Casting a wide net in the interpretation of 'user engagement' suggests options related to engagement operationalized wrt keyword search activities and wikipedia editing: Sreenivasan, *Quantitative analysis of the evolution of novelty in cinema through crowdsourced keywords* https://www.nature.com/articles/srep02758.pdf?proof=true *Abstract* The generation of novelty is central to any creative endeavor. Novelty generation and the relationship between novelty and individual hedonic value have long been subjects of study in social psychology. However, few studies have utilized large-scale datasets to quantitatively investigate these issues. Here we consider the domain of American cinema and explore these questions using a database of films spanning a 70 year period. We use crowdsourced keywords from the Internet Movie Database as a window into the contents of films, and prescribe novelty scores for each film based on occurrence probabilities of individual keywords and keyword-pairs. These scores provide revealing insights into the dynamics of novelty in cinema. We investigate how novelty influences the revenue generated by a film, and find a relationship that resembles the Wundt-Berlyne curve. We also study the statistics of keyword occurrence and the aggregate distribution of keywords over a 100 year period. Metyan, et al., *Early Prediction of Movie Box Office Success Based on Wikipedia Activity Big Data* http://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.007122... *Abstract* Use of socially generated ‘‘big data’’ to access information about collective states of the minds in human societies has become a new paradigm in the emerging field of computational social science. A natural application of this would be the prediction of the society’s reaction to a new product in the sense of popularity and adoption rate. However, bridging the gap between ‘‘real time monitoring’’ and ‘‘early predicting’’ remains a big challenge. Here we report on an endeavor to build a minimalistic predictive model for the financial success of movies based on collective activity data of online users. We show that the popularity of a movie can be predicted much before its release by measuring and analyzing the activity level of editors and viewers of the corresponding entry to the movie in Wikipedia, the well-known online encyclopedia. On Sun, Jul 1, 2018 at 11:36 AM, Lior Zalmanson <zalmanson@gmail.com> wrote:
Hello,
I'm teaching a seminar on "Understanding User Engagement" and while most studies will be more empirical work on the nature of online user behavior, I want to encourage discussions around critical notions of user engagement, social media, and online participation.
I would love any recommendations you might have. Of course, I've added thinkers such as Lovink and Lanier, but I'm looking for as many points of view as possible, representing an array of cultures, genders, etc. They don't have to be academic journal papers. Articles in Wired/Atlantic and similar publications will be even better.
Looking forward and thank you in advance,
Dr. Lior Zalmanson, Assistant Professor Dept. of Information and Knowledge Management University of Haifa, Israel _______________________________________________ The Air-L@listserv.aoir.org mailing list is provided by the Association of Internet Researchers http://aoir.org Subscribe, change options or unsubscribe at: http://listserv.aoir.org/ listinfo.cgi/air-l-aoir.org
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