A minimalistic model of bias, polarization and misinformation in social networks

[Mis- and disinformation in social media: mitigating risks in digital health communication]

Misinformation and disinformation in social media have become a challenge for effective public health measures. Here, we examine factors that influence believing and sharing false information, both misinformation and disinformation, at individual, social, and contextual levels and discuss intervention possibilities.At the individual level, knowledge deficits, lack of skills, and emotional motivation have been associated with believing in false information. Lower health literacy, a conspiracy mindset and certain beliefs increase susceptibility to false information. At the social level, the credibility of information sources and social norms influence the sharing of false information. At the contextual level, emotions and the repetition of messages affect belief in and sharing of false information.Interventions at the individual level involve measures to improve knowledge and skills. At the social level, addressing social processes and social norms can reduce the sharing of false information. At the contextual level, regulatory approaches involving social networks is considered an important point of intervention.Social inequalities play an important role in the exposure to and processing of misinformation. It remains unclear to which degree the susceptibility to belief in and share misinformation is an individual characteristic and/or context dependent. Complex interventions are required that should take into account multiple influencing factors.

The roots of polarization in the individual reward system

Polarization raises concerns for democracy and society, which have expanded in the internet era where (mis)information has become ubiquitous, its transmission faster than ever, and the freedom and means of opinion expressions are expanding. The origin of polarization however remains unclear, with multiple social and emotional factors and individual reasoning biases likely to explain its current forms. In the present work, we adopt a principled approach and show that polarization tendencies can take root in biased reward processing of new information in favour of choice confirmatory evidence. Through agent-based simulations, we show that confirmation bias in individual learning is an independent mechanism and could be sufficient for creating polarization at group level independently of any additional assumptions about the opinions themselves, a priori beliefs about them, information transmission mechanisms or the structure of social relationship between individuals. This generative process can interact with polarization mechanisms described elsewhere, but constitutes an entrenched biological tendency that helps explain the extraordinary resilience of polarization against mitigating efforts such as dramatic informational change in the environment.

Used like Pawns or Treated like Kings? How Narratives around Racehorse Welfare in the 2023 Grand National May Affect Public Acceptance: An Informed Commentary

The 2023 Grand National steeplechase race was delayed when protesters from the animal rights group, 'Animal Rising', gained access to the course just prior to the race. The international media spotlight was focused on what is already a high-profile event and the social licence of both this race and racing in general was scrutinised. Both at the time and for several days afterwards, the general public was exposed to two different narratives from pro- and anti-racing communities. This paper discusses these perspectives and the potential impact on the general public's relationship with racing. Whilst well-meaning and aiming to promote racing, much of the racing industry's commentary inadvertently risked damaging its reputation due to a poor understanding of social licence principles. We explore the reasons for these two groups' alternative perspectives on welfare and suggest considerations for change. Ultimately, if 'the people's race' is to maintain its social licence, the racing community needs to both understand and embrace the concept. Welcoming independent opinions, engaging with different viewpoints, accepting that change is inevitable and, most importantly, being proactive in making changes to prioritise equine welfare will all help racing to move towards greater public acceptance.

Seven years of studying the associations between political polarization and problematic information: a literature review

This literature review examines the intersection between political polarization and problematic information, two phenomena prominent in recent events like the 2016 Trump election and the 2020 COVID-19 pandemic. We analyzed 68 studies out of over 7,000 records using quantitative and qualitative methods. Our review revealed a lack of research on the relationship between political polarization and problematic information and a shortage of theoretical consideration of these phenomena. Additionally, US samples and Twitter and Facebook were frequently analyzed. The review also found that surveys and experiments were commonly used, with polarization significantly predicting problematic information consumption and sharing.

Troll and divide: the language of online polarization

The affective animosity between the political left and right has grown steadily in many countries over the past few years, posing a threat to democratic practices and public health. There is a rising concern over the role that "bad actors" or trolls may play in the polarization of online networks. In this research, we examined the processes by which trolls may sow intergroup conflict through polarized rhetoric. We developed a dictionary to assess online polarization by measuring language associated with communications that display partisan bias in their diffusion. We validated the polarized language dictionary in 4 different contexts and across multiple time periods. The polarization dictionary made out-of-set predictions, generalized to both new political contexts (#BlackLivesMatter) and a different social media platform (Reddit), and predicted partisan differences in public opinion polls about COVID-19. Then we analyzed tweets from a known Russian troll source (N = 383,510) and found that their use of polarized language has increased over time. We also compared troll tweets from 3 countries (N = 79,833) and found that they all utilize more polarized language than regular Americans (N = 1,507,300) and trolls have increased their use of polarized rhetoric over time. We also find that polarized language is associated with greater engagement, but this association only holds for politically engaged users (both trolls and regular users). This research clarifies how trolls leverage polarized language and provides an open-source, simple tool for exploration of polarized communications on social media.

Cognitive cascades: How to model (and potentially counter) the spread of fake news

Understanding the spread of false or dangerous beliefs-often called misinformation or disinformation-through a population has never seemed so urgent. Network science researchers have often taken a page from epidemiologists, and modeled the spread of false beliefs as similar to how a disease spreads through a social network. However, absent from those disease-inspired models is an internal model of an individual's set of current beliefs, where cognitive science has increasingly documented how the interaction between mental models and incoming messages seems to be crucially important for their adoption or rejection. Some computational social science modelers analyze agent-based models where individuals do have simulated cognition, but they often lack the strengths of network science, namely in empirically-driven network structures. We introduce a cognitive cascade model that combines a network science belief cascade approach with an internal cognitive model of the individual agents as in opinion diffusion models as a public opinion diffusion (POD) model, adding media institutions as agents which begin opinion cascades. We show that the model, even with a very simplistic belief function to capture cognitive effects cited in disinformation study (dissonance and exposure), adds expressive power over existing cascade models. We conduct an analysis of the cognitive cascade model with our simple cognitive function across various graph topologies and institutional messaging patterns. We argue from our results that population-level aggregate outcomes of the model qualitatively match what has been reported in COVID-related public opinion polls, and that the model dynamics lend insights as to how to address the spread of problematic beliefs. The overall model sets up a framework with which social science misinformation researchers and computational opinion diffusion modelers can join forces to understand, and hopefully learn how to best counter, the spread of disinformation and "alternative facts."

A Confirmation Bias View on Social Media Induced Polarisation During Covid-19

Social media has played a pivotal role in polarising views on politics, climate change, and more recently, the Covid-19 pandemic. Social media induced polarisation (SMIP) poses serious challenges to society as it could enable 'digital wildfires' that can wreak havoc worldwide. While the effects of SMIP have been extensively studied, there is limited understanding of the interplay between two key components of this phenomenon: confirmation bias (reinforcing one's attitudes and beliefs) and echo chambers (i.e., hear their own voice). This paper addresses this knowledge deficit by exploring how manifestations of confirmation bias contributed to the development of 'echo chambers' at the height of the Covid-19 pandemic. Thematic analysis of data collected from 35 participants involved in supply chain information processing forms the basis of a conceptual model of SMIP and four key cross-cutting propositions emerging from the data that have implications for research and practice.

The impact of noise and topology on opinion dynamics in social networks

We investigate the impact of noise and topology on opinion diversity in social networks. We do so by extending well-established models of opinion dynamics to a stochastic setting where agents are subject both to assimilative forces by their local social interactions, as well as to idiosyncratic factors preventing their population from reaching consensus. We model the latter to account for both scenarios where noise is entirely exogenous to peer influence and cases where it is instead endogenous, arising from the agents' desire to maintain some uniqueness in their opinions. We derive a general analytical expression for opinion diversity, which holds for any network and depends on the network's topology through its spectral properties alone. Using this expression, we find that opinion diversity decreases as communities and clusters are broken down. We test our predictions against data describing empirical influence networks between major news outlets and find that incorporating our measure in linear models for the sentiment expressed by such sources on a variety of topics yields a notable improvement in terms of explanatory power.