Twitter usage patterns

What kind of Twitterer am I? I'm kind of on a role with this data mining of Twitter so I'll take a brief look at my own Twitter habits.

<<Twitter`

session = TwitterSessionOpen["User"->"ragfield"]

TwitterSession[<ragfield>]

Length[tweets = TwitterUserAllTimeline[session]]

1487

DateDifference[TwitterStatusDate@Last@tweets, TwitterStatusDate@First@tweets, {"Year", "Month", "Day"}]

{{1, Year}, {3, Month}, {20.83855324074074`, Day}}

Length[tweets] / DateDifference[TwitterStatusDate@Last@tweets, TwitterStatusDate@First@tweets, {"Day"}][[1, 1]]

3.125009501379522`

1487 total tweets in the last 1 year, 3 months, and 21 days. On average that's 3.125 tweets per day.

DateListPlot[Tally[{#[[1]], #[[2]], 1, 0, 0, 0.}&/@(TwitterStatusDate/@tweets)], Joined->True, Filling->Axis, FrameLabel->{"Month", "Tweets per month"}, PlotLabel->"Ragfield Twitter usage", DateTicksFormat->{"MonthNameShort", " ", "Year"}]

Total[StringLength[TwitterStatusText[#]]&/@tweets]

107442

N[% / Length[tweets]]

72.2542030934768`

107,442 total characters typed. On average that's 72 characters per tweet, roughly half the allotted space.

First@SortBy[TwitterStatusText/@tweets, StringLength]

bed

My shortest tweet was simply "bed".

Length[allWords = StringSplit[StringJoin[Riffle[TwitterStatusText/@tweets, " "]], Except[WordCharacter|"'"]..]]

19188

19,188 unique words typed.

Length[allWords = DeleteCases[allWords, x_/;StringMatchQ[x, DigitCharacter..]]]

18528

18,528 if you don't count numbers.

First@Reverse@SortBy[Tally[allWords], Part[#, 2]&]

{the, 604}

The most common word I've typed is "the". That's not terribly useful. Let's take a look at just nouns to see what kind of topics I mention most frequently.

Length[nouns = Cases[allWords, x_/;MemberQ[WordData[x], "Noun", ‚àû]]]

7883

Grid[Take[Reverse@SortBy[Tally[nouns], Part[#, 2]&], 30], Alignment->{{Right, Left}}]

I	431
a	419
in	259
at	126
have	77
bike	62
work	54
time	52
out	51
are	48
ride	45
think	43
so	43
run	43
now	42
like	42
one	38
d	38
morning	36
last	36
can	36
race	35
miles	35
mile	35
home	35
first	35
way	34
still	34
year	31
good	31

Most common topics: bike, work, time, ride, think, run, like, morning, race, mile(s), home.

We can do the same thing with verbs to see what kind of actions I describe most frequently.

Length[verbs = Cases[allWords, x_/;MemberQ[WordData[x], "Verb", ‚àû]]]

4300

Grid[Take[Reverse@SortBy[Tally[verbs], Part[#, 2]&], 30], Alignment->{{Right, Left}}]

is	164
was	108
be	80
have	77
bike	62
up	59
work	54
time	52
out	51
ride	45
been	44
think	43
run	43
has	43
like	42
last	36
can	36
race	35
home	35
still	34
had	32
got	32
get	30
do	30
back	28
long	27
will	26
see	26
did	25
know	24

Most common actions: bike, work, ride, think, run, race, see, know. I guess there's a lot of overlap between the nouns and the verbs.

It's also pretty easy to determine the other users I mention most frequently.

Grid[Take[Reverse@SortBy[Tally[StringCases[StringJoin[Riffle[TwitterStatusText/@tweets, " "]], "@"~~a:Except[WhitespaceCharacter]..:>Hyperlink[ToLowerCase[a], "http://twitter.com/"<>ToLowerCase[a]]]], Part[#, 2]&], 10]]

melissa_raguet	84
spoonshake	69
esmithrunner	32
erik_d	28
dbfulton	18
gutzville	14
ultrashea	11
erik__d	9
adamengst	9
chockenberry	6

Downloads

Download Twitter.m.

Download WebUtils.m (required by Twitter.m).

Download Mathematica Notebook.