TOWARD A POST-SERIALITY MAP OF TV SERIES

Visualizing the New TV Seriality System with Protovis

Pier Giuseppe Mariconda and Stefano Perna

Department of Communication, University of Salerno, Via Ponte Don Melillo, Fisciano, SA, Italy

Keyword: Cultural Analysis, Serials, Visualization, Protovis.

Abstract: The aim of this work is to visualize some information resulted from an analysis on 60 series from the top

rated ones in the 2001-2010 decade. Arc-Diagram is presented as an overview of the TV series co-actor

network. Airing networks and ratings are highlighted in the circular graph. Major insights are discussed.

1 INTRODUCTION

The visualization based study of TV series data is

interesting for several reasons:

The first one is that most people know about

them and can relate them to movies and actors, so,

when it is presented with a visualization of serials

data, they will try to find their favorite series and

actors, identify series of potential interest or explore

the complex co-actor relationships among actors.

The second one is that the built dataset has some

rich information on each TV series allowing for a

wide variety of data, it is sufficiently clean and easy

to update so further analysis can be done without

using semantic matching techniques.

The third one is that with this work we hope to

communicate the power of the visualization, and

more generally of the computer based approach to a

traditional field of humanities research, the

sociology of culture.

In this, as in other fields of humanities research,

visualizations can be more than eye candy, rather it

can be used as an analysis tool through which

quickly obtain new insights.

For some years now several authors and scholars

(Manovich, 2007, 2010) have begun to show a

serious interest in the use of interactive visualization

as a tool for humanistic research: in 2007 at the

campus of University of California, San Diego

(UCSD) was established Software Studies Initiative

led by Lev Manovich; one year later NEH (National

Endowment for Humanities) announced a new

“Humanities High-Performance Computing”

(HHPC) initiative that WAS based on similar

insights. Manovich called this approach "Cultural

Analytics", a methodology through which "cultural

data" could be analyzed with the instruments of

quantitative research, of statistics and computational

information visualization. Quantitative analysis is

not new to humanistic research, as sociology from

its beginnings employed quantitative methodologies.

What's new is the huge amount of "cultural traces"

(Manovich, 2010) which are stored in digital

archives and, for this reason, IT may be subjected to

computational analysis for showing overall trends

and models. If computational quantitative analysis

and visualization is quite normal in the hard science

and in the new media communities, more traditional

humanistic disciplines as theory of culture or

semiotics are not familiar with this kind of

instruments.

An important exception (and a fundamental

inspiration for our work) is Franco Moretti's

approach to literary history (Moretti, 2005): his

“Graphs, maps and trees” is an impressive

demonstration of the power of quantitative

visualization in the analysis of qualitative subjects

(as the history of literature). Moretti claims that

these instruments could give to humanistic research

what he calls a "distant reading", an opportunity for

an overall view of very complex cultural process

(e.g. the rise of novel in Europe across three

centuries). Other similar approaches are beginning to

emerge, as well as visualization tools and computer-

based experiments in the humanities began to be

developed. Some notable examples are: in literary

field Literature Fingerprints (Keim, Oelke, 2007),

TextArc (Paley, 2002) and BibleViz (Harrison,

2008) in sociology worthy of note is the work based

262

Giuseppe Mariconda P. and Perna S..

TOWARD A POST-SERIALITY MAP OF TV SERIES - Visualizing the New TV Seriality System with Protovis.

DOI: 10.5220/0003355702620265

In Proceedings of the International Conference on Imaging Theory and Applications and International Conference on Information Visualization Theory

and Applications (IVAPP-2011), pages 262-265

ISBN: 978-989-8425-46-1

 2011 SCITEPRESS (Science and Technology Publications, Lda.)

on some theories of (Latour, 2005), Cartography of

Controversies (Venturini, 2008) in which the

visualization is used as a tool for sociological

analysis. In the context of the actual spreading of

information over the web and on digital archives,

humanistic researchers have the opportunity to

manage large sets of data for cultural analysis and

for the interpretation of large cultural trends in what

Jurij Lotman called the "Semiosphere" (Lotman,

2001). Now we can gain a "distant reading" with the

help of computational analysis of data and

interactive visualization.

2 VISUALIZATION IN

HUMANITIES RESEARCH:

TOOL AND PURPOSE FOR A

"DISTANT READING".

PROJECT DETAILS

2.1 TV Series as a Major Cultural

Process in the Contemporary

Semiosphere

In this study we try to apply the paradigm of cultural

analytics and of "distant reading" to an important

part of the contemporary culture industry: the

American TV series system. Many sociologist and

critics recognized the importance of TV series in the

contemporary media system. In Italy sociologist as

Alberto Abbruzzese (2001) and Sergio Brancato

(2007) analyzed deeply this evolution. They claim

that TV is a formidable device that characterized

daily life and the social construction of the "reality

principle" for the entire second half of the twentieth

century. Today it appears in a state of profound

transformation of its original statutes and it seems

involved in the dynamics of "demassification" that

involves contemporary society as a whole. TV

traditional genres - typical of the general strategy of

a mass medium facing the domestic space and the

system of relations existing between his subjects -

make it increasingly difficult to withstand the impact

of cultural changes initiated “by” the advent of the

Web. Among the traditional television formats only

fiction can still operate on the level of relationship

with the public, renewing that particular narrative

function which is strong from its origins and that

give reasons for the success in the context of

consumption of aesthetic forms. The narratives of

the television drama evolve within a media

framework governed by the logic of scheduling, as

to say from a grid that orders the time of TV flow in

relation to the personal needs of the audience as well

as the strategies of the advertising market. Even

more evidently, the deep meaning of TV

consumption is built not so much on the originality

of the texts, but on their ability to attract the public

through the choreography of repetition, the ritual

return to the already known. In This sense TV series

represent the real incarnation of the deep sense of

the contemporary televisual consumption and

production. Especially in the USA - with great

frequency and intensity - the TV-series or sit-coms

are the real laboratory in which the redefinition of

the writing work processes and the audiovisual

declination of digital technologies take shape.

How to map this broad cultural system - a system

in which different kinds of actors interrelate and

conflict (e.g. TV Networks, audience, advertising,

writers, actors, genres, etc.)?

2.2 Mapping the System of Seriality in

2.2.1 Data Sources

The purpose of this project is to make a visualization

about some aspects of the new television series

system that Brancato (2007) calls post-serial system.

The data set used in this study is related to 60 TV

series and is about ranking, rating, longevity, cast

members and some information about the production

(network, crew, etc.).

Data have been obtained both from the databases

of companies specialized in the measurement of

rating and audience as The Nielsen Company

(http://www.nielsen.com/) and from UGC based

sites as Wikipedia (http:// en.wikipedia.org) and

TV.com (http://www.tv.com/).

2.2.2 Details of Data

The 60 TV series analyzed have been selected

according to the ranking from the list of the “100

most appreciated TV series in the 2001 - 2010

decade” drawn up by TV.com users. In choosing the

60 more meaningful series we have used the ranking

index rather than rating in order to include in our

analysis some series, diffused through pay TV

channels and that have been watched by a smaller

number of persons, due to economic concerns; the

audience rating we have used in our analysis is the

average rating of all seasons of airing; in the matter

of the series started before 2001 the longevity data

was calculated by the airing of the first season

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premiere.

2.2.3 Visualizations Explanation

Figure 1: Legend.

Legend: the legend at the center of poster refers to

the visualizations A1, A2, A3 and A4.

Visualization A: the information is displayed on

two levels:

- the internal level consists of a donut-chart

graph that represents the distribution of the audience

aggregated according to the airing network of all the

series. The colored sectors represent the distribution

of audience related to television networks under

investigation. Each color represents a TV network

(see legend);

- the outer layer consists of a sunburst graph in

which the height of each blocks represents the

longevity (or else the number of seasons of each

series), and the width of these indicates the average

number of viewers for each series. Always referring

to the blocks, the dark gray indicates an ended series

while the light gray indicates a still on air series.

Figure 2: Visualization A.

Visualization B. This visualization consists of a

node-arc diagram, a layout already developed by

other authors (Harrison, 2008; Wattemberg, 2002).

The nodes, arranged on the horizontal line, represent

the TV series, size and color of each node indicate,

respectively, the rating (average level of audience)

and the broadcasting network (see legend). The arcs

(links between nodes) indicate human relationships

among series (cast sharing or crew sharing), the

stroke of the arcs changes in proportion to the

number of relationships existing between the two

linked knots.

Interaction. The visualization B is originally

developed for digital media insofar as it provides the

opportunity to interact with the information

displayed, highlighting the links starting from a

single node selected by mouse-over. B1 reproduce a

"snapshot" captured on the interactive version of B

in which a node has been activated by an

hypothetical user. The interactive version of this

visual artifact is available at: http://dsc.

unisa.it/disind/proj/SAR_map.html

Figure 3: Visualization B.

Figure 4: Visualization B1 - Example of interaction.

2.2.4 Design and Production

All the visualizations presented in this work were

made with Protovis (http://vis.stanford.edu/

protovis/), a visualization toolkit for JavaScript

developed by Mike Bostock and Jeff Heer of the

Stanford Visualization Group (http://vis.stanford.

edu/), with help from Vadim Ogievetsky (Bostock,

IVAPP 2011 - International Conference on Information Visualization Theory and Applications

264

Heer, 2009).

We have processed the data following the seven

steps stressed by (Ben Fry, 2008) in his work on

visualization. This path consists of seven stages:

1.Acquire - 2.Parse - 3.Filter - 4.Mine - 5.Represent

- 6. Refine - 7.Interact:

1 - the acquisition phase was carried out

manually writing out the data from the sources cited

above, in some spreadsheets;

2 - the parsing was made using open source

spreadsheet application (Calc, included in

Openoffice Suite);

3 - the filtering stage involved the exclusion of

the negligible samples (in order to this analysis) and

some adjustment on the wrongly reported values;

4 - mining is consisted in reporting data gotten in

the earlier stages in a JavaScript file

5 - in the representation step two different sketch

were generated with Protovis using as data source

the JavaScript file created in 4th step;

6 - the refinement was made by acting directly

on the Protovis html and JavaScript related files by

an open source code editor (notepad++);

7 - the interaction phase was in part already

developed during the 5st stage and, afterwards

completed by uploading the project on the server.

3 CONCLUSIONS

This work is the first step of an ongoing project.

Further developments include the application of this

kind of analysis and visualization to the actual

contents (audio and video) of TV-series and in the

correlation of these semantic data to quantitative

data types and categories.

This analysis reveals some interesting insights.

First we see that the TV series aired on the same

network tend to have a similar number of spectators.

Notwithstanding the difference in the dataset and the

type of analysis, the two visualizations must be

viewed in relation: if visualization A shows how the

universe of the TV series is divided and segmented

according to market and consumption logic, typical

of large television networks, visualization B,

contrariwise, shows a dense set of correlations that

suggest a deeply interconnected world, so the Tv

series universe emerges as united and unified,

regardless of the commercial logic of the single

network. For example, visualization B shows a

dense network of cooperation between the products

aired by different networks.

Another insight revealed by the analysis of

audience datas (visualization A) concerns the

proportion between the number of viewers and

longevity of the series. On large networks free-to-air

longevity is directly linked to the number of

spectators and to its stability over time, instead pay-

TV networks keep on air for a longer time even

niche products (e.g. Dexter, Weeds, Californication).

This new prospective model of analysis could be

the basis for a new cultural analysis (Manovich,

2007) of the television system as an agency of

cultural dissemination.

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