9 research outputs found
Statistical Physics Of Opinion Formation: is it a SPOOF?
We present a short review based on the nonlinear -voter model about
problems and methods raised within statistical physics of opinion formation
(SPOOF). We describe relations between models of opinion formation, developed
by physicists, and theoretical models of social response, known in social
psychology. We draw attention to issues that are interesting for social
psychologists and physicists. We show examples of studies directly inspired by
social psychology like: "independence vs. anticonformity" or "personality vs.
situation". We summarize the results that have been already obtained and point
out what else can be done, also with respect to other models in SPOOF. Finally,
we demonstrate several analytical methods useful in SPOOF, such as the concept
of effective force and potential, Landau's approach to phase transitions, or
mean-field and pair approximations.Comment: 29 pages, 4 figures, new section 6 slightly extended, figures of
higher quality, corrected typos, extended references, other minor
improvements throughout the tex
Asymmetric contrarians in opinion dynamics
Asymmetry in contrarian behavior is investigated within the Galam model of
opinion dynamics using update groups of size 3 with two competing opinions A
and B. Denoting and the respective proportions of A and B contrarians,
four schemes of implementations are studied. First scheme activates contrarians
after each series of updates with probabilities and for agents holding
respectively opinion A and B. Second scheme activates contrarians within the
update groups only against global majority with probability when A is
majority and when B is majority. Third scheme considers in-group
contrarians acting prior to the local majority update against both local
majority and minority opinions. Last scheme activates in-group contrarians
prior to the local majority update but only against the local majority. The
main result is the loss of the fifty-fifty attractor produced by symmetric
contrarians. Producing a bit less contrarians on its own side than the other
side becomes the key to win a public debate, which in turn can guarantee an
election victory. The associated phase diagram of opinion dynamics is found to
exhibit a rich variety of counterintuitive results.Comment: 13 pages LaTeX with numerous figs; ver 2 updated with new
bibliographic refeernces and corrections to figure reference
Evaluating Stakeholder Bias in Stakeholder Analysis In Social Media
Stakeholder analysis is the first step in the planning of most infrastructure projects. Selecting and then applying the best method for a project’s stakeholder analysis is extremely important for correctly assessing stakeholder opinions. Social media platforms allow stakeholders to participate directly in analysis. However, as with most other analysis methods, social media introduces inherent biases.
Social media is a powerful tool for communication and networking, and it also provides a valuable source of information for analyzing user opinions about infrastructure projects. By using data collected from Twitter, analysts can create networks to represent connections among users, quantify their similarities, and then use those values to predict public opinion. We can also use this information to measure bias – that is, the impact the social media has on the opinions of its users.
Research and analysis show a correlation between user similarity and user opinion that indicates bias. Additionally, I observed that disagreement was stronger than agreement – if users disagreed, they would disagree strongly; if they agreed, they had varying levels of agreement strength. In other words, disagreement was fairly polarizing, but agreement tended not to invoke strong emotions one way or another.
The nearly universal use of social media is a powerful tool to both predict and shape public opinion. Stakeholder managers can predict stakeholder opinion by using their social network connections to determine conformity. And although social media has its own biases, its value as a data source for preliminary planning analysis should not be discounted
Social sampling and expressed attitudes : authenticity preference and social extremeness aversion lead to social norm effects and polarization
A cognitive model of social influence (Social Sampling Theory: SST) is developed and applied to several social network phenomena including polarization and contagion effects. Social norms and individuals’ private attitudes are represented as distributions rather than the single points used in most models. SST is explored using agent-based modeling to link individual-level and network-level effects. People are assumed to observe the behavior of their social network neighbors and thereby infer the social distribution of particular attitudes and behaviors. It is assumed that (a) people dislike behaving in ways that are extreme within their neighborhood social norm (social extremeness aversion assumption), and hence tend to conform and (b) people prefer to behave consistently with their own underlying attitudes (authenticity preference assumption) hence minimizing dissonance. Expressed attitudes and behavior reflect a utility-maximizing compromise between these opposing principles. SST is applied to a number of social phenomena including (a) homophily and the development of segregated neighborhoods, (b) polarization, (c) effects of norm homogeneity on social conformity, (d) pluralistic ignorance and false consensus effects, (e) backfire effects, (f) interactions between world view and social norm effects, and (g) the opposing effects on subjective well-being of authentic behavior and high levels of social comparison. More generally, it is argued that explanations of social comparison require the variance, not just the central tendency, of both attitudes and beliefs about social norms to be accommodated
Generating Strong Diversity of Opinions: Agent Models of Continuous Opinion Dynamics
Opinion dynamics is the study of how opinions in a group of individuals change over time. A goal of opinion dynamics modelers has long been to find a social science-based model that generates strong diversity -- smooth, stable, possibly multi-modal distributions of opinions. This research lays the foundations for and develops such a model. First, a taxonomy is developed to precisely describe agent schedules in an opinion dynamics model. The importance of scheduling is shown with applications to generalized forms of two models. Next, the meta-contrast influence field (MIF) model is defined. It is rooted in self-categorization theory and improves on the existing meta-contrast model by providing a properly scaled, continuous influence basis. Finally, the MIF-Local Repulsion (MIF-LR) model is developed and presented. This augments the MIF model with a formulation of uniqueness theory. The MIF-LR model generates strong diversity. An application of the model shows that partisan polarization can be explained by increased non-local social ties enabled by communications technology