Sheffield Digital Humanities Congress 2016: #dhcshef 100 Most Frequent Terms

The Sheffield Digital Humanities Congress 2016 was held from the 8th to the 10th of September 2016 at the University of Sheffield. The full conference programme is available here: http://www.hrionline.ac.uk/dhc.

The event’s official hashtag was the same as in previous editions, #dhcshef.

I made a collection of Tweets tagged with #dhcshef published publicly between Monday September 05 2016 at 17:54:58 +0000 and Saturday September 10 2016 at 23:37:06 +0000. This time I used Tweepy 3.5.0, a Python wrapper for the Twitter API, for the collection. To compare results I also used, as usual, Martin Hawksey’s TAGS, with results being similar (I only collected Tweets from accounts with at least 1 follower).

As in previous occasions I extracted the text and usernames from this dataset and used VoyantTools for a basic text analysis. The dataset contained 1479 Tweets posted by 256 different accounts. 841 of those were RTs. The text of the Tweets composed a corpus with 26,094 total words and 3,057 unique word forms.

I used Voyant’s Terms tool to get the most frequent terms, applying an edited English stop words list that included Twitter and congress-specific terms (this means that words expected to be frequent like ‘digital’, ‘humanities’, ‘congress’, ‘sheffield’, as well as usernames, project’s names and people’s names were filtered out). I exported a list of 500 most frequent terms and then I manually refined the data so remaining people or project’s names were removed. (This is not case sensitive so I may have made mistakes and further disambiguation and refining would be required). If you are interested I previously detailed a similar methodology here.

Here’s my resulting list of the 100 most frequent terms.

Term	Count
great	106
project	98
data	76
research	64
students	63
word	58
funding	55
work	55
just	53
spread	53
use	51
opportunity	48
text	47
historical	46
oa	46
looking	45
open	45
editions	44
pedagogy	40
academic	38
access	36
keynote	36
like	36
analysis	35
follow	35
using	34
book	33
new	33
projects	33
university	33
important	32
innovation	32
today	32
tomorrow	32
early	31
minimal	31
paper	31
south	31
content	30
excellent	30
love	30
social	30
look	29
talking	29
tools	29
discussing	28
global	28
grants	28
london	28
network	28
review	28
forward	27
libraries	27
resources	27
sudan	27
history	26
talk	26
books	25
online	25
programme	25
really	25
teach	25
teaching	25
digitisation	24
issues	24
tactical	24
archive	23
critique	23
make	23
different	22
need	22
peer	22
session	22
cultural	21
heritage	21
starts	21
studies	21
value	21
art	20
cool	20
don’t	20
good	20
live	20
press	20
start	20
arts	19
available	19
colleagues	19
delegates	19
going	19
metadata	19
presenting	19
day	18
digitised	18
let’s	18
networks	18
notes	18
person	18
started	18
begins	17

Please bear in mind that RTs count as Tweets and therefore the repetition implicit in RTs affects directly the frequent term counts. What terms made it into the top 100 reflects my own bias (I personally didn’t want to see how many times ‘digital’ or ‘humanities’ was repeated), but individual trend counts remain the same regardless.

I appreciate the stop words selection is indeed subjective (deictics like ‘tomorrow’ or ‘today’ may very well mean very little).  It’s up to the reader to judge if such a listing offers any insights at all; as Twitter moves relentlessly and as such data remains a moving a target, I’d like to believe that collecting and looking into frequent terms offers at least another point of view if not gateway into how a particular academic event is represented/discussed/reported on Twitter. Perhaps it’s my enjoyment of poetry that makes me think that seeing words out of context (or recontextualised) like this can offer some kind of food for thought or creativity.

Interestingly the dataset showed user_lang metadata other than en or en-GB: de, es, fr, it, nl and ru were also present even if in minority. The dataset also showed that some sources are clearly identified as bots.

I am fully aware this would be more interesting and useful if there were opportunities for others to replicate the text analysis through access to the source dataset I used. There are lots of interesting types of analysis that could be run and data to focus on in such a dataset as this. I am simply sharing this post right now as a quick indicative update after the event concluded.