In the second preliminary study of SIRUP we tried to identify indicators that predict the perceived familiarity of a television program. During this project we work with broadcasting data from BBC’s ViSTA-TV project. Based on the available data from this project and external public sources, we identified five possible indicators for familiarity (see Figure 1):
- Number of BBC viewers
- Number of Google results
- Number of Facebook likes
- Number of Twitter references
- IMDB user ratings
Figure 1. Testing the selected indicators for familiarity
For all five indicators, a positive relationship was hypothesized, such that a higher number of BBC ratings, Google results, Facebook likes, Twitter references, and IMDB user ratings would positively predict the perceived familiarity of the program (e.g., programs with more Facebook likes are perceived as more familiar by users).
Using the BBC ViSTA-TV data, we selected the five programs with the lowest and highest number of BBC viewers, Google results, Facebook likes, Twitter references, and IMDB user ratings. As such, a total of 50 programs was rated by the participants. The selected programs can be found here: SIRUP Preliminary study 2, Familiarity – Selected programs.
Similar to the first preliminary study (complexity), participants were provided with a program episode description for each of the programs informing about the program title and season number, the episode number and title, and the episode synopsis, genre, format, and release year. Below an example of such a description is provided for the program Only Fools and Horses (high IMDB user ratings; see Figure 2).
Figure 2. Program episode description example Only Fools and Horses
In its measurement, familiarity was conceptualized as popularity. For each of the programs participants were asked whether they had watched the program or not. If they had watched the program, they were asked to rate the perceived popularity of the program. If they had not watched the program, they were asked to rate the perceived popularity of the program based on its description. In both cases participants rated popularity on a 7-point semantic scale, with “unpopular” on the left and “popular” on the right (see Figure 3).
Figure 3. Measure for familiarity
Data were collected via Amazon Mechanical Turk and a total of 164 participants from the United States of America participated in the study. As was the case for the first preliminary study (complexity), the results showed that none of the indicators we selected was related to familiarity. This means that the number of BBC viewers, Google results, Facebook likes, Twitter references, and IMDB user ratings of a program did not predict the perceived familiarity of a program. This finding was replicated when we distinguished between participants that had watched the show and those that had not. More information about the results of the preliminary study for familiarity can be found here: SIRUP Preliminary study 2, Familiarity – Results.
In short, we have not yet successfully indentified indicators that predict the perceived familiarity of a program. If you have any ideas or suggestions, please leave us a message below!
The dataset for the SIRUP preliminary study for familiarity has been made publically available and can be found here: SIRUP Preliminary study 2, Familiarity – Dataset.