Social data analysis – Information visualization and participatory culture
Researchers in all areas of human knowledge are overwhelmed with data. Through the process of sensemaking, in which information is collected, organized, and analyzed, new knowledge is formed and further action is informed. We must make sense of data in order to produce real value from it. “The availability of information is irrelevant without a means to interpret it effectively” (Danziger, 2008). Information visualization supports the process of sensemaking by exploiting an individual’s visual perception to facilitate cognition (Card, Mackinlay, and Shneiderman, 1999). On the other hand, sensemaking is often also a social process (Heer, Viégas, and Wattenberg, 2007). People may interpret data differently and also some data sets are very large and impossible for exploration by a single person. People may also learn from their peers through consensus building and decision making. Heer, Viégas, and Wattenberg conclude “that to fully support sensemaking, visualizations should also support social interaction.”
The observations have shown that “the social life of visualizations” (Wattenberg, 2005) is an important factor in shaping the adoption, use, and efficacy of a visualization – “an aspect often overlooked by the psychological/analytic orientation of contemporary information visualization” (Heer, 2006). These findings and developments motivated Jeffrey Heer to recast visualization applications as social artifacts, and not just external cognitive artifacts; to move “from purely task-based considerations to that of ludic, or playful, activity.”
In recent years, we have witnessed the emergence of online services for data collection and analysis that facilitate social data analysis – the development of interactive visualizations and mass interaction with data supported by social interaction (e.g. Many Eyes, Swivel, Tableau Public, etc.). Social data analysis is a style of analysis in which people work in a social, collaborative context to make sense of data and in which collective intelligence is assisted to uncover understanding (definition from Wikipedia). These ‘social data analysis’ sites serve as collaborative information visualization tools that let people import datasets, create a graph or other type of visual representation of this data, and then share that visualization with other users. The users of these sites may share data sets and associated analysis results and they may also interact via online discussions related to shared items. Many Eyes stands as the current benchmark for socially designed information visualization in its incorporation of these social media standards at all levels of the system (Danziger, 2008).
[Visualization and comments on Many Eyes]
It could be said that most important about this new ‘data culture’ is its social and cultural impact. The usage of social data analysis sites may be viewed as a component of ‘participatory culture‘ (Jenkins, 2006) – a cultural system in which users are also producers, and where visualization serves as much as an authoring tool as a method of analysis (Viégas, Wattenberg, and Feinberg, 2009).
In an essay (2002) based on Pierre Levy’s idea of ‘collective intelligence’ Jenkins argues that new media offered “new tools and technologies that enable consumers to archive, annotate, appropriate, and recirculate content,” and that these tools led to “a range of subcultures that promote Do-It-Yourself media production.” Jenkins suggests that ‘transmediality’ is altering “the way media consumers relate to each other, to media texts, and to media producers … and is demanding more active modes of spectatorship” (Jenkins, 2002).
In his book Convergence Culture (2006), Jenkins develops his arguments suggesting that ‘convergence’ is not a technological process but a feature of audience behaviour – that we, the audience, are ‘converging’. He also defines participatory culture as a culture with relatively low barriers to artistic expression and civic engagement, strong support for creating and sharing creations, and some type of informal mentorship whereby experienced participants pass along knowledge to novices. In a participatory culture, members also believe their contributions matter and feel some degree of social connection with another (Jenkins, 2006).
In the book “Confronting the Challenges of Participatory Culture”, Jenkins et al. (2009) list four forms of participatory culture: affiliations, expressions, collaborative problem solving, and circulations. On social data analysis sites, like Many Eyes, users have memberships in online communities (affiliations), users produce new creative forms (expressions), and users work together in teams to complete tasks and develop new knowledge (collaborative problem solving). A growing number of scholars suggests potential benefits from these forms of participatory culture, “including opportunities for peer-to-peer learning, a changed attitude toward intellectual property, the diversification of cultural expression, the development of skills valued in the modern workplace, and a more empowered conception of citizenship” (Jenkins et al., 2009).
The user activity around Many Eyes fits Jenkins’ definition of participatory culture. The barriers to entry are low, and we see both self expression and, in political graphs, civic engagement. There is technical and legal infrastructure for sharing creations, and finally, there is informal mentorship in terms of more experienced users, blog comments, and online videos. The range of uses, from casual to serious, is also characteristic of the participatory culture.
[User created content on Many Eyes]
The notion of the active audience has been central to understanding the nature of oppositional and community uses of media (Harrison and Barthel, 2009). ‘Audiences’ have created media content on a long-term for purposes related largely to political and community communication. The idea that engagement with new media can be used to enhance the processes and practices of democracy is common in cases of radical media, community radio and television, and community computer networking (Harrison and Barthel, 2009). Publicly available data along with technology that would enable users to analyze it in a collaborative effort should foster more democratic environment. Web 2.0 visualization platforms are expected to contribute to democratization of the public sphere on the Net. These expectations could be said to reflect utopianists’ ‘technology will change all’ way of thinking which is typically proclaimed with every new technology.
However, this ‘liberation’ is not automatic. Some research has pointed at the statistics that run contrary to the expectations of technological empowerment which would give rise to massive collaborative data analysis. According to Noël and Lemire (2009), social data analysis tools are not yet as popular and there are not known collaborative projects of this kind that could compare in a scale or significance with any of Wikipedia entries. While people are motivated for collaboration on data analysis once given the tools, not everyone has the expertise or knowledge of statistics, mathematics and information science to really indulge in a deeper or meaningful data analysis; “It is utopian to think that, one day, experts will collaboratively analyze data repositories” (Noël and Lemire, 2009). Data structuring and pre-processing involves a lot of hard work and people will probably not be willing to just share it if not getting credit for it, or if their work could get taken by someone else. The question of insufficient expertise has also been addressed by Freyne and Smyth (2009) in an effort to automate visualization choices in relation to specification of data and make tasks easier for the novice (non-expert) user. By using Many Eyes as a platform for research, these authors exploit case-based reasoning techniques to make visualization recommendations.
According to a 2007 study conducted by the Pew Internet & American Life project, 59 percent of all American teenagers (69 percent of teens who use the Internet) could be considered content/media creators (Lenhart et al., 2007). As expected, teenagers constitute the largest demographic category of content creators and sharers. Adults do not engage in content creation in the same capacity, still 35 percent of online adults have participated in blogging, content sharing, etc.
There is an evident need to match new technical means and tools with models of social learning in the new environment. It seems that there is a problem in adaptation to the new media as to be able to use them productively. Alan Kay emphasizes the necessity of new literacy and makes distinction between the ability to ‘read’ medium and the ability to ‘write’. Reading corresponds with the passive behavior, possibility to use products generated by others. Writing assumes active behavior. ”The ability to ‘write’ in a medium means you can generate materials and tools for others. You must have both to be literate” (Kay, 1990).
Jenkins et al. (2009) argue that schools and after-school programs should spend more time on fostering “new media literacies” – a set of cultural competencies and social skills that young people need in the new media landscape. Authors state that participatory culture shifts the focus of literacy from individual expression to community involvement and that the new literacies almost all involve social skills developed through collaboration and networking, and built on the foundation of traditional literacy, research skills, technical skills, and critical-analysis skills taught in class. Authors further argue for educators to work together in order to ensure that all children have access to the skills and experiences needed to become full participants, that they can articulate their understanding of how media shapes perceptions, and that they are “socialized into the emerging ethical standards that should shape their practices as media makers and participants in online communities” (Jenkins et al., 2009). It would be a mistake, authors say, to reduce new media literacies to technical skills.
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