Visualization Design: How to Design and Address Data Visualizations

Visualization Design How to design and address data visualizations

2011 This assignment was written for school purpose. University of Utrecht // Faculty of Humanities Degree/program // MA New Media & Digital Culture Course // Get Real! Student Salko Joost Kattenberg // 3614875 Paper // Visualization Design: How to design and address data visualizations. Supervisor Ann-Sophie Lehman // www.ann-sophielehmann.nl

“Complexity is a perceived quality that comes from the difficulty in understanding or describing many layers of inter-related parts” (Benjamin Jotham Fry, 2000)

By Tomasz Malisiewicz

Abstract In this paper I will write a practical approach about the making and use of data visualizations. How should we use or address data visualizations? And what should therefore be key factors in the process of making data visualizations? To do this, I critically look into the process of making and understanding data visualizations. Using cases and scientific analyses I will pinpoint important aspects of data visualizations that should be taken into account when making data visualizations. Keywords: visualization, data, design, dynamic, making

Introduction Over the last ten years we have seen an increasingly amount of data visualizations: from infographics explaining news topics in the daily newspaper to Facebook applications visualizing your social network. But the increasing amount of data visualizations is not the greatest change, while looking at the development over the past ten years. Data visualizations have transformed from plain statistic graphs and images to interactive mind blowing graphical designs. Doing so, some data visualizations are opening up new aesthetics and are venturing into hybrid appearances between data visualizations and art. Besides their appearance, they are also changing in complexity. High-tech computers and algorithms are visualizing huge datasets that are consisting over more than millions of entries. The increase of data in the digital 21st century comes with new technology and techniques. Yet this is not a mere increase of data but also a change in the sort of data. “The emergence of social media in the middle of 2000s created opportunities to study social and cultural processes and dynamics in new ways.” (Lev Manovich, 2011. pp.2). The fact that we now use these new social and cultural data also change the way we make new data visualizations. With the change in aesthetics, technology and content scientists find themselves questioning and hoping to understand these new and sometimes hybrid data visualizations. Not only science should start to reanalyze and critically look at today’s data visualizations. While analyzing data visualization one cannot forget the involvement and choices of the maker. Makers of data visualizations are designers, programmers, scientists and perhaps even artists, and most of them do not have a scientific approach to data visualizations. I myself -being a junior graphic designer and programmer- can see that in this area of expertise there is much room for improvement. Makers of data visualizations do not address or make data visualization from a scientific perspective; they just design or program it. In this paper I will confront these two perceptions of data visualizations and come up with important aspects that should be taken into account when making today’s data visualizations. I believe that in the future this will become more and more important, looking at the rise of data and the use of data visualizations. Next to that, there is an increasing amount of open software and tools like Many Eyes, Tableau, Gephi and Processing, making data visualizations available for a much wider public. Educating them to better address and make better data visualizations will perhaps prove to be a challenge in the future.

What are data visualizations? Recently I have given a presentation about data visualization. The event was hosted in a movie theatre with the main question being: are data visualizations just proposing new graphical styles/aesthetics without the graphical designs being functional for providing us information/data. Infographics designer Chad Hagen beautifully shows 1

this in his work . By creating beautiful images that look like data visualization, but in fact do not represent any data. In my research for this presentation I began to wonder about this question. What was the purpose of the artists who were making data visualizations as art? For them there was no purpose of finding new insights in the data analyses or showing new data and information to their viewers. For them data visualizations are just an artistic expression. In my opinion these are no real data visualizations anymore. I needed to state a clear definition of the term data visualizations. Lev Manovich writes in his paper What is Visualization? the following definition of information visualization: “Lets define information visualization as a mapping between discrete data and a visual representation” (Lev Manovich, 2

2010. pp.2). But as for instance DNA11 shows that something can represent discrete data (in the form of human DNA) but in fact can be nothing more than art. So I needed to come up with another definition on how data visualizations can be defined, a definition that would exclude the artistic expressions that just wanted to make art instead of visualizing information or data. I was inspired how to better formulate my definition by a presentation of Bernhard Reider (Reider, 2011). In his presentation he talked about how researchers can and are using data visualizations, and showed examples how he is currently using data visualizations in his own research. During the presentation he stated that data visualizations used in scientific research cannot always be seen as standalone images. Data visualizations in scientific research are used as part of the research method and do not have the purpose of presenting data and information to a layman. Hearing this notion of ‘purpose’, I came up with the idea to include the purpose of the makers of the data visualizations into the definition, thereby showing why a data visualization was made. Introducing purpose into my definition leads me to use the following definition in this paper: a data visualization can be defined as a visual representation of data with the purpose of presenting that data. With this more narrow definition of data visualization we can exclude the artists that make data visualizations as art, because in most cases art does not have the purpose to present discrete data. Although I will use this definition in my paper, I will mostly use data visualizations that have been programmed. Programmed data visualizations allow for larger datasets and therefore are more complex in the process of making and understanding. This does not mean that I won’t use static data visualizations like infographics in this paper, but I will use them less because they could lack the involvement with digital technology or programming.

We feel fine Throughout this paper I will use one main case that I will reflect upon from different angles. This case is the data 3

visualization We Feel Fine , launched in 2006 by Jonathan Harris and Sepandar D. Kamvar. We Feel Fine is a data visualization “that aims to collect the world’s emotions to help people better understand themselves and others” (Harris & Kamvar, 2011. pp.1). The dynamic data visualization scrapes sentences from LiveJournal, MSN Spaces, MySpace, Blogger, Flickr, Technorati, Feedster, Ice Rocket, and Google with the occurrences of the phrases “I feel” and “I am feeling” every 5 minutes. “The result is a database of several million feelings, increasing by 10,000 -15,000 new feelings per day.”(Harris & Kamvar, 2011. pp.1). The data visualization is a real looker and

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became a hit. Using no static but mostly dynamic data visualization techniques, We Feel Fine has been used as an interactive installation and has been exhibited in museums all over the world. We Feel Fine is also partially 3

available online . Over the course from 2006 until 2011 We Feel Tine has been used by 8,5 million visitors (Harris & Kamvar, 2011. pp.1). For the purpose to better understand the main case that I am using in this paper, I will advise you to try out the We Feel Fine data visualization on www.wefeelfine.org. I will repeatedly come back to We Feel Fine in this paper, but for now let’s situate it a bit more. Looking at my definition stated earlier we can say We Feel Fine is a data visualization. It has a clear purpose to show the data that has been gathered and uses visuals for presenting the gathered data. But on the other hand, we can say that We Feel Fine has been used by museums as an artistic expressions or art, and therefore has for instance been exhibited in the National Museum of Contemporary Art in Athens (Harris & Kamvar, 2011. pp.1). We could say that We Feel Fine is right on the edge between functional data visualizations and art. The data used in We Feel Fine belongs to the relatively new data, coming from digital social and cultural processes available on the internet. Because We Feel Fine uses new social data and is leaning towards art we can situate it at the front of today’s new and interactive data visualizations. This is also the reason why I chose this data visualization to be the main case in this paper.

The process of making The process of making data visualizations is a complex process. Unique algorithms work to gather and represent data. The programmed algorithms works hand in hand with complex graphical designs and together create unpredictable outcomes. But the process of making starts not with programming or designing the data visualization. To get a clear understanding of the process of making data visualizations, I have divided the process of making into three different stages. All of these stages are important to understand and give new insight in the way data visualizations are constituted and given meaning to. Gathering of data The makers of data visualizations always choose their dataset and therefore confine their data visualization within that chosen frame of data. So a dataset can always be seen as the initial framework where data visualizations rest upon. The dataset determines the outcome of data visualizations as much as the choices in the design of data visualizations. Even in static data visualizations this is the case because the maker will still use no other data than the one within the used dataset. This means that choosing your dataset is the first important step when making data visualizations and can have huge impact on the outcome of the data visualization. Christine Paul writes about the use of databases and storage of data in her paper The Database as System and

Cultural Form. She writes that even the way we store data and design data models already imply some kind of narrative, patterns or structures within the data. This would mean that the choices we make in structuring and storing data in databases already in some way form the outcome of the data visualization. “The understanding of a database as the underlying principle and structure of any new media object delineates a broad field that includes anything from a network such as the Internet (as one gigantic database) to a particular data set” (Paul, 2007. pp.5). You might say that basis for structure and pattern analysis in data visualizations are already made by designing databases. Another example is shown by Duncan Watts et al. in the paper Identity and Search in Social

Networks. They discuss the underlying data structures of social media. With these analyses they reveal that there are already patterns within social media that afford us to view and gather data is some way. Looking at these patterns they address the relationship with for instance non digital social structures and find interesting ways to make algorithms to correctly gather data from social media. Manovich writes about a change in the current use of datasets, and addresses this in his article Trending: The

promises and the challenges of Big Social Data. He states that for the first time we can use huge quantities of data. While before we could only use data from a few (deep data) to explain the overall state, we can now use

large quantities of data (surface data). While some ethnographers might say that large quantities are very shallow and can’t explain more about cultural or social studies than we could with deep data or perhaps even less, Manovich disagrees. Manovich writes that he sees a scenario in which both sorts of data are used differently. “I believe that in our hypothetical scenario, ethnographers and computer scientists have access to different kinds of data.” (Manovich, 2011. pp. 7) He sees a new form, a way of analyzing great quantities but still dig deep enough into different samples to explain the overall state, effectively using both methods of deep and surface data at once. You can easily see from Manovich perspective that the way we gather data is extremely important for the outcome of our research or data visualization. Considering the right amount of data and also the right way to address different samples of data, you can determine the quality and impact of your data visualization. This becomes even more important when not gathering the data yourself but writing algorithms to gather data for you. Here the margins for errors or misinterpretation are even greater and can ruin or make your entire research or visualization. In the case of We Feel Fine we can see that they chose to very strictly gather data. They used algorithms to gather sentences form specific sites with the occurrence of “I feel” or “I am feeling”. But this was not the only thing; We Feel Fine also tries to collect some personal information about the writer of the sentence: age, location, gender and time. What does this imply for the quality and outcome of the data visualization? How are these different data used and collected? This will be discussed in the chapter on Science versus practice. Programming/designing of data visualizations The second stage of the process of making and giving meaning to data visualizations is the actual programming or designing of data visualizations. This stage is also a vital part of the making process of data visualization, in the way that this stage will determine how the data visualization will be presented to the viewer/user. Here we see that common issues of style, form, color and other basis design principles are important for the understanding and clarity of data visualizations. Monovich highlights two district aspects of this process in his paper What is Visualization?. He sees reduction and spatiality as two of the main factors in data visualizations. Reduction is the process of using samples in reduced forms in order to clarify of represent complex structures. “Infovis uses graphical primitives such as points, strait lines, curves, and simple geometric shapes to stand in for objects and relations between them - regardless of whether these are people, their social relations, stock prices, income of nations, unemployment statistics, or anything else” (Manovich, 2010. pp. 5). For Monovich reduction is an element of design that is very important for our understanding and perception of data visualizations. And I believe he is right, by choosing different forms or shapes we are designing and situating taxonomies into our data visualization or information design. Therefore we make choices in the use of reduction, what is important and what is less important for instance. By adding reduction to data we thus control the interpretation of our viewer/user. For spatiality this is roughly the same: “They all use spatial variables (position, size, shape, and more recently curvature of lines and movement) to represent key differences in the data and reveal most important patterns and relations” (Manovich, 2010. pp. 7). While spatiality also implies some forms of taxonomies, spatiality is more often used for the visualization of the relationship between different data. A bunch of tight circles might mean a close relation of reductive elements while a very scattered number of circles might be more perceived as an open or distanced relationship. Reduction and spatiality is not always used to analyze data. Jeremy Douglass is a researcher working with Manovich who gatherers and analyzes gameplay data. Here he uses techniques like motion tracking and RGB level analysis and newly developed technique called eigenmodes to visually analyze the gameplay data. “The full range of uses for simple computational approaches to gameplay recording has barely been considered, but the potential for new kinds of artistic representations and analytic insights about games is huge.”(Douglass,2009. pp.3) This stage of making data visualizations has changed a lot with the introduction of interactive and digital technology. The introduction of interactive and even dynamic ways to design and create data visualizations opens

up new ways of designing and programming data visualizations. These new/extra attributes of data visualizations ought to be addressed in the same careful way reduction and spatiality should be used. An insight into the use of interaction and dynamic data visualizations is given in the chapter on Interactive/dynamic data visualizations. Interpretation of the viewer/user The last stage of the process of making and giving meaning to data visualizations is the interpretation of the viewer/user. It is always true, just like any other medium, that you can never fully predict the interpretations of users. While this is hard to see as a stand along stage of the process of making, I believe otherwise. This is mainly why I not merely use the word viewer, but include the word user when talking about the receiver of data visualizations. Today’s data visualizations just like We Feel Fine, facilitates the user with the means to dynamically change and alter the data used in data visualizations. This was not possible with the traditional static data visualizations. This transformation causes for a huge change in the way the viewer can interpret the data or the visualization. Interpretation has become an interactive process that has become more dependent on the choices of interaction by the makers and users of data visualizations. Making good interaction designs for data visualization is therefore becoming more and more important because they shape the way we interpret and give meaning to data visualizations. Jasper Schelling who wrote his thesis on data visualization writes: “Since the interpretation of the visualized data is key, user (like in interaction design and usability) tend to be involved from the beginning, to insure that a visualization has its intended result.” (Schelling, 2007. pp. 28). So he already talks about the way interaction design is being used with the practice of making data visualization, and in fact is already imbedded within the process of making. To go a step further we can take Manovich concept of direct visualization. What Manovich described is an interaction design that skips the second stage of the process of making data visualizations. By interacting with the data visualization it is possible to directly show the source of the (gathered) data, and therefore eliminates part of the second stage in the process of making. Interaction design also makes it possible to address only a portion or sample of the complete dataset. These processes help to understand and discover patterns in data visualizations. “Displaying the actual visual media as opposed to representing it by graphical primitives helps the researcher to understand meaning and/or cause behind the pattern she may observe, as well as discover additional patterns” (Manovich, 2010. pp. 23).

The process of understanding Besides the process of making there is also the process of analyzing how we interpret or understand data visualizations. In the definition of data visualization I talk about the purpose of the data visualization. To be able to make good data visualizations you need to be aware how data visualizations fulfill their purpose. What are the key factors in which we receive data or information from data visualizations. Why do we feel the need to visualize data? Benjamin Jotham Fry writes about this, and looks how humans interpret data visualizations: “Because of the accuracy and speed with which the human visual system works, graphic representations make it possible for large amounts of information to be displayed in a small space” (Fry, 2000. pp. 14). It seems that we are very fast at seeing complex graphics. Older data was represented by long lists of data or endless arrays of numbers. With a more textual/non-visual representation of data we can’t make out differences or relations, we need visual tools and systems to analyze and reproduce data in a visual way to better analyze them. Manovich talks about seeing and discovering patterns and relations in data visualizations. “In our experience, practically every time we analyze and then visualize a new image video collection, or even a single time-based media artifact (a music video, a feature film, a video recording of a game play), we find some surprising new

patterns.” (Manovich, 2011. pp. 8). So these relations and patterns can be seen as new insights, new information gathered by the clever use of visualizing data. During my research and work with data visualizations I always use the sentence: Making the invisible visible. For me a good data visualization is one that has the potential to reveal coherence, relations, patterns and comparison between different samples of data, thereby revealing/allowing new insight to be formed or analyzed. In that way you can even say that old bar charts for instance are bad data visualizations, because they hardly reveal or allow any other analysis than the data that is behind the bar chart. For me the user is an important aspect here, and should be allowed to actively interpret/analyze the data. Japser Schelling seems to agree with me and writes about data visualizations: “They derive their strength from the fact that they let people use their eyes and minds to draw their own conclusions rather than explicitly state a fact.” (Schelling, 2007 pp 27) Looking at We Feel Fine we can see that they tried to attract and afford active user participation: making different ways to visually represent, analyze and sample data. This lets to users create a part of their own analysis and thereby stimulate the process of analyzing data for themselves. “data visualization has been defined as a tool to amplify cognition” (Harris & Kamvar, 2011. pp.8)

Interactive/dynamic visualization I already addressed interaction and dynamic data visualizations a couple of times in this paper. I think this is one of the most interesting new aspects of data visualizations and needs some further critical analysis. We have seen that interaction design is integrated is as well the making as the understanding of data visualizations. Next to the fact that interaction is important to the makers and users of data visualization, interaction also is important for the medium/technology on which the data visualization is displayed. Interaction is becoming a very important aspect of data visualizations and is entwined with every process surrounding data visualizations. Benjamin Jotham Fry, but also Jasper Schelling, write about data visualizations that with the use of motion and interactive techniques comes a whole new array of possibilities to represent, analyze and design data. In the thesis of Fry, Organic Information Design, he tries to answer questions like: “How can a continually changing structure be represented?” (Fry, 2000 pp. 13). In his thesis Fry constructs his concept of organic information visualization: “An Organic Information Visualization provides a means for viewers to engage in an active deconstruction of a data set” (Fry, 2000 pp. 16). Fry searches for ways in which complex and interactive data can be best represented. Looking at behaviors of organic systems/organisms like structure, appearance, metabolism, growth, homeostasis, responsiveness, adaptation, movement and reproduction Fry hopes to find new and innovation ways to represent complex data. “These behavioral rules map meanings determined by the designer, such as ‘importance’, to a property like appearance” (Fry, 2000 pp. 43). You can note that with the rise of interactive media these new (organic) representations have become available, creating new data visualizations. Again interactivity is a changing factor in all three stages of making and also changes the way we are able to understand data visualizations. Although interaction is a big changing factor, modern technology and database structures let us change/sample datasets and perhaps even work with instant data gathering. Dynamic aspects of data visualizations are able to transform and adjust appearances and data samples (data-framework) in data visualizations. Fry shortly addresses this as emergent characteristics; the way in with visualizations can be transformed to form a new appearance. The aspect of dynamic emergent systems is one of the main topics of the thesis of Schelling. Here we talk about the many possibilities that rise form a dynamic emergent system. Schelling cites the work of Chris Crawford explaining that with the possibility of many outcomes, we introduce another state of mind or the behavior: the behavior of exploring and actively thinking and interacting. This state of mind would be one about the meaning and interpretation of the data used together with other possibilities.

In game studies we see that the same concept is applied to games. Katie Salen and Eric Zimmerman write in Rules of Play about games as emergent systems: “creates unexpected patterns of events out of very simple set of rules” (Salen&Zimmerman, 2004. pp.538). By designing systems that allow for large quantities of outcome you would create unexpected complex outcomes that are able to transform and show new insight and meaning. Using these techniques in data visualization will perhaps give way to new and undiscovered patterns and relations. Salen and Zimmerman go a step further and address culturally emergent systems, allowing us to look at the cultural aspects of these complex systems. Cultural emergent systems allow for the exchange of game content (data) and therefore change the outcome of the game and its meaning according to Salen en Zimmerman (Salen&Zimmerman, 2004. pp.538). This is already the case in some data visualizations allowing us to import our own social media data or import our own text or web-addresses to form the input for dynamic data visualizations. Using emergent systems we again see a huge impact on the all three stages of the making of data visualizations. Looking at We Feel Fine we indeed see that with the use of algorithms to gather data we see a dynamic data visualization that changes over time. If we can speak of a true emergent system is perhaps a bit too far because with these dynamic data in We Feel Fine we don’t get new insights and therefore will not find new patterns or relations. Culturally looking we could only indirectly change the content, what would mean going to a lot of blogs and sites to post weird sentences about how we feel. Although We Feel Fine might not be the best case to write about innovating dynamics it gives us a clear new perspective to approach and think about data visualizations. This could lead to a better understanding and making of data visualizations in the future. Looking at visualization tools and software as cultural emergent systems might be an interesting topic for further research but will not be addressed in this paper.

Science versus practice During the research for this paper I somehow felt cheated or twisted about the We Feel Fine. Looking at it from a practical perspective being a maker of data visualizations myself, you can say that it is a great success, both for the makers and for users. If I would have made a similar data visualization I would be proud and perhaps also 4

publish a book about the findings and reactions of the data visualization, just like Harris and Kamvar did . In other cases from Harris and Kamvar we see the same amount of excitement and curiosity. Take for instance I Want You

To Want Me, another data visualizations, here they scrape data form dating sites to reveal longings en feelings of online people (Harris & Kamvar, 2008). On the other hand I am a student in media studies. Because of that background I am quickly reminded of the goals, findings and conclusions from Kamvar and Harris. We Feel Fine “gives us the ability to better understand emotions themselves” (Harris & Kamvar, 2011. pp.1). The underlying questions We Feel Fine raises are scientifically impossible to prove. You would not get answers about our understanding of emotions just by scraping some sentences form some random chosen forums and blog posts. You would need a better understanding of how, why and where all these sentences are written. This is not just the problem with We Feel Fine, there are bigger problems here. Mostly the new social and cultural data that has become available prove to be difficult to use in science. Bernhard Reider who I mentioned earlier showed during the presentation some of his work. He also coped with the same sort of problem using twitter posts for his current research and data visualizations. Reider questioned the means in which these data can scientifically be seen as a source of information. He also does this on his own blog page were he makes more

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data visualizations from for instance Wikipedia. The problem is that there is no context other than twitter itself, which basically can be mutated and altered or even lacking. Most of the data is also not seen by the researchers but was gathered using algorithms. How can we validate these findings? Reider even went as far as to ask the question if all the thousands of twitter users he used could become co-authors of his paper in order to scientifically validate his findings (Reider, 2011). Looking at these social and cultural data online we quickly venture into the study of our online identity versus our ‘normal’ identity. Danah Boyd writes about this issue in the form of how friendships are represented and made in online environments. In her research she shows that real life friendship has little to do with online friendship. The online environment forces us to change our way of thinking and expression. “While networked context shifts the focus away from interests onto people, it is also vulnerable to the architectural aspects of mediated environments” (Boyd, 2006. pp.12). This means that online structures change the way we think and act in online environments. While this already implies that we can’t make worldly statements out of one-sided online data alone, we now do begin to grasp the idea that it is really difficult to use these data. On the other hand we can also look at how we behave online, our online identity. Valerie Frissen and Jos de Mul write about the constitution of our online identity. They see a movement in online identity towards a more culturally constructed notion of identity. An identity that is more aware of the cultural contexts, norms and personal narratives. Therefore an online identity changes from our own physical identity, again showing us that mere twitter tweets and other sentences online cannot be directly compared with off-line identities. These notions make it difficult to actually use these data for the use of scientific research. Reider concluded therefore at the end of the presentation that in science we should use data visualizations as a part of our research method, not as end result or final image. This eliminates some of our problems using social and cultural data. In the text of Harris and Kamvar we do find some small solution to the whole problem. Harris and Kamvar start to analyze their own findings of We Feel Fine through the use of comments and filmed usability testing. Here you see that they use test subjects to form opinions and ideas about these difficult social data to reveal real usable opinions of people. With these they make interesting discoveries how emotions might actually affect us. “While users of We Feel Fine are generally not trained data analysts and the time spent in exploration is often short, the insights from the community have been both real-time and sophisticated.” (Harris & Kamvar, 2011. pp.9). It means that there is hope in changing the method of how we use data visualizations in science. By opening up the visualizations to a target-group we might be able to use large amounts for digitally gathered data in our scientific research. This would introduce an fourth stage in the process of making and giving meaning to data visualizations, a stage were researchers use findings from data visualizations as basis for the analysis of the data.

Conclusions In this paper we critically looked at the making and key features of today’s data visualizations. We have seen that data visualizations are quickly introducing state of the art technology and algorithms. With the use of new graphical styles, technology and interaction we see that data visualizations are changing and are even venturing into hybrid form between data/information design and art. These new data visualizations give us new methods and ways to analyze and research data visualizations. With the knowledge how data visualizations are interacting, designed, programmed and comprehend, we can now use this knowledge to make better and more appealing data visualizations in the future. In the matter of the use of data visualizations in science: we have seen that there al already new methods to include social and cultural data visualizations in scientific research. Hereby we find ourselves exploring new ways of retrieving information from data visualizations, and adding new stages to the process of making and giving meaning to data visualizations.

Bibliography Boyd, Danah. “Friends, friendsters and top 8”. First Monday 11(12), December 2006. http://www.firstmonday.org/issues/issue11_12/boyd/index.html Christiane Paul, “The Database as System and Cultural Form: Anatomies of Cultural Narratives”, in: Victoria Vesna (ed.) Database Aesthetics: Art in the Age of Information Overflow. Minneapolis, MN: University of Minnesota Press, 2007, http://victoriavesna.com/dataesthetics/readings.php Frissen, Valerie en Jos de Mul. ”Under Construction. Persoonlijke en culturele identiteit in het multimediatijdperk”. Infodrome, 2000. Fry, Ben. “Organic Information Design”. M.S. Thesis. Massachusetts Institute of Technology, Program in Media Arts & Sciences, 2000. Harris, Jonathan & Kamvar, Sepandar. “We Feel Fine and Searching the Emotional Web”. www.feelfeelfine.org, 2011. Jeremy Douglass. "Computer Visions of Computer Games: analysis and visualization of play recordings.". Workshop on Media Arts, Science, and Technology (MAST) 2009: The Future of Interactive Media. UC Santa Barbara, January 2009. Lev Manovich, "What is Visualization?" Manovich.net, 2010. http://manovich.net/2010/10/25/new‐article‐what‐is‐visualization/ Lev Manovich. "Trending: The Promises and the Challenges of Big Social Data." Debates in the Digital Humanities, edited by Matthew K. Gold. The University of Minnesota Press, forthcoming 2012. PDF: http://lab.softwarestudies.com/2011/04/new-article-by-lev-manovich-trending.html. Schelling A., Jasper, “Social Network Visualization”. Hogeschool Rotterdam, 2007. http://thesis.jasperschelling.com/thesis_jasperschelling_socialnetworkvisualization.pdf Watts, Duncan. Sheridan Dodds, Peter. Newman, M.E.J. “Identity and search in social networks”. Colombia University, New Tork. February 1, 2008. Zimmerman, Eric & Salen, Katie.”Rules of Play”. Cambridge: MIT Press. 2004

Cases And Presentations Kamvar, Sepandar and Harris, Jonathan. We Feel Fine and Searching the Emotianal Web. 2010. http://www.wefeelfine.org/ Kamvar, Sepandar and Harris, Jonathan. I want you to want me. 2008. http://iwantyoutowantme.org/ Rieder, Bernhard. “Workshop: Computer Simulation & Data Visualisation in the Humanities”. Host: Mirko Tobias Schäfer, Universiteit Utrecht, 18-10-2011. Rieder, Bernhard. The Politics of Systems (Blog). http://thepoliticsofsystems.net/about/