This week on DITA we learnt about distant reading and text analysis and used various online tools to analyse ext.
Distant reading is a form of reading where instead of focusing on an in-depth analysis of one text, many texts are analysed together as a dataset to understand them all. Text analysis as a form of distant reading by analysing large amounts of text for frequency of words appearing, patterns within the text and how often they are used in a particular context. There are various tools that can be used in text analysis and in our lab we tried out just a few to generate text clouds and I did it with an Altmetric report on how often articles about Gender were tweeted in Library and Information Science..
The first one is Wordle which a simple word cloud generator. It gives people the option of changing visuals such as font and colour as well as the number of words used in the cloud. At the most it is only capable of generating a visual of the words
The next one was Many-Eyes, which offers people a few more ways to visualise data besides word clouds including pie charts and graphs. However as much as I wanted to have a word cloud of of this again, it took a long time to get it to visualise one without it crashing. In terms of abilities I find it pretty similar to wordle however with the added choice when it comes to forms of visualisation. It still searches through text by frequency of appearance or alphabetically.
The final one and my personal favourite is Voyant. Voyant not only generates a word cloud but also offers many tools such as editing stop words so you can exclude words that you feel are irrelevant as well as see the number of times each word appears in the text..
Not only that, the user is able to pick and observe specific words. For example if I wanted to know how often science is a subject in the tweets then it can highlight and show where they occurred in the text as well as the context of those words. It could also compare them with different words on a chart and I compared it with the Internet as a way to see how often they appear together and where. Overall it is an effective tool for more detailed text analysis compared to the other two.
This week on DITA we covered altmetrics, which measures the impact of articles and other scolarly documents. There are a few tools available to help understand and observe this impact and the one we used during lab was Almetric, which measures the amount of online attention an article and dataset (with a DOI) gets on social media platforms, literary review, news outlets and reference managers. This does this by using APIs and will track down the number of times it’s been referenced or linked by particular websites.
How Altmetrics work is that a person can view the number of times the blog had been linked in other sites, and would show which sites and readers it had been viewed from.
Altmetric compiles all the information on the attention received and gives a score based on the attention received. Each type of website that links the article is given a different weighting, Facebook being the lowest with 0.25 and news being the highest with a score of 8. Altmetric also will attempt to look into each mention when possible to gauge the importance of the source and how many people may reach it as well as any bias that they may have.
It also shows the demographic of the readers viewing the article, both by geography and by type of reader (member of the public, scientist, science communicators, practitioners). Type of readers is discovered by looking at keywords in their profile description and geographic location is found using geolocators.
From this I understand how altmetrics can benefit people who want to know more about the quality of the article or the reception it receives from the public. Unlike citations which only show which journals cite the article, altmetrics can show a greater view of the impact including page views, downloads and more.
However I feel that Altmetric doesn’t give enough information to determine the quality of the article at times. It doesn’t show whether the attention towards the article is positive or negative nor can it tell us anything about he actual validity of the article. Geolocation can only be used when people allow their geolocation to be known and on twitter that makes up only 1% of the users on it. It also doesn’t show us anything about the quality of the researchers using it and comparing it to older articles is difficult when older articles are more likely to receive attention due to time.
I believe altmetrics is useful in finding out more about the impact of the article however there is still so much that it cannot tell us and there is few tools available to help find out such information at this moment.
All my life I’ve loved all kinds of animals but the creatures that hold the biggest space in my heart are insects.
This may be a bit unusual seeing as most people are quite deterred from them, but I personally find them fascinating and beautiful creatures, small yet complex compared to vertebrates. Which is why when Ernesto mentioned that tweets were like butterflies in this week’s DITA lesson I found myself resonating so strongly with the concept. It opened my eyes to view tweets in a completely different and almost natural way than before.
This week we created an app used to collect and archive tweets using keywords and hashtags, we’ve used TAGS, an application created by Martin Hawsey, Twitter Search API to compile all the tweets including the tag #citylis. The exercise taught me how data could be visualised and I’ve learnt several things such as top tweeters and subjects related to them. It’s interesting to see how data could be generated using apps and I wonder how it could be used to aid further research.
When I read Ernesto’s articles on Twitter being used for public evidence and archiving and storing data sets, I started to understand the importance of twitter data being used in research especially in understand today’s trends. Tweets are already recognised to contain significant information about today’s culture and even the Library of Congress now holds an archive of tweets from 2006-2010. In regards to the ethics behind how to use twitter data, I find that scientists should have the right to use data made available to the public as they would in any public situation where data would be gathered. I think Caitlin M. Rivers’s report on ethical standards when using big data does outline how we should treat datasets while respecting privacy and it works just as most ethical standards for scientific research would.
Tweets are like butterflies in more ways than one: they’re small, numerous and contain huge amounts of data important for study, and unless scientists are able to collect samples you can not be expected to learn more about the subject that’s been studies. It has proven its use in past studies such as the one made by JISC relating to the London riots which I find this a particularly interesting example as it does dismiss original ideas relating to the use of social media using data collected from them. It’s important that we study social network data when understanding today’s culture as it plays such an integral part of people’s lives today that it would be irrational not to take it into account.
To conclude, I’ve decided to rename this blog title to lepidoterans, the taxonomic order of butterflies and moths, to respect this metaphor which joins together two things that I love dearly. I think that if tweets are butterflies than we are the information entomologists that study them!