In a sense, Google is a reputation system, and its methods can be adopted to measure the reputations of political leaders, or to cluster them into parties if they have not already organized. The original area in which such network-based techniques were developed was bibliometrics — specifically studying the pattern of literature citations to identify the most influential publications and scientists (Börner 2010*; 2011*). Similar methods are now used in a number of fields, using a range of computational methods, to identify leaders in a network of communication.
A recommender system is a database and statistical analysis engine that recommends future actions to the user — typically what movies to rent or books to buy — based on the user's prior behavior or expressed preferences (Basu et al 1998*; Canny 2002*; Herlocker et al 2004*). These systems are widely used in Internet advertising, in order to customize the sales effort to fit the interests of the audience, but can be developed not only to cluster small issues into coherent political programs, but also even to conduct a form of science-moderated direct voting. The distinction between reputation systems and recommender systems is unclear, and the two share many technical features. But the best way to get the idea across is to look at one of the best-known pure recommender systems, the Netflix movie rating system. [3]
After people rent a movie from Netflix, they are encouraged to rate it on a preference scale from 1 to 5, and their responses are used to determine which movies Netflix will recommend they should rent. Starting in 2006, Netflix held a contest, providing a huge training subset of their data, based on hundreds of thousands of raters, and challenging contestants to devise an algorithm that would best predict customers’ ratings on movies for which the data were not in the training set. I entered the contest, not intending to compete, but to explore how such data might be used to map the styles and ideological orientations of movies. I knew from my earlier research, that people’s preferences were often largely shaped by the visual style of a movie, the leading actors in it, and the year in which it was released — but modulo all these extraneous factors ideology could sometimes be detected (Bainbridge 1992: 470-481*, 2007*).
To illustrate the methods here, I have selected 15 movies that concern artificial intelligence or virtual realities — topics close to fluid democracies in their reliance on information technology for radical social purposes. One consequence is that these films may not differ much from each other in term of ideologies, precisely because they have so much in common. The first methodological challenge is that many respondents rate very few movies, so to get robust results I focused on the 6,551 respondents who had rated at least 10 of the 15, only 110 of whom had rated all 15. They are all diehard sci-fi fans, but if the data concerned politics rather than films, we would be dealing with knowledgeable experts on that very different topic. Table 1 lists the films, the year each was released, the average ratings, and the results of a factor analysis of the data.
Table 1: Fifteen Movies about Advanced Information Technology
| Title | Year | Netflix Raters | Mean Netflix Rating (1-5) | Factor 1 | Factor 2 | Factor 3 | Factor 4 |
|---|---|---|---|---|---|---|---|
| Blade Runner | 1982 | 6313 | 4.18 | 0.84 | |||
| The Matrix | 1999 | 6523 | 4.56 | 0.70 | |||
| The Terminator | 1984 | 6468 | 4.25 | 0.70 | |||
| Tron | 1982 | 5563 | 3.58 | 0.62 | |||
| Westworld | 1973 | 3173 | 3.50 | 0.54 | 0.55 | ||
| RoboCop | 1987 | 5914 | 3.55 | 0.46 | 0.46 | ||
| eXistenZ | 1999 | 2498 | 3.07 | 0.77 | |||
| Star Trek: The Motion Picture | 1979 | 5321 | 3.46 | 0.70 | |||
| The Thirteeth Floor | 1999 | 2524 | 3.31 | 0.60 | 0.51 | ||
| Bicentennial Man | 1999 | 4658 | 3.27 | 0.83 | |||
| A.I. Artificial Intelligence | 2001 | 5990 | 3.14 | 0.69 | |||
| I, Robot | 2004 | 6047 | 3.81 | 0.41 | 0.56 | ||
| Cyborg | 1989 | 2068 | 2.79 | 0.66 | |||
| Johnny Mnemonic | 1995 | 4186 | 3.03 | 0.52 | |||
| 2001: A Space Odyssey | 1968 | 5920 | 3.81 | -0.75 |
*
Börner, Katy. 2010. Atlas of Science: Visualizing What We Know. Cambridge, Massachusetts: MIT Press.
*
Börner, Katy. 2011. “Network Science: Theory, Tools, and Practice.” Pp 49-59 in Leadership in Science and Technology, edited by W. S. Bainbridge. Thousand Oaks, California: Sage.
*
Canny, John. 2002. "Collaborative Filtering with Privacy via Factor Analysis.” Pp. 238-245 in Proceedings of the 25th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. New York: ACM.
*
Herlocker, Jonathan L., Joseph A. Konstan, Loren G. Terveen, and John T. Riedl. 2004. "Evaluating Collaborative Filtering Recommender Systems." ACM Transactions on Information Systems 22:5-53.