Social Noise: Generating Random Numbers from Twitter Streams

Abstract

Due to the multiple applications of random numbers in computer systems (cryptography, online gambling, computer simulation, etc.) it is important to have mechanisms to generate these numbers. True Random Number Generators (TRNGs) are commonly used for this purpose. TRNGs rely on non-deterministic sources to generate randomness. Physical processes (like noise in semiconductors, quantum phenomenon, etc.) play this role in state of the art TRNGs. In this paper, we depart from previous work and explore the possibility of defining social TRNGs using the stream of public messages of the microblogging service Twitter as randomness source. Thus, we define two TRNGs based on Twitter stream information and evaluate them using the National Institute of Standards and Technology (NIST) statistical test suite. The results of the evaluation confirm the feasibility of the proposed approach.

Implementation of the Binary Random Number Generator Using the Knight Tour Problem

“…Many tests can be applied to measure the randomness strength in the generated sequence (Fernández, Quintas, Sánchez, & Arias, 2015;Zhou, Liao, Wong, Hu, & Xiao, 2009). A binary string can be considered a random stream if no observable relationship exists between the individual bits of the sequence.…”