Context: The wide adoption of social media presents an interesting opportunity for performing data mining and knowledge discovery in a real-world context. However, the observations and research findings made with social media data can only provide unprecedented insights into human behavior if social data provides a representative description of human activity. Recent empirical evidence on US 2016 election or 2016 Brexit prediction outcomes has warned against biases and inaccuracies occurring not only at the source of the data and during the processing but also under the influence of those that design, build, maintain or use the analytical models, embedding and amplifying their biases. The recognition of different sorts of biases in the data analytics pipeline and their consequences open new research venues related to bot detection, neutralizing content pollution, noise removal from social media data, needs for innovative evaluation methods as well as ethical boundaries around the use of social data.
Goal: The goal of this workshop is to contribute a practical perspective to the body of research that aims to quantify biases, to devise better methods for prototypical processing pipeline for social data, to evaluate such methods in context and to develop actionable frameworks on different dimensions of responsible social analytics aiming to mitigate the potential issues associated with many sorts of biases.