My latest research interests have been in using computational methods to understand society, and in gaining a better understanding of the online platforms that are at the fabric of our every day connections. 

Quantifying the Creator Economy: A Large-Scale Analysis of Patreon

EL Sanyoura, L. and Anderson, Ashton. Quantifying the creator economy: a large scale analysis of Patreon. Extended Abstract in the 7th Annual International Conference of Computational Social Science. working paper

Abstract Membership platforms allow creators to receive income from their followers, but the consumption characteristics of these emergent types of platforms remain poorly understood. We analyze transaction-level data to reveal consumption behaviour and creator dynamics as influenced by user breadth, activity level, and financial spending.

​El Sanyoura, L. and Xu, Y. Gender convergence in the expressions of love: A computational analysis of lyrics. In Proceedings of the 42nd Annual Meeting of the Cognitive Science Society.

Abstract Love is a central theme in modern music, but do women and men differ in their expressions of love? Results from empirical studies on gender differences in love attitudes have evolved from showing consistent differences to more similarities over time and witnessed gender convergence in relationship expectations, housework responsibilities, and sexual attitudes. Independently, pop culture studies have shown how music can be used as a contextual artifact whose lyrics can reflect a culture's changing psychological processes and ideologies. We combine these two research areas to explore whether the gender convergence reported in psychological studies is mirrored in love songs. Using a corpus of lyrics and song metadata from 1960 to 2009, we present a computational analysis of the lexical distribution of lyrics across genre, gender and time. We show that love songs between vocalists who are men vs. women have become significantly more similar in their lyrical expressions of love.

​ Leiyu, J. El Sanyoura, L.  Xu, Y. How nouns surface as verbs: Inference and generation of word class conversion. In Proceedings of the 42nd Annual Meeting of the Cognitive Science Society.

Abstract Word class conversion refers to the extended use of a word from one grammatical class to another without overt morphological marking. Noun-to-verb conversion, or denominalization, is one form of word class conversion studied extensively in the literature. Previous work has suggested that novel denominal verb usages are comprehensible if the listener can compute the intended meaning based on shared knowledge with the speaker. However, no existing work has explored the computational mechanism under this proposal. We propose a frame-semantic generative model, Noun2Verb, that supports the inference and generation of novel denominal verb usages via semi-supervised learning. We evaluate this framework in a dataset of denominal verbs drawn from adults and children against a state-of-the-art model from natural language processing. Our results show that Noun2Verb aligns better with human interpretation and bridges the gap between machines and humans in lexical innovation.