3 Methods To Create Your Own Searchable Twitter Archive

If you want to secure your tweets against any issues that might happen to Twitter and its servers, consider backing up your Twitter shares. If you use Twitter as a bookmarking tool sharing the most interesting links you come across, you may want to create a searchable archive to be able to quickly find a link you remember sharing a few days or months ago.

Twitter search could be a good solution (there is also a handy from: operator that allows you to find all tweets from any user) but it is only able to work for recently shared links. Twitter search only stores tweets for up to 10 days. If you want to be able to search through older tweets, consider using one (or all) of these tools to create a searchable archive of your tweets or any other Twitter-powered databases.


Why Twitter can be the Next Big Thing in Scientific Collaboration


Imagine yourself in the following situation: You, a scientist pur sang, are busy researching and analyzing A and you are having doubts about the values in the model, suspecting a technical error. Without hesitation, you compose a tweet describing the research and the problem, attach a photo made of the model, add a hashtag (e.g. #science or #labhelp) and send it to Twitter.  A few moments later six people have replied with an answer and your problem is solved.

While this looks like a fairytale, computation in general has become more and more embedded and is starting to play a fundamental role in science in the last couple of years. Scientists have always been among the first to adopt and use new communication technologies into their field of work; technologies such as wiki’s, e-mail and instant messaging (Sonnenwald, 2007).

It can be said that computation in the field of science will become even bigger, due to the new upcoming generation of “social media-scientists”: scientists who grew up in the age of Facebook, Wikipedia and Twitter, and who are used to collaborate and communicate globally online.  Not only do our “future scientists” already generate an enormous amount of data, also referred to as the data tsunami[1], but will only continue to increase this amount, since more forms of collaboration with social media are appearing.

While more data isn´t necessarily always better data, James Surowiecki argues that we are now living in the age of “the wisdom of the crowds”. Here, the larger the group of people collaborating and communicating is, the better the results will be. A classic scenario of one and one is three.

Wisdom of the scientific crowd

There are currently already a few tools that are providing in the creation of collective wisdom, making possible scientific collaborations through (social) media. A first group of examples can be seen in the rise of collaborative analysis tools such as IBM´s Many Eyes and Chartle. Here, people create graphs and charts of big amounts of data and are able to share and discuss them with others. By first choosing a dataset, a user can then choose a visualization, customize it and then publish it to the internet for everyone to see [2].

A second, better known example is Wikipedia: a collaborative digital encyclopedia launched in 2001 by Jimmy Wales. At the moment of writing the English version of the website contains a total of 3.622.868 articles and more than 450 million collaborative edited pages [3]. While the encyclopedia is open and accessible to everyone, the adding and editing of articles isn’t: to do this one needs to have a registered account on the website. Also, studies have been done on the reliability of the entries in Wikipedia, mostly ending up quite positive (Magnus, 2008 & Schiff, 2006). Furthermore, it has also been researched that a majority of college students use the website for course-related research, mostly by providing background information (Lee, 2010). Wikipedia is a general form of encyclopedia, containing articles on many different subjects. How these subjects can be categorized is displayed in figure 1.

Figure 1: Differences in Wikipedia's category structure and the Universal Decimal Classification

The classification in Wikipedia is however a bit different than that of the more taxonomy based Universal Decimal Classification (UDC). In Wikipedia the structure of classification seems to be more wide-spread and focused on Arts & Entertainment. Also, we can see that science (light and dark blue) is practically anywhere in the Wiki-sphere, where in UDC they are more clustered together.

Why Twitter and Why Not

In short: I do believe Twitter can play an important role in the field of science and in scientific collaboration. As argued by Mark Granovetter, who wrote about the effects of social and professional networks on information diffusion, innovation often travels most effectively via weak connections (Grannovetter, 1973). In order for scientific collaboration to happen, information must thus be able to diffuse among people or groups.

I hereby argue that Twitter enables this form of information diffusion. In Twitter, a user has “followers” who aren’t necessarily “friends”. Unlike other social media, followers don’t have to be approved by the one that is being followed (but of course can be blocked). This means that everyone can follow everyone, and can then read everything that is being ‘tweeted’ by the followed user without any real acceptance. In order to find users of interest, Twitter has implemented a powerful search mechanism that makes it easy to search for a specific topic and see all tweets on that topic. If someone likes what another user has said, he or she can choose to follow the user and read all future tweets. Retweeting a tweet then spreads the message across their own networks, creating a cascade effect of information and showing that the diffusion of information indeed travels via weak ties.

A scientist can thus reach a huge amount of people, when for example he/she is in need of help, by simply composing a tweet and sending it on Twitter.

There are currently already networks of scientists on Twitter, as for example can be seen in figure 2.

Figure 2

An interactive version of this can be seen on Many Eyes. The figure is generated from a list of Scientific Twitter Friends of David Bradley and is visualized by 2020 Science. The bubble-chart shows how influential a Scientific Twitter User in the list might be and that Twitter can indeed be used for the connection (and collaboration) between scientists.

While this does sound like a perfect tool for scientific collaboration, there are some downsides in using social media and Twitter according to a survey held under 300 lab managers[4]. They explain that the reasons for not adopting social media are:

  1. Blurring of the boundaries between private and business use
  2. Loss of productivity
  3. Security: the danger of confidential information being leaked

These all make a point of course, but these ‘downsides’ can all be avoided when carefully using these social media and by paying attention on what information is being spread.


[1] http://thesocialcustomer.com/peterauditore1/37255/social-media-data-tsunami, accessed on 27 april 2011

[2] http://www-958.ibm.com/software/data/cognos/manyeyes/page/create_visualization.html, accessed on 28 april 2011

[3] http://en.wikipedia.org/wiki/Special:Statistics, accessed on 27 april 2011

[4] These results were published in Lab Manager Magazine and can be found online at http://www.labmanager.com/?articles.view/articleNo/4496/


Granovetter, M. S. (1973), “The Strenght of Weak Ties”, American Journal of Sociology, Vol. 78, No. 6 (May 1973), 1360-1380.

Sonnenwald, D. H. (2007), “Scientific Collaboration: A Synthesis of Challenges and Strategies”, Annual Review of Information Science and Technology, Vol. 4, Blaise Cronin, (Ed,), Medford, NJ: Information Today.

Surowiecki, J. (2004), “The Wisdom of Crowds: Why the Many Are Smarter Than the Few and How Collective Wisdom Shapes Business, Economies, Societies and Nations”, Little Brown

Auditore, P. (2011), “The Social Media Data Tsunami”, The Social Customer, 27 April, accessed on 27 April 2011, http://thesocialcustomer.com/peterauditore1/37255/social-media-data-tsunami

Magnus, P.D. (2008), “Early response to false claims in Wikipedia,” First Monday, 13(9), September, accessed on 27 April 2011.

Schiff, S. (2006). “Know it all. Can Wikipedia conquer expertise?” The New Yorker. 31 July, accessed on 28 April 2011. http://www.newyorker.com/archive/2006/07/31/060731fa_fact/.

Lee, A. (2010), “Science students more likely to use Wikipedia”, The Daily Princetonian, 23 March, accessed on 28 april 2011. http://www.dailyprincetonian.com/2010/03/23/25575/

12 Useful Facebook WordPress Plugins For Bloggers

“Facebook is one of the most popular social networking websites. There are a lot of information exchanges happen daily through Facebook. So, some serious bloggers start paying their attention on Facebook and threat it as a medium to promote and share stuffs with their friends.

If you are a WordPress user and looking for WordPress plugin to connect, integrate or share stuffs with Facebook, then you had came to the right place. Today, we are going to look into 12 useful WordPress Plugins for you to work with your Facebook account.”


bibcamp 2011, impressions twittesques de l’atelier « Construire sa bibliothèque numérique  « «La bibliothèque apprivoisée

“Je me suis plus particulièrement intéressé à l’atelier « Construire sa bibliothèque numérique ». Je vous propose ici une synthèse de celui-ci à partir de ce qui s’est dit sur twitter. Nous ne pouvons pas parler de compte rendu d’atelier, mais d’impressions twittesques. J’ai en fait compilé et organisé ce que les auteurs des tweets ont jugé intéressant à partager. Une synthèse totalement tributaire de cette subjectivité que j’ai néanmoins tenté d’atténuer en confrontant les tweets entre eux. Très vite je me suis aperçu que les même remarques émergées … comme si finalement un consensus s’affirmait spontanément de ces conversations. Les réseaux sociaux déstructuraient-il le débat ?”


Digital Humanities Now

Digital Humanities Now est un service en ligne qui édite en temps réel les messages envoyés sur Twitter par des membres de la communauté des digital humanities. Il a pour but de rendre compte de l’actualité (annonces de projets, articles, outils…) sur laquelle pointent les messages.

Développé par le Center for History and New Media (CHNM) de l’université George Mason (Virginie), il agrège de façon entièrement automatique les tweets provenant d’une liste de plus de 350 chercheurs et autres professionnels en digital humanities et domaines connexes. C’est Dan Cohen, créateur de Digital Humanities Now, qui a établit cette liste et a eu ensuite l’idée de l’exploiter en utilisant Twitter Times pour diffuser en ligne l’ensemble des tweets.

par Elisabeth Caillon, veille internationale du TGE Adonis

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