Abstract
We analyze and extend a recently proposed model of linguistic diffusion in social networks, to analytically derive time to convergence, and to account for the innovation phase of lexical dynamics in networks. Our new model, the degree-biased voter model with innovation, shows that the probability of existence of a norm is inversely related to innovation probability. When the innovation rate in the population is low, variants that become norms are due to a peripheral member with high probability. As the innovation rate increases, the fraction of time that the norm is a peripheral-introduced variant and the total time for which a norm exists at all in the population decrease. These results align with his-torical observations of rapid increase and generalization of slang words, technical terms, and new common expressions at times of cultural change in some languages.
Original language | English (US) |
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Title of host publication | 10th International Conference on Autonomous Agents and Multiagent Systems |
Place of Publication | Taipei |
Pages | 649-656 |
Number of pages | 8 |
Volume | 1 |
State | Published - 2011 |
Event | 10th International Conference on Autonomous Agents and Multiagent Systems 2011, AAMAS 2011 - Taipei, Taiwan, Province of China Duration: May 2 2011 → May 6 2011 |
Other
Other | 10th International Conference on Autonomous Agents and Multiagent Systems 2011, AAMAS 2011 |
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Country/Territory | Taiwan, Province of China |
City | Taipei |
Period | 5/2/11 → 5/6/11 |
Keywords
- Degree-biased voter model
- Lexical innovation
- Norms
- Social simulation
ASJC Scopus subject areas
- Artificial Intelligence