Learning Ideological Embeddings from Information Cascades

TY - GEN

T1 - Learning Ideological Embeddings from Information Cascades

AU - Monti, Corrado

AU - Manco, Giuseppe

AU - Aslay, Cigdem

AU - Bonchi, Francesco

N1 - Publisher Copyright: © 2021 ACM.

PY - 2021/10

Y1 - 2021/10

N2 - 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.

AB - 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.

KW - embedding

KW - information diffusion

KW - polarization

UR - http://www.scopus.com/inward/record.url?scp=85119186238&partnerID=8YFLogxK

U2 - 10.1145/3459637.3482444

DO - 10.1145/3459637.3482444

M3 - Article in proceedings

AN - SCOPUS:85119186238

SP - 1325

EP - 1334

BT - Proceedings of the 30th ACM International Conference on Information & Knowledge Management (CIKM '21)

PB - Association for Computing Machinery

CY - New York

T2 - 30th ACM International Conference on Information and Knowledge Management, CIKM 2021

Y2 - 1 November 2021 through 5 November 2021

ER -