DemiAuzziefan’, ‘worldofprint’, ‘KevinEdwardsJr’, ‘REDDITSPAMMOR’, ‘NatValentine’, ‘ChanelLebrun’, ‘nowbot’, ‘hollyswansonUK’, ‘youngrhome’, ‘M_Abricot’, ‘thefakemandyv’, ‘scrapbookingpas’, ‘Naughtytimes’, ‘Opcode1300_bot’, ‘tellsecret’, ‘tboogie937’, ‘Climber_IT’, ‘comlist’, ‘with_a_smile’, ‘USN_retired’, ‘Climber_EngJobs’, ‘Climber_Finance’, ‘Climber_HRJobs’, ‘intanalwi’, ‘Climber_Sales’, ‘nadhiyamali’, ‘wonderfulquotes’, ‘MRAustria’, ‘O2Q’, ‘GL0’, ‘SookieBonTemps’, ‘MRSchweiz’, ‘latinasabor’, ‘nineleal’, ‘casservice’, ‘AltonGin54’, ‘KulerFeed’, ‘_cesaum’, ‘HFMONAIR’, ‘DeeOnDreeYah’, ‘rockstalgica’, ‘iamword’, ‘rpattzproject’, ‘madblackcatcom’, ‘ftfradio’, ‘marciomtc’, ‘SocialNetCircus’, ‘AnotherYearOver’, ‘ichig’, ‘tcikcik’, ‘HelenaMarie210’, ‘mrbax0’, ‘SWBot’, ‘DayTrends’, ‘_Embry_Call_’, ‘eProducts24’, ‘The_Sims_3’, ‘tom_ssa’, ‘woxy_vintage’, ‘urbanmusic2000’, ‘dopeguhxfresh’, ‘erections’, ‘DudeBroChill’, ‘lookingformoney’, ‘drnschneider’, ‘MosesMaimonides’, ’92Blues’, ‘elarmelar’, ‘rock937fm’, ‘sonicfm’, ‘erikadotnet’, ‘sky0311’, ‘weqx’, ‘brandamc’, ‘Hot106’, ‘woxy_live’, ‘ksopthecowboy’, ‘vixalius’, ‘cogourl’, ‘Cashintoday’, ‘Andrewdaflirt’, ‘oodle’, ‘mkephart25’, ‘doomed’, ‘spotifyuri’, ‘mangelat’, ‘Cody_K’, ‘swayswaystacey’, ‘KLLY953’, ‘onlaa’, ‘Ginger_Swan’, ‘Call_Embry’, ‘conservatweet’, ‘weerinlelystad’, ‘ruhanirabin’, ‘tmgadops’, ‘wakemeupinside1’, ‘horaoficial’, ‘xstex’, ‘franzidee’, ‘tommytrc’, ‘khopmusic’, ‘tez19’, ‘GaryGotnought’, ‘UnemployKiller’, ‘felloff’, ‘Kalediscope’, ‘TheRealSherina’, ‘jasonsfreestuff’, ‘johnkennick’, ‘sel_gomezx3’, ‘OE3’, ‘AddisonMontg’, ‘_rosieCAKES’, ‘neownblog’, ‘PrinceP23’, ‘ontd_fluffy’, ‘USofAl’, ‘Kacizzle88’, ‘somalush’, ‘FrankieNichelle’, ‘jiva_music’, ‘itz_cookie’, ‘soundOfTheTone’, ‘knowheremom’, ‘Jayme1988’, ‘TrafficPilot’, ‘tweetalot’, ‘TheStation1610’, ‘lasvegasdivorce’, ‘1000_LINKS_NOW2’, ‘KeepOnTweeting’, ‘uFreelance’, ‘ChocoKouture’, ‘Magic983’, ‘SnarkySharky’, ‘agthekid’, ‘cashinnow’, ‘jamokie’, ‘jessicastanely’, ‘Q103Albany’, ‘GPGTwit’, ‘xAmberNicholex’, ‘wjtlplaylist’, ‘sjAimee’, ‘chrisduhhh’, ‘failbus’, ‘1stwave’, ‘RichardBejah’, ‘nyanko_love’]

Web Queries Overlap

How much overlap is there between tweets and trending web search queries?

I took the top trending queries during the days of my twitter crawl from Google Trends, then query expanded each trending query until the length was 6 tokens so as to equalize the average lengths. Then, I simply counted how many tweets match at least 2 (cleaned) tokens of any of these query-expanded trends:

0.0185654981775 or 2%

That’s it for now. I have some more stats but need a bit more time to clean those up before publishing here.

Notes

Can’t distribute my data set unfortunately, but it shouldn’t take too long to assemble a comparable set via Twitter’s spritzer feed – that’ll probably be more useful as it’ll be more update-to-date than the one I analyzed here. Feel free to pull my stats off if you find them useful (top hashtags and users are in JSON format).

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Filed under Data Mining, Research, Search, Social, Statistics, Trends, Twitter

Build an Automatic Tagger in 200 lines with BOSS

My colleagues and I will be giving a talk on BOSS at Yahoo!’s Hack Day in NYC on October 9. To show developers the versatility of an open search API, I developed a simple toy example (see my past ones: TweetNews, Q&A) on the flight over that uses BOSS to generate data for training a machine learned text classifier. The resulting application basically takes two tags, some text, and tells you which tag best classifies that text. For example, you can ask the system if some piece of text is more liberal or conservative.

How does it work? BOSS offers delicious metadata for many search results that have been saved in delicious. This includes top tags, their frequencies, and the number of user saves. Additionally, BOSS makes available an option to retrieve extended search result abstracts. So, to generate a training set, I first build up a query list (100 delicious popular tags), search each query through BOSS (asking for 500 results per), and filter the results to just those that have delicious tags.

Basically, the collection logically looks like this:

[(result_1, delicious_tags), (result_2, delicious_tags) …]

Then, I invert the collection on the tags while retaining each result’s extended abstract and title fields (concatenated together)

This logically looks like this now:

[(tag_1, result_1.abstract + result_1.title), (tag_2, result_1.abstract + result_1.title), …, (tag_1, result_2.abstract + result_2.title), (tag_2, result_2.abstract + result_2.title) …]

To build a model comparing 2 tags, the system selects pairs from the above collection that have matching tags, converts the abstract + title text into features, and then passes the resulting pairs over to LibSVM to train a binary classification model.

Here’s how it works:

tagger viksi$ python gen_training_test_set.py liberal conservative

tagger viksi$ python autosvm.py training_data.txt test_data.txt

__Searching / Training Best Model

____Trained A Better Model: 60.5263

____Trained A Better Model: 68.4211

__Predicting Test Data

__Evaluation

____Right: 16

____Wrong: 4

____Total: 20

____Accuracy: 0.800000

get_training_test_set finds the pairs with matching tags and split those results into a training (80% of the pairs) and test set (20%), saving the data as training_data.txt and test_data.txt respectively. autosvm learns the best model (brute forcing the parameters for you – could be handy by itself as a general learning tool) and then applies it to the test set, reporting how well it did. In the above case, the system achieved 80% accuracy over 20 test instances.

Here’s another way to use it:

tagger viksi$ python classify.py apple microsoft bill gates steve ballmer windows vista xp

microsoft

tagger viksi$ python classify.py apple microsoft steve jobs ipod iphone macbook

apple

classify combines the above steps into an application that, given two tags and some text, will return which tag more likely describes the text. Or, in command line form, ‘python classify.py [tag1] [tag2] [some free text]’ => ‘tag1’ or ‘tag2’

My main goal here is not to build a perfect experiment or classifier (see caveats below), but to show a proof of concept of how BOSS or open search can be leveraged to build intelligent applications. BOSS isn’t just a search API, but really a general data API for powering any application that needs to party on a lot of the world’s knowledge.

I’ve open sourced the code here:

http://github.com/zooie/tagger

Caveats

Although the total lines of code is ~200 lines, the system is fairly state-of-the-art as it employs LibSVM for its learning model. However, this classifier setup has several caveats due to my time constraints and goals, as my main intention for this example was to show the awesomeness of the BOSS data. For example, training and testing on abstracts and titles means the top features will probably be inclusive of the query, so the test set may be fairly easy to score well on as well as not be representative of real input data. I did later add code to remove query related features from the test set and the accuracy seemed to dip just slightly. For classify.py, the ‘some free text’ input needs to be fairly large (about an extended abstract’s size) to be more accurate. Another caveat is what happens when both tags have been used to label a particular search result. The current system may only choose one tag, which may incur an error depending on what’s selected in the test set. Furthermore, the features I’m using are super simple and can be greatly improved with TFIDF scaling, normalization, feature selection (mutual information gain), etc. Also, more training / test instances (and check the distribution of the labels), baselines and evaluation measures should be tested.

I could have made this code a lot cleaner and shorter if I just used LibSVM’s python interface, but I for some reason forgot about that and wrote up scripts that parsed the stdout messages of the binaries to get something working fast (but dirty).

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Filed under AI, Boss, Code, CS, Data Mining, delicious, Information Retrieval, Machine Learning, Open Source, Research, Search, Social, Statistics, Talk, Tutorial, Yahoo