People influence each other when they interact to solve problems. Such social influence introduces both benefits (higher average solution quality due to exploitation of existing answers through social learning) and costs (lower maximum solution quality due to a reduction in individual exploration for novel answers) relative to independent problem solving.
Very large spatio-temporal lattice data are becoming increasingly common across a variety of disciplines. However, estimating interdependence across space and time in large areal datasets remains challenging, as existing approaches are often (i) not scalable, (ii) designed for conditionally Gaussian outcome data, or (iii) are limited to cross-sectional and univariate outcomes.
In this article, we summarize the first civic game contest, its rules and process, and the results. We describe civics, games and argue that there is a fruitful intersection to be had between those two fields. Finally, we introduce the winning games.
Millions of conversations are generated every day on social media platforms. With limited attention, it is challenging for users to select which discussions they would like to participate in. Here we propose a new method for microblog conversation recommendation.
Conventional wisdom assumes that increased censorship will strictly decrease access to information. We delineate circumstances when increases in censorship expand access to information for a substantial subset of the population.
Prior work established the benefits of server-recorded user engagement measures (e.g. clickthrough rates) for improving the results of search engines and recommendation systems. Client-side measures of post-click behavior received relatively little attention despite the fact that publishers have now the ability to measure how millions of people interact with their content at a fine resolution using client-side logging.
The rise of fake news highlights the erosion of long-standing institutional bulwarks against misinformation in the internet age. Concern over the problem is global. However, much remains unknown regarding the vulnerabilities of individuals, institutions, and society to manipulations by malicious actors. A new system of safeguards is needed.
Manual annotations are a prerequisite for many applications of machine learning. However, weaknesses in the annotation process itself are easy to overlook. In particular, scholars often choose what information to give annotators without examining these decisions empirically. For subjective tasks such as sentiment analysis, sarcasm, and stance detection, such choices can impact results.
Recent studies show the remarkable power of fine-grained information disclosed by users on social network sites to infer users’ personal characteristics via predictive modeling. Similar fine-grained data are being used successfully in other commercial applications.
predictive modeling; transparency; privacy; comprehensibility; inference; control