If you go down to the library today, you’re in for a big surprise

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Aby Warburg, British Library, British Library Labs, data manipulation, data mining, datasets, disruptive innovation, Flickr, Fun Palaces, information society, James Baker, LAPIS, libraries in society, library OPACs, Matt Finch, public libraries, social media, text analysis, Tumblr, Twitter, user experience, Warburg Institute

The ninth and penultimate LAPIS lecture of term featured guest lectures by Matt Finch, a writer and content producer who has developed immersive play experiences for libraries, and James Baker, a curator of digital research at the British Library. These lectures both centred on the challenges facing libraries—in particular public libraries—today that result from the myriad changes in publishing that we have discussed in previous lectures. In short, the transition to a digitised, automated model of publishing content that can be accessed from (almost) anywhere via the Internet threatens to make the traditional physical library irrelevant, as potential users can simply find the information they seek online from within the comfort of their own homes.

Dr Finch approached this problem from the perspective of re-inventing the public library as a community hub, which encourages users of all ages to explore and learn on their own terms. Examples of this type of activity in which he has been involved include using “comic-book dice” to facilitate storytelling games for children, and staging live-action events such as a staged zombie invasion of the Tullamore Public Library in Australia. At first glance, these projects may appear somewhat frivolous, especially when run in collaboration with initiatives with names such as Fun Palaces, but they achieve measurable positive results in terms of both publicity for the public library service as a whole, and a sustained increase in user-based metrics, without any increase in expenditure. These activities also rely upon the library as a physical entity, thus providing justification for its continuing existence.

Nevertheless, the general public can be hard to convince: for instance, the comments on an article on the opening of a new central public library in Christchurch, New Zealand reveal a sharp divide between those who agree with the ideal of the library as a multi-purpose and multimedia community facility, and those who believe that anything beyond a repository of books and some reading rooms for silent study is a waste of public money. Yet more traditional areas of LIS research also support the first view: for instance, much work has been carried out into the effects of serendipity on information retrieval (or, in everyday terms, how browsing for information can produce interesting and unexpected results deriving from the layout of physical books, whether in a library or bookshop—how often have you gone looking for a particular book, only to emerge with another that happened to catch your eye?), how some libraries such as that attached to the Warburg Institute has tried to maximise this effect with an extremely idiosyncratic in-house classification scheme, and how it is difficult to create the same atmosphere using an online search engine or library OPAC. Furthermore, the role of libraries as valued community centres has been well-documented during social crises, such as in Ferguson last year following the police shooting of Michael Brown, and in the already-mentioned Christchurch following the devastating earthquakes in 2011. The recognised importance of the public library in society is officially enshrined in the UNESCO Public Library Manifesto.

Dr Baker’s lecture focussed on the changing nature of library collections and services in the digital age, and covered some of the same areas that we explored last term in the DITA module, such as text analysis and data mining. He also emphasised the British Library’s role in(and legal responsibility for, under recently-revised British legal deposit law) collecting webpages for the UK Web Archive, and the importance of curating and sharing datasets, such as this one relating to the British Library’s Flickr stream. Yet these responsibilities can also manifest themselves in fun and creative ways as the British Library Labs work on projects to maximise the accessibility of the institution’s digital collections: examples include the Mechanical Curator, an automated Twitter and Tumblr account that sequentially posts images from a digitised corpus of texts, PicaGuess, a crowdsourcing app designed to create metadata for similar images by identifying their defining characteristics, and even an art installation by David Normal at the Burning Man Festival in 2014. These developments all help to foster community engagement with the British Library (which, let us not forget, is also a public library—just a particularly large one) and justify its place in society.

Thus, although the changing face of publishing and technology has challenged the traditional role of the public library, measures undertaken to facilitate community engagement, whether through innovative events, the promotion of resources through social media, and the training of librarians in such skills as how to handle large datasets, are keeping them relevant. The tools and technology used to seek information, and aspects of the information itself, may have changed, but the average user will still be appreciative of a professional to help guide them through the information-seeking process—and if this process can take place in a pleasant, creative environment in the heart of the local community, then that is even better!

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Altmetrics in academia

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Altmetric, altmetrics, application programming interfaces, databases, digital divide, DITA, Ernesto Priego, information architecture, information society, JSON, markup languages, reference managers, social media

In the world of academia, the “impact” of scholarly articles has traditionally been measured using citation counts—how often an article is cited in a subsequent article. However, the development of the Internet has challenged this model: the immediacy of social media has allowed for greater and quicker collaboration between academics that exposes the much longer timeframe needed for new articles to be published and their citations to be counted and linked back to the original sources; and the growth of digital repositories has enabled the dissemination of articles outside of the traditional media of academic journals, and to a more diverse, potentially non-academic audience as well.

In recent years, therefore, altmetrics (alternative metrics) have come to the forefront of measuring impact. They are not intended as a replacement for counting citations in order to measure academic impact, but instead as a complementary technique to quantify societal impact. In practice, these new metrics cover elements such as the number of views that an article published online receives; the number of times that it is downloaded; and the number of times that it is “shared”. The latter category includes mentions in news media, social media, blogs, and uses in reference managers such as Mendeley and CiteULike.

There are several providers of altmetrics that have appeared in the last few years: the one that we used in our most recent DITA class is called—appropriately enough—Altmetric (founded in 2011, it now handles approximately 2.5 million requests per day). The way in which it works combines many of the areas that we have learnt about already (and several of which I have previously posted about in this blog): Altmetric maintains a relational database of both online academic publishers—to track the appearance of new articles–and news websites–to track mentions of these articles in the media; it then uses a mashup of Application Programming Interfaces (APIs) from social media platforms, such as Facebook and Twitter, and the aforementioned reference managers, to harvest their data outputs in the JavaScript Object Notation (JSON) markup language from which it produces its own JSON output. This can then be exported to a programme like Microsoft Excel as a comma separated values (csv) file for further analysis and manipulation.

The purpose of Altmetric is that it gives each article that it encounters a score, denoting its impact, according to how often it is shared in news and social media platforms, and saved in reference manager software. The score is not simply a cumulative total, but is instead calculated using an algorithm (that gives different sources different values according to how important a panel of experts believes that source to be in communicating academic information, and also rewards a wide breadth of sources: for instance, an article shared several thousand times on Facebook will be likely to have a lower score than another that is has only a few hundred mentions, but across several social media platforms and in traditional news media websites). As a quick visual aid to indicate the diversity of an article’s societal impact, Altmetric uses a modified version of its rainbow-coloured logo for each article: the example below shows an article with a variety of sources, and therefore colours, (top) compared to one which relies solely on one source, Facebook, for its impact (bottom).

(The first article has a total of 245 mentions; the second has 2546, but receives a lower score due to Altmetric’s algorithm.)

So how useful are altmetrics? The success of Altmetric and its competitors in the industry certainly indicates their popularity: not only can the scores produced be used by academics to research their field (and LIS students!), but also by the authors of articles to demonstrate their credentials, by universities to demonstrate their faculties’ credentials, and by publishers to demonstrate their writers’ credentials. Altmetric not only provides its scores in a searchable browser-based application, but also sells its API to institutions: for example, City University London’s digital repository has an Altmetric score for each of its deposited items, in addition to buttons for online sharing and its own altmetric (number of downloads).

However, there are several potential problems. Most obviously, altmetrics do not take into account the qualitative value of an article, merely its quantitative impact. As Altmetric itself is at pains to point out, with a large red box in its “How is the Altmetric score calculated?” help-page:

The Altmetric score is useful to rank articles based on attention – it can’t tell you anything about the quality of the article itself, though reading the linked discussions might.

A good example of these limitations is the fact that the current holder of the record for the highest Altmetric score of all time is a humorous article about psychiatry in the world of Winnie-the-Pooh: not of much value for research, but likely to attract a great deal of superficial attention due to its offbeat subject matter. To further demonstrate the point, when doing my own searching, I found another frivolous yet popular article on the disappearance of teaspoons from an Australian research institute. It is therefore vital to take the data provided by altmetrics with a grain of salt; to place it in a wider context and to use it in conjunction with the traditional citation-counting.

Furthermore, Altmetric is limited by the number of potential sources that it can monitor effectively in its database. It also limits itself to articles that have a digital object identifier (DOI) or similar record, which excludes those journals that opt not to use one. There is also the question of subject bias: in the searches that I have performed using the service, I have noticed a distinct bias towards the sciences, particularly medicine, at the expense of the humanities. For instance, of the current top ten-scored articles listed under the subject “Library and Information Studies”, five (including the top two) are concerned primarily with medicine and healthcare with no obvious connection to LIS.

Finally, our own module leader, Ernesto Priego, has written on the subject of how altmetrics may be influenced by digital opportunity: in a world where Internet use is still dominated by first-world countries, with a clear correlation to their performance in the Digital Opportunity Index, does using altmetrics reinforce a Western-centric view of academia by paying the greatest attention to those who already possess the greatest means of making themselves heard?

It seems clear to me that, although altmetrics have proven themselves valuable in keeping the world of academia apace with the wider societal and informational developments stimulated by the growth of the Internet, they are not a panacea: they should be used in conjunction with older techniques, and further research is necessary into new methods that have yet to be determined. However, as altmetrics—a very young technology, don’t forget–continue to develop and become further integrated into the established paradigm, then hopefully some of the problems I have mentioned should become less severe in any case.

Mapping the Twitterverse

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Andy Baio, application programming interfaces, Big Data, data manipulation, data visualisation, datasets, Dhiraj Murthy, DITA, Google Sheets, information architecture, JSON, markup languages, Martin Hawksey, metadata, Raffi Krikorian, social media, TAGS, Twitter

In an information era defined by an exponential growth in data output, fuelled by the connective and interactive technology possibilities provided by the Internet, there is perhaps no better exemplar of these phenomena than Twitter. Twitter is a social media website that allows its users to send short (140 characters or fewer) messages (“tweets”) that can be viewed instantly be their followers, or by other users searching for particular words or phrases. The scale of the enterprise is vast: Twitter estimates that it has 284 million active montly users, and that 500 million tweets are sent by these users every day. Despite the brevity of each message, each also contains a great deal of associated metadata, as shown by this “Map of a Tweet” (in the markup language JSON) produced by Twitter developer Raffi Krikorian.

Four more years. pic.twitter.com/bAJE6Vom

— Barack Obama (@BarackObama) November 7, 2012

(The most re-tweeted tweet of 2012.)

Twitter’s importance in contemporary culture can be seen in its use by political leaders the world over (a verified list includes a total of 66). The role of Twitter, and other forms of social media, in influencing world events such as the Arab Spring has also been well discussed and documented, and its sociological implications are being studied by academics such as Dhiraj Murthy. This makes it a valuable tool for scholarly research, but there is a key problem that must be overcome: Twitter’s main strengths as a communications medium—its timeliness, conciseness and immediacy—and its popularity mean that older Tweets are continually buried beneath successive waves of newer ones. The site’s own search function is notoriously limited (only extending back for one week, for example), and despite recent improvements, is still not capable of producing useful data. For instance, searching for mentions of an extremely popular hashtag (a Twitter tool used to denote key words or phrases, e.g. #citylis, City University London’s Library and Information Science course) will not return all of the possible results due to bandwidth limitations imposed on individual user queries.

However, using the technological possibilities opened up by APIs (as first mentioned in a previous post). A user can create their own Twitter app to gain access to the Twitter platform (under strict conditions), and, using the ingenious TAGS tool, developed by Martin Hawksey, which in turn uses the APIs of Twitter and Google Sheets (an online spreadsheet programme similar to Microsoft Excel) to automatically export the metadata—encoded in JSON—of selected Tweets (usually filtered by hashtag) into a database. One of my classmates, Daniel van Strien, has written more fully on the technicalities of how this is done, and I recommend that you read his blog to find out more.

These API processes result in a Google Sheets document which consists of four spreadsheets:

1. A “Readme/Settings” sheet that the user manipulates to set the search parameters, and including links to data visualisation tools (more on which later).
2. An “Archive” sheet, consisting of the tabulated metadata of each tweet retrieved.
3. A “Summary” sheet, listing users by number of Tweets about the desired hashtag, word or phrase, and also including other basic information derived from the Archive.
4. A “Dashboard” sheet, consisting of the Archive data presented in graph and chart form.

My TAGS Google Sheets document, which is used to search for the #citylis hashtag, can be viewed here.

Perhaps the most compelling results of TAGS, however, is the creation of tools to further manipulate and visualise the raw metadata. TAGS Archive creates something that looks, superficially at least, like a standard Twitter feed, but is in fact a fully-preserved archive of Tweets on the given search term that can also be filtered by screen name or tweet content, or searched for chronologically, far more effectively than by using Twitter’s own search interface. TAGS Explorer is even more visually exciting, as it creates a map of interactions between all of the different users who have used the search term.

This is particularly useful as it clearly demonstrates Twitter activity—within the chosen parameters—over time. Another of my classmates, Shermaine Waugh, tweeted this image last Monday (October 27), showing a map of tweet replies between users within #citylis:

Citylis' Top Tweeters! #citylis #woah pic.twitter.com/P90qguxiNQ

— Shermaine Waugh (@ok_shermaine) October 27, 2014

The equivalent image from today (November 2), indicates that new links have appeared and that existing ones have been strengthened, after a further six days of Twitter activity (another advantage of using TAGS is that all tweets from the moment of the automated programme being set up are retained, not just those sent seven days prior to the most recent export of data being carried out):

TAGS Explorer also allows the user to create more complex maps by including mentions (when a Twitter user mentions another user’s screen name in one of their Tweets):

An even more intricate map can be included by also including retweets (when a user republishes another user’s tweet without modification):

Finally, every node in the network can be clicked on to view each individual Twitter user’s interactions with the wider group (this can even be animated!), in this case myself:

These examples have all been rather self-indulgent; only of use to someone encountering these tools for the first time (or perhaps a future historian researching how Twitter was integrated into LIS university courses in the early 21st century? I can dream!), but these visualisation principles can also be applied to areas of more value for scholarly research, or indeed public interest. For example, I mentioned the Arab Spring earlier, and this video shows the activity, in real-time, concerning the #jan25 hashtag at the moment of former President Hosni Mubarak’s resignation:

Another example concerns Twitter itself, specifically its use in the current “Gamergate” controversy. American technologist and blogger, Andy Baio, used a set of 316,669 tweets and their associated metadata to produce a series of data visualisations in order to demonstrate a series of illuminating conclusions.

It is clear that Twitter offers a great variety of research possibilities and I am very much looking forward to continuing on this theme in the future lectures and lab sessions that comprise this module.