What’s in a Word? Most Frequent Terms in #WLIC2016 Tweets (part III)

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This is part three. For necessary context please start here (part 1) and here (part 2). The final, fourth part is here.

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It’s Friday already and the sessions from IFLA’s WLIC 2016 have finished. I’d like to finish what I started and complete a roundup of my quick (but in practice not-so-quick) collection and text analysis of a sample of #WLIC2016 Tweets. My intention is to finish this with a fourth and final blog post following this one and to share a dataset on figshare as soon as possible.

As previously I customised the spreadsheet settings to collect only Tweets from accounts with at least one follower and to reflect the Congress’ location and time zone. Before exporting as CSV I did a basic automated deduplication, but I did not do any further data refining (which means that non-relevant or spam Tweets may be included in the dataset).

What follows is a basic quantitative summary of the initial complete sample dataset:

• Total Tweets: 22,540 Tweets (includes RTs)
• First Tweet in complete sample dataset: Sunday 14/08/2016 11:29:03 EDT
• Last Tweet in complete sample dataset: Friday 19/08/2016 04:20:43 EDT
• Number of links:  11,676
• Number of RTs:    13,859
• Number of usernames: 2,811

The Congress had activities between Friday 12 August and Friday 19 August, but sessions between Sunday 14 August and Thursday 18 August. Ideally I would have liked to collect Tweets from the early hours of Sunday 14 August but I started collecting late so the earliest I got to was 11:29:03 EDT. I suppose at least it was before the first panel sessions started. For more context re: timings: see the Congress outline.

I refined the complete dataset to include only the days that featured panel sessions, and I have organised the data in a different sheet per day for individual analysis. I have also created a table detailing the Tweet counts per Congress sessions day. [Later I realised that though I had the metadata for the Columbus Ohio time zone I ended up organising the data into GMT/BST days. There is a 5 hours difference but the collected Tweets per day still roughly correspond to the timings of the conference. Of course many will have participated in the hashtag remotely –not present at the event– and many present will have tweeted not synchronically (‘live’).  I don’t think this makes much of a difference (no pun intended) to the analysis, but it’s something I was aware of and that others may or not want to consider as a limitation.

Tweets collected per day

Total Tweets in refined dataset: 22, 327 Tweets.

(Always bear in mind these figures reflect the Tweets in the collected dataset, it does not mean that as a fact that was the total number of Tweets published with the hashtag during that period. Not only does the settings of my querying affects the results; Twitter’s search API also has limitations and cannot be assumed to always return the same type or number of results).

I am still in the process of analysing the dataset. There are of course multiple types of analyses that one could do with this data but bear in mind that in this case I have only focused on using text analysis to obtain the most frequent terms in the text from the Tweets tagged with #WLIC2016 that I collected.

As before, in this case I am using the Terms tool from Voyant Tools to perform a basic text analysis in order to identify number of total words and unique word forms and most frequent terms per day; in other words, the data from each day became an individual corpus. (The complete refined dataset including all collected days could be analysed as a single corpus as well for comparison). I am gradually exporting and collecting the ‘raw’ output from the Terms tool per day, so that once I have finsihed applying the stop words to each corpus this output can be compared and so that it could be reproduced with other stop word lists if desired.

As before I am useing the English stop word list which I edited previously to include Twitter-specific terms (e.g. t.co, amp, https), as well as dataset-specific terms (e.g. the Congress’ Twitter account, related hashtags etc), but this time what I did differently is that I included all the 2,811 account usernames in the complete dataset so they would be excluded from the most frequent terms. These are the usernames from accounts with Tweets in the dataset, but other usernames (that were mentioned in Tweets’ text but that did not Tweet themselves with the hashtag) were logically not filtered, so whenever easily identifiable I am painstakingly removing them (manually!) from the remaining list. I am sure there most be a more effective way of doing this but I find the combination of ‘distant’ (automated) editing and ‘close’ (manual) editing interesting and fun.

I am using the same edited stop word list for each analysis. In this case I have also manually removed non-English terms (mostly pronouns, articles). Needless to say I did this not because I didn’t think they were relevant (quite the opposite) but because even though they had a presence they were not fairly comparable to the overwhelming majority of English terms (a ranking of most frequent non-English terms would be needed). As I will also have shared the unedited, ‘raw’ top most frequent terms in the dataset, anyone wishing to look into the non-English terms could ideally do so and run their own analyses without my own subjective stop word list and editing getting in the way. I tried to be as systematic as possible but disambiguation would be needed (the Terms tool is case and context insensitive, so a term could have been a proper name, or a username, and to be consistent I should have removed those too. Again, having the raw list would allow others to correct any filtering/curation/stop word mistakes).

I am aware there are way more sophisticaded methods of dealing with this data. Personally, doing this type of simple data collection and text analysis is an exercise and an interrogation of data collection and analysis methods and tools as reflective practices. An hypothesis behind it is that the terms a community or discipline uses (and retweets) do say something about those communities or disciplines, at least for a particular moment in time and a particular place in particular settings. Perhaps it also says things about the medium used to express those terms. When ‘screwing around‘ with texts it may be unavoidable to wonder what there is to it beyond ‘bean-counting’ (what’s in a word? what’s in a frequent term?), and what there is to social media and academic/professional live-tweeting that can or cannot be quantified. Doing this type of work makes me reflect as well about my own limitations, the limits of text analysis tools, the appropriateness of tools, the importance of replication and reproducibility and the need to document and to share what has been documented.

I’m also thinking about documentation and the open sharing of data outputs as messages in bottles, or as it has been said of metadata as ‘letters to the future’. I’m aware that this may also seem like navel-gazing of little interest outside those associated to the event in question. I would say that the role of libraries in society at large is more crucial and central than many outside the library and information sector may think (but that’s a subject for another time). Perhaps one day in the future it might be useful to look back at what we were talking about in 2016 and what words we used to talk about it. (Look, we were worried about that!) Or maybe no one cares and no one will care, or by then it will be possible to retrieve anything anywhere with great degrees of relevance and precision (including critical interpretation). In the meanwhile,  I will keep refining these lists and will share the output as soon as I can.

Next… the results!

The final, fourth part is here.