The influence of community structure on opinion expression: an agent-based model

Abstract

Social media has become important in shaping the public discourse on controversial topics. Many businesses therefore monitor different social media channels and try to react adequately to a potentially harmful opinion climate. Still, little is known about how opinions form in an increasingly connected world. The spiral of silence theory provides a way of explaining deviations between the perceived opinion climate and true beliefs of the public. However, the emergence of a spiral of silence on social media is hard to observe because only the thoughts of those who express their opinions are evident there. Recent research has therefore focused on modelling the processes behind the spiral of silence. A particular characteristic of social media networks is the presence of communities. Members of a community tend to be connected more with other members of the same community than with outsiders. Naturally, this might affect the development of public opinion. In the present article we investigate how the number of communities in a network and connectivity between them affects the perceived opinion climate. We find that higher connectivity between communities makes it more likely for a global spiral of silence to appear. Moreover, a network fragmented into more, smaller communities seems to provide more “safe spaces” for a minority opinion to prevail.

Breaking the silence: the role of social media in fostering community and challenging the spiral of silence

“…These findings suggest that exposure to a topic in a closed group setting OIR 48,4 can have significant effects on individual attitudes and behavior toward the subject matter and can lead to a normalization of the topic of discussion. These findings align with prior studies that have established that fragmented networks provide a conducive environment for the flourishing of minority viewpoints (Cabrera et al, 2021). Consequently, the higher the degree of specificity within a social media group, the greater the participants' inclination to express their perspectives candidly and in an authentic manner.…”

“…Nevertheless, identifying the spiral of silence’s manifestation on social media remains challenging due to bias toward opinion expressers. The influence of social media on shaping public discourse regarding contentious subjects has gained prominence (Cabrera et al. , 2021) as recent research focuses on modeling the underlying processes of this phenomenon.…”

Protective health behaviors and fear of social isolation during the COVID-19 pandemic: a public opinion perspective

“…As noted in a recent study about vaccine hesitancy (Yang & Liu, 2021), the government or media decision-makers should also monitor how the public perceive risk of the ongoing pandemic or disease, so as to avoid one-sided information delivery. It is also important to monitor smaller networks in social media as they could influence the majority voice thereby creating conflicting information (Cabrera et al, 2021;Mihelj et al, 2022). After assessment, the public would adjust their willingness to perform behaviors to protect themselves as well as to motivate others to follow the public opinion.…”

“…As has been empirically examined (Neuwirth & Frederick, 2004), peer influence is also compatible with the spiral of silence theory, wherein people could lose their assessment ability due to the conflicting opinions in the social milieu (Noelle-Neumann, 1993). Recent research similarly notes that stronger interconnections between smaller social groups could generate a 'global spiral' in wider society (Cabrera, Ross, Röchert, Brünker & Stieglitz, 2021). Moreover, underrepresented ethnic groups may be more susceptible to peer influence, according to research among adolescents (Gardner & Steinberg, 2005).…”

Genomic surveillance reveals dynamic shifts in the connectivity of COVID-19 epidemics

“…To validate the use of PhyloSor in measuring the temporal connectivity between locations, we conducted epidemic simulations using FAVITES V1.1.35 [52]. First we generated static contact networks in FAVITES using a modified Barabási-Albert algorithm [53]. We generated two separate 20,000 member communities using the Barabási-Albert algorithm with a mean value of 8 contacts per day.…”