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Visualization as a New Media Literacy


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Visualization as a New Media Literacy

  1. 1. Chapter 6 Visualization as a New Media Literacy Erin Reilly Defining Visualization Every day, visualizations affect what we do. As data increasingly mediates our lives, visualization allows us all to be inquirers and interpreters so that we can represent data in meaningful ways. Today, we are able to combine multiple data sets resulting from our aggregation of knowledge from social media and multiple research resources, which allows us to philosophize about what we want and how we exist in the world to a wide audience. Visualization is not a new phenomenon. Scientists have been collecting data to aggregate, analyze, and quantify information into visual presentations for some time. In the past, scientific visualizations were equated with numeric data rather than texts, images, and relational networks. Today, scientific research is not isolated in a lab. New forms of data aggregation and visualization allow scientists to collaborate across the world and across disciplines to hypothesize, solve complex problems, and offer new theories. The use of images as visualizations in mass media is common today. Newspapers, such as The New York Times, employ an in-house interactive team to create custom visualizations for journalists to clarify large amounts of information. With the rise of free visualization tools (such
  2. 2. as Tableau Public, Many Eyes, and more accessible data sets open to the public, the process of making sense of our world and our place in it has become democratized. Through social media platforms such as Twitter or Facebook, we now keep track of our actions and store personal data to better understand our thoughts, interests, and desires. Companies associated with every facet of life—economy, environment, education, health, transportation— collect data too. Combined, these companies have given rise to a world made up of pools of information gathered from multiple digital devices we use every day. Not only do companies collect data, they also share data with the public through visualizations. Visualizations help us make sense of data, and of our society and our place in it. The traditional, top-down means of gathering data and analyzing content we now combine with grassroots aggregation of opinion and sentiment, or of nuanced actions that reflect what we like or don’t like. Aggregated data gives us a broader, more complex view of the world provided to us from multiple sources rather than from singular experts. Visualization—the ability to interpret and create data representations for the purpose of expressing ideas, finding patterns and persuading people to take action. Broadly defined, visualization means the act or process of interpreting in visual terms or of putting into visible form. But defining visualization as another new media literacy is more complicated. In 2006, Confronting the Challenges of Participatory Culture: Media Education in the 21st Century (Jenkins, Purushotma, Clinton, Weigel, & Robison, 2006) was released, where eleven new media literacies were identified as a set of social skills and cultural competencies to best equip young people to become full participants in the emergent media landscape and raise
  3. 3. public understanding about what it means to be literate in a globally interconnected, multicultural world. From this white paper, a new research group, Project New Media Literacies emerged at MIT and currently is situated at USC’s Annenberg Innovation Lab. This research group which I have led since 2007 as Research Director determined that the initial set of eleven new media literacies was not an exhaustive list and that as our society and cultural exchanges through media change, so too does the importance of new skills needed to be a full participant in our rich media landscape. Adding to the original eleven new media literacies, Visualization as a new media literacy builds upon information visualization. As defined by Lev Manovich, visualization is “a mapping between discrete data and a visual representation” (Manovich, 2010). He includes in his discussion, the understanding of visualization as an important social skill to acquire in that it bridges the relationship between numeric and non-numeric data. Visualization as a new media literacy is defined as the ability to interpret and create data representations for the purpose of expressing ideas, finding patterns, and persuading people to take action. Type the word, “Why. . .” in Google’s search box and up pops the most searched phrases, like “Why is the sky blue?” Traditional search, though, is not the only viable option for the web users of tomorrow. Netflix recommends movies you’ll enjoy based on past films watched. Wikipedia’s History page allows you to view the development of any entry back to its formation. Twitter shows you your friends and those to whom they are connected. Post images from your vacation and they will automatically geo-locate onto a map to share your journey with others. Want to make sense of the State of the Union speech? Load it into Wordle and the key themes will be revealed. Be motivated to make change in our world with artists like Chris Jordan who
  4. 4. uses visualizations to issue strong statements about human consumption and waste? Jordan’s visual images on Gyre, 2009 represent actual statistics relating to 2.4 million pieces of plastic, which are equal to an estimated number of pounds of plastic pollution that enter the world’s oceans every hour. Relationship to other New Media Literacies The Internet provides us with the ability to connect, on a global scale, our ideas, objects and actions. It provides the venue for fostering collective intelligence—the ability to pool knowledge and compare notes with others in order to achieve a common goal. The value of data is its ability to inform or to enable decision and action. Practicing visualization within social constructs enables us to communicate data effectively so that others can gain new insight into the data we shared. With this ability to represent large amounts of data, we use visualization beyond numeric metrics, such as tables and charts, to make sense of our social constructs. The adage, “A picture is worth a thousand words,” now includes the hundreds of pictures, hundreds of thousands of words, and multiple strands of layered stories we can create through visualization. Computer scientists, designers, and artists like Jonathan Harris use visualizations to combine humanities with science. While new media offers ways to systematize information, it also offers a place for people to share and contribute their thoughts, ideas and feelings about themselves, others and society. In two works, Harris explores “Man in the Machine” (I Want You to Want Me []), an interactive installation about online dating; and in “The Machine in Man” (The Whale Hunt []),
  5. 5. a “photographic heartbeat” of a traditional Alaskan Eskimo whale hunt consisting of over three thousand photographs that view the event from multiple perspectives and visual manipulations. As visualization is more than a static image on a page—it is interactive—it provides new levels of data representation. It combines quantity and quality (numbers and text) so we can better understand, interpret and interact with art, science, and society. Visualization uses data as input and encourages users to manipulate the data, whether to explore a story or an argument, or to understand and interpret complex problems. Visualization fosters new questions; and often, by manipulating or combining data sets, new patterns emerge. Confronting the Challenges of Participatory Culture white paper (Jenkins et al., 2006) also introduced the concept of simulation—the ability to interpret and construct dynamic models of real-world processes. As a complement to this idea, visualization provides powerful new ways of representing and manipulating information. It expands our cognitive capacity, allowing us to deal with larger bodies of information, to experiment with more complex configurations of data, to form hypotheses quickly and to test them against different variables in real time. Visualization can be effective in representing known knowledge or in testing emerging theories. However, there are differences between simulation and visualization. Simulations create new data by modeling processes whereas visualizations translate data to evoke relationships. It is more likely that visualizations allow hypotheses to emerge from play rather than from scientific ruminations. While data visualization has become more popular as a field of study, it is still an essentially rhetorical practice, able to influence people or engender action but only to the degree that it is leveraged in a larger social, cultural framework. Using visualization effectively also
  6. 6. requires it to be complemented with the new media literacy, judgment—the ability to evaluate the reliability and credibility of different information sources. In other words, does the data shared in the visualization come from a trusted source? Is it collected in a reliable manner? Is its release timely and influential? Is it embedded in a good story? Bringing together the new media literacies of judgment and visualization encourages a balance of participation and experimentation and offers opportunities to bring the real world into the classroom. Visualization as a New Form of Literacy In today’s media-rich world, we cannot think of language (such as print literacy) as the main means for representation and communication. New modes of meaning have emerged that need to be taken into consideration when thinking about literacy (Kress, 35). Though it is important to know how to read and write using traditional modes of communication, the new media literacies build upon these modes and offer new forms of reading and writing through social interaction with others. Visualization takes into account the importance of knowing how to read and write data as images and mediates our interaction with the visual information. Learning how to read and write visual data will help make literacy accessible to everyone. Like a lens on a camera, visualizations give us the ability to zoom out for the macro overview or zoom into the micro view and see the tiniest detail in the original context of the visualization shared. However, we need to understand which structure works best with which type of data. Visualization as a new form of reading and writing can also be identified as a new form of writing at the structural level. At the structural level, different types of visual methods are
  7. 7. used to represent data, which requires the writer to clearly understand which mode of communication will best represent the inquiry. Visualization methods provide a systematic graphic format to create, share and codify knowledge (Lengler & Eppler, 2007). For example, network visualization methods use macro representations of data. They give overviews of social constructs, the relationship between people, items or entities. Similar to bar charts found in Excel, network visualizations are readable by understanding spatial dimensions. The larger the spatial node, the more that node represents connections to a specific person. If nodes are clustered together, the connection of each node to the others becomes readily apparent. Silobreaker is an automated search service for news and current affairs that aims to provide more relevant results to the user than traditional search-and-aggregation engines. Instead of listing articles matching a search query, Silobreaker displays people, companies, organizations, topics, places, and keywords associated with the search; it understands how they relate to each other in the news flow and puts them in context through visualizations. The visualization of the network is optimized to keep strongly related items in close proximity to each other. In this way, the overall arrangement of nodes in the network clearly represents the connections between nodes (nodes that are far away are weakly related to each other). In this visualization, the size of a node is proportional to the number of edges emanating from it. To interpret the data, however, we also need to read the presentation the visualization offers so that we can interpret the hierarchy and composition of the nodes as they are mapped onto the image. Where spatial arrangement has been the primary means of reading visualizations
  8. 8. since the eighteenth century, color, saturation, and texture can also support how a fixed spatial layout (such as the geography of a country) is represented (Manovich, 2010). A micro representation of data uses a different visualization method all together, like a phrase net diagram PhraseNet can be used to look at literary styles over the century, to understand power relations between characters in a story, and to identify alliteration and assonance in poetry. PhraseNet explores the relationships between different words used in a specific text. It uses a simple form of pattern matching to provide multiple views of the concepts contained in a book, speech or poem. For example, through creating a word graph made from Jane Austen’s Pride and Prejudice, .the program has drawn a network of words; two words are connected if they appear together in a phrase of the form “X and Y.” We don’t know what the characters said or what their actions were, just that they spoke with each other and which character had the most dialogue based on the size of the character’s name and the thickness of the arrow in relation to the other characters. So the main characters are linked, and the positive attributes of the characters form a group. This strategy shows that Darcy, who is a main character, does not appear in the network. The visualization forces us to ask: Why is the main character of the novel not represented in the network? What does this absence tell us about his character? These questions emerge through identifying patterns within the visualization and offer readers a new way of entering the text. Challenges to Consider
  9. 9. Data can be generated, stored, and used in ways that raise questions like these either well or poorly. Knorr (1981) questions whether visualization can convince anyone in any specific way about a text because many interpretations are possible. However, Latour (1983) argues that visualization supports making the invisible be visible. While it is true visualizations can convey a story in multiple ways, we must be mindful of the intent behind the data collected and shared visually. To be media literate, visualizations should be transparent and allow a de-layering process, like peeling back the layers of an onion. An example of this process in action is being conducted at University of Southern California’s Annenberg Innovation Lab led by Francois Bar. His research group is currently working to develop appropriate methods for better determining sentiment through Twitter. To determine sentiment in Twitter, researchers use tools to select keywords, hashtags (#), and users’ handles (@____) to scrape and catalogue the tweets. The cataloguing of this data is often broken into positive, neutral, and negative, which can then be represented visually through spatial placement, color, and size and gives the readers a quick view of the pulse of the people. Though natural language processing, technology can quickly identify positive or negative language. When using these methods with the 2012 Presidential Campaign, Francois’ research group quickly realized the limitations of visually representing sentiment analysis. The difficulty lies when false positives occur when people tweet language such as sarcasm. These are harder for a computer alone to determine, and the distributed cognition between the person and the tools becomes much more important. Another problem in manipulating and representing data is determining which data to use. Each day, we add more data to our networked world. In a given month on Facebook, each user, on average, uploads 70 pieces of content. With half a billion members of Facebook, that’s
  10. 10. roughly 35 billion pieces of media shared. The key to overcoming this mass of data input is to determine what data to leave out. If we added how much media is created and shared through YouTube and Twitter, you can begin to see the world of big data that we have entered. In a world of rapid change, representation is fluid and not static. When trying to represent any data that moves through our social communities, the schema would not be the same from year to year or, for that matter, from day to day. The more we add data, the easier it will become for numbers to be meaningless without context. An important part of visualization as a skill is the ability to see and understand where potential points of relationships in the data can be analyzed. This begins with formulating specific questions to help narrow searches, find points of intersection to review and identify patterns in the data. Pattern recognition is an acquired skill and research has shown that encouraging interdisciplinary studies (such as combining art and math, or architecture and nature) and looking beyond the definitive to the ripples or echoes of decisions may help to foster this skill. What might be Done in the Classroom Educators should encourage students to participate in a community of practice that is interested in deepening their understanding of visualization. This offers a way for users to pool their knowledge together toward a deeper understanding of the representations and patterns different visualizations in discussion offer, and in turn, this provides a historical analysis of the data that
  11. 11. will change over time since data continues to grow and therefore constantly changes the results we identify and can analyze. A few platforms are Tableau Public, Many Eyes, and MapTube. Though each takes a different approach to visualizations, they all offer a suite of data visualization tools for you to create and share visualizations. Fernanda Viegas and Martin Wattenberg created IBM’s Many Eyes, and to date it is probably the most robust visualization tool that incorporates many layers of social features into its platform. People can share comments, datasets, and data visualizations to support their discussion and arguments, including the ability to make comments on a specific view of the visualization based on the variables you are interacting with or the filters you have created to view the visualization. For novice visualization creators, this is an opportunity to offer a context for the design of the visualization and to receive feedback from others who have more experience. Done correctly—using the right tools, data, and appropriate questions—visualization can promote transparency. By asking the right questions and telling a story, visualizations provide an outlet to understand the relationship between quantifiable numbers and quality of people’s opinions and sentiments. Armed with more access to information, visualizations can encourage people to be active in civic life and turn data into action. References Jenkins, H., Purushotma, R., Clinton, K., Weigel, M., & Robison, A. J. (2006). Confronting the challenges of participatory culture: Media education for the 21st century. Cambridge, MA: MIT Press.
  12. 12. Jordan, Chris. (2007). Running the Numbers: Gyre [painting]. Los Angeles, CA: Paul Kopeikin Gallery. Knorr, K. (1981). The manufacture of knowledge. Oxford, UK: Pergamon. Kress, G. (2003). Literacy in the new media age. New York: Routledge. Latour, B. (1983). Comment redistribuer le grand partage? Revue Internationale de Synthèse, 104(110), 202–236. Latour, B. (1985). Visualization and cognition: Drawing things together. Culture Technique, 14, 14–17. Lengler, R., & Eppler, M. J. (2007). Towards a periodic table of visualization: Methods for management. Lugano: Switzerland Institute of Corporate Communication. Manovich, Lev. (2001, Oct. 27). Info-aesthetics: Information and form. Retrieved July 2013 from Manovich, Lev. (2010). What is visualization? Retrieved from Tufte, E. R. (1983). The visual display of quantitative information. Cheshire, CT: Graphics Press. Viégas, F. & Wattenberg, M. (2006). Communication-minded visualization: A call to action. IBM Systems Journal, 45(4), 801-812.