Word clouds continue to be a popular tool for summarizing textual information, despite their well-documented deficiencies for analytic tasks. Much of their popularity rests on their playful visual appeal. In this paper, we present the results of a series of controlled experiments that show that layouts in which words are arranged into semantically and visually distinct zones are more effective for understanding the underlying topics than standard word cloud layouts. White space separators and/or spatially grouped color coding led to significantly stronger understanding of the underlying topics compared to a standard Wordle layout, while simultaneously scoring higher on measures of aesthetic appeal. This work is an advance on prior research on semantic layouts for word clouds because that prior work has either not ensured that the different semantic groupings are visually or semantically distinct, or has not performed usability studies. An additional contribution of this work is the development of a dataset for a semantic category identification task that can be used for replication of these results or future evaluations of word cloud designs.

Online content creators have to manage their relations with opaque, proprietary algorithms that platforms employ to rank, filter, and recommend content. How do content creators make sense of these algorithms and what does that teach us about the roles that algorithms play in the social world? We take the case of YouTube because of its widespread use and the spaces for collective sense-making and mutual aid that content creators (YouTubers) have built within the last decade. We engaged with YouTubers in one-on-one interviews, performed content analysis on YouTube videos that discuss the algorithm, and conducted a wiki survey on YouTuber online groups. This triangulation of methodologies afforded us a rich understanding of content creators' understandings, priorities, and wishes as they relate to the algorithm. We found that YouTubers assign human characteristics to the algorithm to explain its behavior; what we have termed algorithmic personas. We identify three main algorithmic personas on YouTube: Agent, Gatekeeper, and Drug Dealer. We propose algorithmic personas as a conceptual framework that describes the new roles that algorithmic systems take on in the social world. As we face new challenges around the ethics and politics of algorithmic platforms such as YouTube, algorithmic personas describe roles that are familiar and can help develop our understanding of algorithmic power relations and accountability mechanisms.