Character network analysis of two science fiction Series – stargate and Star Trek / Melody Tan Shi Ai

• Main Author: Melody Tan , Shi Ai
• Format: Thesis
• Published: 2018
• Subjects: Q Science (General); QA Mathematics
• Online Access: http://studentsrepo.um.edu.my/10209/1/Melody_Tan_Shi_Ai.pdf; http://studentsrepo.um.edu.my/10209/6/melody.pdf; http://studentsrepo.um.edu.my/10209/

This work undertakes a social network analysis of two science fiction franchise, Stargate and Star Trek. These two franchise consist of several television series and movies. Social network analysis is used to explore the social network of characters, derived from the transcripts. The stories conveyed are in the form of the interactions of characters, which can be represented as “character networks”. The character networks are formed by extracting the social interactions or connectivity of the characters. That is, the co-occurrences of the characters from a specific scene demarcates the presence of a connection irrespective of exchange of spoken dialogue. These networks are then used to characterise the overall structure and topology of each franchise. The character networks of both franchise have similar structure and topology to that found in previous work on scientific collaborations, literature, mythology and comics networks. The character networks exhibit the small-world effect and is disassortative, but no significant support for power-law distribution was found. Furthermore, since the progression of a story depends to a large extent on the interaction between each of its characters, the underlying network structure relates something about the complexity of the storyline. The complexity is assessed using techniques from spectral graph theory. The episode networks are found to be structured either as (1) closed networks, (2) those containing bottlenecks, (3) a mixture of the first two structures, or (4) those containing two or more connected components. Lastly, the characters’ role in the narrative was found to be supported by the characters’ centrality and centralisation measures.