BBC Politics Election Tweets: A Quick Text Analysis

I collected 100 tweets from the official @BBCPolitics Twitter account posted between 26/05/2014 00:14:31 and  26/05/2014 12:59:10 BST. I collected the tweets using Martin Hawksey‘s TAGS.

I copied the text of the tweets and ran a basic text analysis using Voyant Tools by Stéfan Sinclair & Geoffrey Rockwell. I customised the English ‘Taporware’ stop word list to include reporting-specific terms (such as ‘says’–this should be further refined, as I accidentally left ‘declared’) and Twitter-specific terms likely to be over-represented, like ‘http’, ‘rt’ and ‘t.co’.  (Some shortened URLs remained).  I left the hashtags ‘#EP2014’ and ‘#vote2014’ in the corpus on purpose.

There is 1 document in this corpus with a total of 1,956 words and 695 unique words.

If needed, click on image to enlarge.

Words in the Entire Corpus
Corpus Term Frequencies provides an ordered list for all the terms’ frequencies appearing in a corpus. The first column indicates the keyword in order of frequency; the second column the number of times it appears in the corpus. The other columns can be toggled to show other statistical information, including a small line graph for term frequency across the corpus.

Words in the Entire Corpus. Corpus Term Frequencies provides an ordered list for all the terms’ frequencies appearing in a corpus. As well additional columns can be toggled to show other statistical information, including a small line graph for term frequency across the corpus. Created with Voyant Tools by Stéfan Sinclair & Geoffrey Rockwell (©2014).

#vote2014	32	5.10	–	172.3	0.000	–	–
ukip	18	2.66	–	96.9	0.000	–	–
election	17	2.49	–	91.5	0.000	–	–
lib	17	2.49	–	91.5	0.000	–	–
elections	15	2.14	–	80.8	0.000	–	–
european	15	2.14	–	80.8	0.000	–	–
results	15	2.14	–	80.8	0.000	–	–
#ep2014	13	1.79	–	70.0	0.000	–	–
vote	13	1.79	–	70.0	0.000	–	–
@bbcr4today	11	1.44	–	59.2	0.000	–	–
lab	11	1.44	–	59.2	0.000	–	–
party	11	1.44	–	59.2	0.000	–	–
green	10	1.27	–	53.9	0.000	–	–
@chrismasonbbc	9	1.09	–	48.5	0.000	–	–
dem	9	1.09	–	48.5	0.000	–	–
eu	9	1.09	–	48.5	0.000	–	–
farage	9	1.09	–	48.5	0.000	–	–
labour	9	1.09	–	48.5	0.000	–	–
meps	9	1.09	–	48.5	0.000	–	–
result	9	1.09	–	48.5	0.000	–	–
uk	9	1.09	–	48.5	0.000	–	–
@bbcbreaking	8	0.92	–	43.1	0.000	–	–
david	8	0.92	–	43.1	0.000	–	–
dems	8	0.92	–	43.1	0.000	–	–
far	8	0.92	–	43.1	0.000	–	–
seat	8	0.92	–	43.1	0.000	–	–
#r4today	7	0.74	–	37.7	0.000	–	–
@bbcnormans	7	0.74	–	37.7	0.000	–	–
cameron	7	0.74	–	37.7	0.000	–	–
coverage	7	0.74	–	37.7	0.000	–	–
london	7	0.74	–	37.7	0.000	–	–
snp	7	0.74	–	37.7	0.000	–	–
@rebeccakeating	6	0.57	–	32.3	0.000	–	–
scotland	6	0.57	–	32.3	0.000	–	–
votes	6	0.57	–	32.3	0.000	–	–
euro	5	0.39	–	26.9	0.000	–	–
new	5	0.39	–	26.9	0.000	–	–
nick	5	0.39	–	26.9	0.000	–	–
parties	5	0.39	–	26.9	0.000	–	–
people	5	0.39	–	26.9	0.000	–	–
pm	5	0.39	–	26.9	0.000	–	–
seats	5	0.39	–	26.9	0.000	–	–
video	5	0.39	–	26.9	0.000	–	–
big	4	0.22	–	21.5	0.000	–	–
bnp	4	0.22	–	21.5	0.000	–	–
clegg	4	0.22	–	21.5	0.000	–	–
declared	4	0.22	–	21.5	0.000	–	–

—

 

I also collected 49 tweets posted by @bbcnickrobinson between 18/05/2014 21:21:34 and 26/05/2014 02:34:07 BST. I followed the same procedure as above, producing the following Cirrus cloud (if needed, click on image to enlarge) and frequency list.

There is 1 document in this corpus with a total of 946 words and 458 unique words.

Words in the Entire Corpus
Corpus Term Frequencies provides an ordered list for all the terms’ frequencies appearing in a corpus. The first column indicates the keyword in order of frequency; the second column the number of itmes it appears in the corpus. The other columns can be toggled to show other statistical information, including a small line graph for term frequency across the corpus.

Words in the Entire Corpus. Corpus Term Frequencies provides an ordered list for all the terms’ frequencies appearing in a corpus. As well additional columns can be toggled to show other statistical information, including a small line graph for term frequency across the corpus. Created with Voyant Tools by Stéfan Sinclair & Geoffrey Rockwell (©2014).

farage	10	2.71	–	106.0	0.000	–	–
ukip	9	2.36	–	95.4	0.000	–	–
blog	7	1.68	–	74.2	0.000	–	–
vote	7	1.68	–	74.2	0.000	–	–
lib	6	1.34	–	63.6	0.000	–	–
@bbcpolitics	5	1.00	–	53.0	0.000	–	–
clegg	5	1.00	–	53.0	0.000	–	–
election	5	1.00	–	53.0	0.000	–	–
nigel	5	1.00	–	53.0	0.000	–	–
night	5	1.00	–	53.0	0.000	–	–
power	5	1.00	–	53.0	0.000	–	–
@bbcnickrobinson	4	0.66	–	42.4	0.000	–	–
@nick	4	0.66	–	42.4	0.000	–	–
dem	4	0.66	–	42.4	0.000	–	–
european	4	0.66	–	42.4	0.000	–	–
just	4	0.66	–	42.4	0.000	–	–
morning	4	0.66	–	42.4	0.000	–	–
romanians	4	0.66	–	42.4	0.000	–	–
#ep2014	3	0.32	–	31.8	0.000	–	–
#vote2014	3	0.32	–	31.8	0.000	–	–
david	3	0.32	–	31.8	0.000	–	–
dimbleby	3	0.32	–	31.8	0.000	–	–
elections	3	0.32	–	31.8	0.000	–	–
got	3	0.32	–	31.8	0.000	–	–
interview	3	0.32	–	31.8	0.000	–	–
know	3	0.32	–	31.8	0.000	–	–
labour	3	0.32	–	31.8	0.000	–	–
millwall	3	0.32	–	31.8	0.000	–	–
poll	3	0.32	–	31.8	0.000	–	–
says	3	0.32	–	31.8	0.000	–	–
send	3	0.32	–	31.8	0.000	–	–
tories	3	0.32	–	31.8	0.000	–	–
uk	3	0.32	–	31.8	0.000	–	–
win	3	0.32	–	31.8	0.000	–	–
words	3	0.32	–	31.8	0.000	–	–
@bbcnews	2	-0.02	–	21.2	0.000	–	–
@nigel	2	-0.02	–	21.2	0.000	–	–
@thelawyercatrin	2	-0.02	–	21.2	0.000	–	–
answer	2	-0.02	–	21.2	0.000	–	–
band	2	-0.02	–	21.2	0.000	–	–
beaming	2	-0.02	–	21.2	0.000	–	–
capital	2	-0.02	–	21.2	0.000	–	–
completely	2	-0.02	–	21.2	0.000	–	–
coverage	2	-0.02	–	21.2	0.000	–	–
day	2	-0.02	–	21.2	0.000	–	–
dems	2	-0.02	–	21.2	0.000	–	–
didn’t	2	-0.02	–	21.2	0.000	–	–
doing	2	-0.02	–	21.2	0.000	–	–
ed	2	-0.02	–	21.2	0.000	–	–
europe	2	-0.02	–	21.2	0.000	–	–

It is significant that in these two small corpora from the two major BBC Politics Twitter accounts the top results had some clear coincidences. It’s up to the reader to draw conclusions. I have uploaded the source data to figshare: