Estimating political leanings from mass media via graph-signal restoration with negative edges

Politicians in the same political party often share the same views on social issues and legislative agendas. By mining patterns in TV news co-appearances and Twitter followers, in this paper we estimate political leanings (left / right) of unknown individuals, and detect outlier politicians who have views different from their colleagues in the same party, from a graph signal processing (GSP) perspective. Specifically, we first construct a similarity graph with politicians as nodes, where a positive edge connects two politicians with sizable shared Twitter followers, and a negative edge connects two politicians appearing in the same TV news segment (and thus likely take opposite stands on the same issue). Given a graph with both positive and negative edges, we propose a new graph-signal smoothness prior based on a constructed generalized graph Laplacian matrix that is guaranteed to be positive semi-definite. We formulate a graph-signal restoration problem that can be solved in closed form. Experimental results show that political leanings of unknown individuals can be reliably estimated and outlier politicians can be detected.