While smart cities have the required infrastructure for traffic prediction, underdeveloped (like in Latin America) cities lack the budget and technology to perform an accurate model. Social networks have been shown to predict online behavior and interactions, but their prediction capabilities are still unknown. The hypothesis of this work is that social networks can aid in predicting vehicular traffic when data is scarce due to a lack of resources. This paper proposes a method with social network analysis to aid in the lack of data due to the minimal amount of traffic sensors. The Twitter API was used to download a network of users that follow traffic update accounts and then, use a model of information diffusion (independent cascade model) to retrieve a variable that holds a metric of how the information regarding current traffic has traveled through the network. Finally, an updated traffic dataset with the new social network variable is used to train and test an LSTM neural network to show if the new variable can be a predictor for traffic. Results show that a deterministic independent cascade model ran on a New York City-based 2-tier social network marginally improved the prediction by 0.4%. This proposal will be replicated in other information diffusion models like Bass, stochastic Independent Cascade, and agent-based. Furthermore, the deep learning methodology will be extended to hold spatio-temporal variables. The main contributions to date of this ongoing work are: (1) a systematic literature review presenting a gap in novel traffic prediction methods for underdeveloped cities, (2) a preliminary study for traffic prediction in cities with ITS that cannot hold a significant amount of sensor data, and (3) a proposition of future research venues where this method can be applied.