Studying Fake News Spreading, Polarisation Dynamics, and Manipulation by Bots: a Tale of Networks and Language

TL;DR

This paper surveys the multidisciplinary research on fake news, analyzing how networks and language contribute to its spread, polarization, and manipulation by bots, aiming to unify insights from network science and computational linguistics.

Contribution

It provides a network-based analysis of existing literature, reviews key computational approaches, and bridges disciplines to enhance understanding of fake news dynamics.

Findings

Identifies key trends and influential publications in fake news research

Highlights the role of network structures in misinformation spread

Emphasizes the importance of combining network science and linguistics

Abstract

With the explosive growth of online social media, the ancient problem of information disorders interfering with news diffusion has surfaced with a renewed intensity threatening our democracies, public health, and news outlets' credibility. Therefore, thousands of scientific papers have been published in a relatively short period, making researchers of different disciplines struggle with an information overload problem. The aim of this survey is threefold: (1) we present the results of a network-based analysis of the existing multidisciplinary literature to support the search for relevant trends and central publications; (2) we describe the main results and necessary background to attack the problem under a computational perspective; (3) we review selected contributions using network science as a unifying framework and computational linguistics as the tool to make sense of the shared content. Despite scholars working on computational linguistics and networks traditionally belong to different scientific communities, we expect that those interested in the area of fake news should be aware of crucial aspects of both disciplines.