Abstract
Many people rely on online social networks as sources of news and information, and the spread of media content with ideologies across the political spectrum influences online discussions and impacts actions offline. To examine the impact of media in online social networks, we generalize bounded-confidence models of opinion dynamics by incorporating media accounts as influencers in a network. We quantify partisanship of content with a continuous parameter on an interval, and we formulate higher-dimensional generalizations to incorporate content quality and increasingly nuanced political positions. We simulate our model with one and two ideological dimensions, and we use the results of our simulations to quantify the "entrainment" of content from non-media accounts to the ideologies of media accounts in a network. We maximize media impact in a social network by tuning the number of media accounts that promote the content and the number of followers of the accounts. Using numerical computations, we find that the entrainment of the ideology of content spread by non-media accounts to media ideology depends on a network's structural features, including its size, the mean number of followers of its nodes, and the receptiveness of its nodes to different opinions. We then introduce content quality --- a key novel contribution of our work --- into our model. We incorporate multiple media sources with ideological biases and quality-level estimates that we draw from real media sources and demonstrate that our model can produce distinct communities ("echo chambers") that are polarized in both ideology and quality. Our model provides a step toward understanding content quality and ideology in spreading dynamics, with ramifications for how to mitigate the spread of undesired content and promote the spread of desired content.
Highlights
Online social media have become extremely influential sources of news in daily life
We propose a measure of media impact; this allows us to quantify the influence that a set of media accounts has on the ideology of content in a social network at consensus
An important question that we are able to study with our model is how much the ideology of the media account(s) in a network entrains the content that is spread by nonmedia accounts in that network
Summary
Online social media (such as Twitter, Facebook, Instagram, and others) have become extremely influential sources of news in daily life. One approach is to mathematically model the spread of content on social media using ordinary differential equations, where the equations can capture changes in time of the proportion of a population that is “susceptible to,” “exposed to,” “infected with,” or “immune to” the propagation of such content (e.g., a rumor) [15,16,17,18] Such compartmental models have the advantage of being analytically tractable, but they do not capture the effects of either network structure or heterogeneity in account characteristics. Some recent work on modeling the spread of content has tried to bridge the gap between these two approaches by introducing mathematical models that capture network features of social media [24,25], including some very recent mechanistic models of radicalization dynamics [26] and filter bubbles [27]. We formulate and study a model for the influence of media accounts on the ideology and quality of content that is shared in an online social network.
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