18 research outputs found
Computational Social Scientist Beware: Simpson's Paradox in Behavioral Data
Observational data about human behavior is often heterogeneous, i.e.,
generated by subgroups within the population under study that vary in size and
behavior. Heterogeneity predisposes analysis to Simpson's paradox, whereby the
trends observed in data that has been aggregated over the entire population may
be substantially different from those of the underlying subgroups. I illustrate
Simpson's paradox with several examples coming from studies of online behavior
and show that aggregate response leads to wrong conclusions about the
underlying individual behavior. I then present a simple method to test whether
Simpson's paradox is affecting results of analysis. The presence of Simpson's
paradox in social data suggests that important behavioral differences exist
within the population, and failure to take these differences into account can
distort the studies' findings.Comment: to appear in Journal of Computational Social Science
Performance Dynamics and Success in Online Games
Online data provide a way to monitor how users behave in social systems like
social networks and online games, and understand which features turn an
ordinary individual into a successful one. Here, we propose to study individual
performance and success in Multiplayer Online Battle Arena (MOBA) games. Our
purpose is to identify those behaviors and playing styles that are
characteristic of players with high skill level and that distinguish them from
other players. To this aim, we study Defense of the ancient 2 (Dota 2), a
popular MOBA game. Our findings highlight three main aspects to be successful
in the game: (i) players need to have a warm-up period to enhance their
performance in the game; (ii) having a long in-game experience does not
necessarily translate in achieving better skills; but rather, (iii) players
that reach high skill levels differentiate from others because of their
aggressive playing strategy, which implies to kill opponents more often than
cooperating with teammates, and trying to give an early end to the match
Can Social News Websites Pay for Content and Curation? The SteemIt Cryptocurrency Model
This is an accepted manuscript of an article published by SAGE Publishing in Journal of Information Science on 15/12/2017, available online: https://doi.org/10.1177/0165551517748290
The accepted version of the publication may differ from the final published version.SteemIt is a Reddit-like social news site that pays members for posting and curating content. It uses micropayments backed by a tradeable currency, exploiting the Bitcoin cryptocurrency generation model to finance content provision in conjunction with advertising. If successful, this paradigm might change the way in which volunteer-based sites operate. This paper investigates 925,092 new members’ first posts for insights into what drives financial success in the site. Initial blog posts on average received 20,680.83. Longer, more sentiment-rich or more positive comments with personal information received the greatest financial reward in contrast to more informational or topical content. Thus, there is a clear financial value in starting with a friendly introduction rather than immediately attempting to provide useful content, despite the latter being the ultimate site goal. Follow-up posts also tended to be more successful when more personal, suggesting that interpersonal communication rather than quality content provision has driven the site so far. It remains to be seen whether the model of small typical rewards and the possibility that a post might generate substantially more are enough to incentivise long term participation or a greater focus on informational posts in the long term