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@article{almaatouq2021empirica,

title = {Empirica: a virtual lab for high-throughput macro-level experiments},

author = {Abdullah Almaatouq and Joshua Becker and James P Houghton and Nicolas Paton and Duncan J Watts and Mark E Whiting},

url = {https://drive.google.com/file/d/1asyd8onp_2df10W1AbzPOyar6WknEiCU/view},

doi = {10.3758/s13428-020-01535-9},

year  = {2021},

date = {2021-01-01},

urldate = {2021-01-01},

journal = {Behavior Research Methods},

pages = {1--14},

publisher = {Springer},

abstract = {Virtual labs allow researchers to design high-throughput and macro-level experiments that are not feasible in traditional in-person physical lab settings. Despite the increasing popularity of online research, researchers still face many technical and logistical barriers when designing and deploying virtual lab experiments. While several platforms exist to facilitate the development of virtual lab experiments, they typically present researchers with a stark trade-off between usability and functionality. We introduce Empirica: a modular virtual lab that offers a solution to the usability--functionality trade-off by employing a ``flexible defaults'' design strategy. This strategy enables us to maintain complete ``build anything'' flexibility while offering a development platform that is accessible to novice programmers. Empirica's architecture is designed to allow for parameterizable experimental designs, reusable protocols, and rapid development. These features will increase the accessibility of virtual lab experiments, remove barriers to innovation in experiment design, and enable rapid progress in the understanding of human behavior.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{houghton2017beyond,

title = {Beyond Keywords: Tracking the evolution of conversational clusters in social media},

author = {James P. Houghton and Michael Siegel and Stuart Madnick and Nobuaki Tounaka and Kazutaka Nakamura and Takaaki Sugiyama and Daisuke Nakagawa and Buyanjargal Shirnen},

url = {https://core.ac.uk/download/pdf/188187247.pdf},

doi = {10.1177/0049124117729705},

year  = {2017},

date = {2017-10-09},

journal = {Sociological Methods & Research},

volume = {48},

number = {3},

pages = {588-607},

abstract = {The potential of social media to give insight into the dynamic evolution of public conversations, and into their reactive and constitutive role in political activities, has to date been underdeveloped. While topic modeling can give static insight into the structure of a conversation, and keyword volume tracking can show how engagement with a specific idea varies over time, there is need for a method of analysis able to understand how conversations about societal values evolve and react to events in the world, incorporating new ideas and relating them to existing themes. In this paper, we propose a method for analyzing social media messages that formalizes the structure of public conversations, and allows the sociologist to study the evolution of public discourse in a rigorous, replicable, and data-driven fashion. This approach may be useful to those studying the social construction of meaning, the origins of factionalism and internecine conflict, or boundary-setting and group-identification exercises; and has potential implications for those working to promote understanding and intergroup reconciliation.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}