Learning Ideological Embeddings from Information Cascades
This addresses the challenge of understanding misinformation propagation and confirmation bias in social networks, but is incremental as it builds on existing cascade modeling approaches.
The paper tackled the problem of modeling information cascades by learning user ideological leanings in a multidimensional space, and found that their model successfully inferred political stances from real-world Twitter and Reddit data.
Modeling information cascades in a social network through the lenses of the ideological leaning of its users can help understanding phenomena such as misinformation propagation and confirmation bias, and devising techniques for mitigating their toxic effects. In this paper we propose a stochastic model to learn the ideological leaning of each user in a multidimensional ideological space, by analyzing the way politically salient content propagates. In particular, our model assumes that information propagates from one user to another if both users are interested in the topic and ideologically aligned with each other. To infer the parameters of our model, we devise a gradient-based optimization procedure maximizing the likelihood of an observed set of information cascades. Our experiments on real-world political discussions on Twitter and Reddit confirm that our model is able to learn the political stance of the social media users in a multidimensional ideological space.