Complex contagion

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Complex contagion is the phenomenon in social networks in which multiple sources of exposure to an innovation are required before an individual adopts the change of behavior. [1] It differs from simple contagion in that unlike a disease, it may not be possible for the innovation to spread after only one incident of contact with an infected neighbor. The spread of complex contagion across a network of people may depend on many social and economic factors; for instance, how many of one's friends adopt the new idea as well as how many of them cannot influence the individual, as well as their own disposition in embracing change.

Contents

Mechanisms

Complex Contagion and the Weakness of Long Ties by Damon Centola of University of Pennsylvania and Michael Macy of Cornell University found that information and disease spread as “simple contagions”, requiring only one contact for transmission, while behaviors typically spread as “complex contagions”, requiring multiples sources of reinforcement to induce adoption. Centola’s work builds on Granovetter’s work on the strength of weak ties and threshold models of collective behavior, as well as Duncan Watts and Steve Strogatz’s work on small world networks. [2] Centola and Macy show that the weak ties and small worlds networks are both very good for spreading simple contagions. However, for complex contagions, weak ties and small worlds can slow diffusion.

Centola and Macy suggest four mechanisms of complex contagion. These properties explain the need for multiple exposures in the spread of contagion:

  1. Coordination. Certain innovations become attractive only when people collectively adopt them. In his book "Change," Centola discusses that social technologies like popular media-sharing platforms such as Twitter and Facebook only become valuable once a critical mass of individuals within your social network begins to use them. Many innovations are costly, especially for early adopters but less so for those who wait. The same holds for participation in collective action.
  2. Credibility. Innovations often lack credibility until adopted by neighbors. Hearing the same story from different people makes it seem less likely that surprising information is nothing more than the fanciful invention of the informant.
  3. Legitimacy. Knowing that a movement exists or that a collective action will take place is rarely sufficient to induce bystanders to join in. Having several close friends participate in an event often greatly increases an individual’s likelihood of also joining, especially for high-risk social movements. Innovators risk being shunned as deviants until there is a critical mass of early adopters, and non-adopters are likely to challenge the legitimacy of the innovation.
  4. Emotional contagion. Most theoretical models of collective behavior – from action theory to threshold models to cybernetics share the basic assumption that there are expressive and symbolic impulses in human behavior that can be communicated and amplified in spatially and socially concentrated gatherings. [1]

Contested vs. uncontested

Uncontested
The spread of the contagion is dependent solely on the number of people you are connected to who are different from your own state. You are not hindered whatsoever by the number of people in the same state as you. Generally, the more neighbors an individual has, the greater the chance of the individual adopting the innovation if the spread is uncontested. [1]
Contested
The spread of the contagion is dependent on both the adamancy of those who are in a different state from your own as well as the countervailing influence of those who share your current state. In this case, the more neighbors an individual has, the smaller the chance of the individual adopting the innovation. [1]

Diffusion and cascading behaviors in networks

Consider a graph of any reasonable size. Node v’s neighbors can be split into two sets: Set A contains v's neighbors who have adopted a new behavior and B is the set of those behaving conservatively. Node v will only adopt the behavior of those in A if at least a q fraction of neighbors follow behavior A. [3]

Cascading – diffusion over the entire network
Consider a set of initial adopters who start with a new behavior A, while every other node starts with behavior B. Nodes then repeatedly evaluate the decision to switch from B to A using a threshold of q. If the resulting cascade of adoptions of A eventually causes every node to switch from B to A, then we say that the set of initial adopters causes a complete cascade at threshold q. Clusters of density d > 1  q are obstacles to cascades across the entire network. [3]

Application and examples

Many interactions happen at a local, rather than a global, level – we often don't care as much about the full population's decisions as about the decisions made by friends and colleagues. For example, in a work setting we may choose technology to be compatible with the people we directly collaborate with, rather than the universally most popular technology. Similarly, we may adopt political views that are aligned with those of our friends, even if they belong to minorities. [3]

Examples

Examples of simple contagion

See also

Related Research Articles

Mark Sanford Granovetter is an American sociologist and professor at Stanford University. He is best known for his work in social network theory and in economic sociology, particularly his theory on the spread of information in social networks known as The Strength of Weak Ties (1973). In 2014 Granovetter was named a Citation Laureate by Thomson Reuters and added to that organization’s list of predicted Nobel Prize winners in economics. Data from the Web of Science show that Granovetter has written both the first and third most cited sociology articles.

An Informationcascade or informational cascade is a phenomenon described in behavioral economics and network theory in which a number of people make the same decision in a sequential fashion. It is similar to, but distinct from herd behavior.

<span class="mw-page-title-main">Diffusion of innovations</span> Theory on how and why new ideas spread

Diffusion of innovations is a theory that seeks to explain how, why, and at what rate new ideas and technology spread. The theory was popularized by Everett Rogers in his book Diffusion of Innovations, first published in 1962. Rogers argues that diffusion is the process by which an innovation is communicated over time among the participants in a social system. The origins of the diffusion of innovations theory are varied and span multiple disciplines.

<span class="mw-page-title-main">Technology adoption life cycle</span> Sociological model

The technology adoption lifecycle is a sociological model that describes the adoption or acceptance of a new product or innovation, according to the demographic and psychological characteristics of defined adopter groups. The process of adoption over time is typically illustrated as a classical normal distribution or "bell curve". The model indicates that the first group of people to use a new product is called "innovators", followed by "early adopters". Next come the early majority and late majority, and the last group to eventually adopt a product are called "Laggards" or "phobics." For example, a phobic may only use a cloud service when it is the only remaining method of performing a required task, but the phobic may not have an in-depth technical knowledge of how to use the service.

<span class="mw-page-title-main">Interpersonal ties</span>

In social network analysis and mathematical sociology, interpersonal ties are defined as information-carrying connections between people. Interpersonal ties, generally, come in three varieties: strong, weak or absent. Weak social ties, it is argued, are responsible for the majority of the embeddedness and structure of social networks in society as well as the transmission of information through these networks. Specifically, more novel information flows to individuals through weak rather than strong ties. Because our close friends tend to move in the same circles that we do, the information they receive overlaps considerably with what we already know. Acquaintances, by contrast, know people that we do not, and thus receive more novel information.

In social dynamics, critical mass is a sufficient number of adopters of a new idea, technology or innovation in a social system so that the rate of adoption becomes self-sustaining and creates further growth. The point at which critical mass is achieved is sometimes referred to as a threshold within the threshold model of statistical modeling.

<span class="mw-page-title-main">Triadic closure</span>

Triadic closure is a concept in social network theory, first suggested by German sociologist Georg Simmel in his 1908 book Soziologie [Sociology: Investigations on the Forms of Sociation]. Triadic closure is the property among three nodes A, B, and C, that if the connections A-B and A-C exist, there is a tendency for the new connection B-C to be formed. Triadic closure can be used to understand and predict the growth of networks, although it is only one of many mechanisms by which new connections are formed in complex networks.

Behavioral contagion is a form of social contagion involving the spread of behavior through a group. It refers to the propensity for a person to copy a certain behavior of others who are either in the vicinity, or whom they have been exposed to. The term was originally used by Gustave Le Bon in his 1895 work The Crowd: A Study of the Popular Mind to explain undesirable aspects of behavior of people in crowds. In the digital age, behavioral contagion is also concerned with the spread of online behavior and information. A variety of behavioral contagion mechanisms were incorporated in models of collective human behavior.

A bridge is a type of social tie that connects two different groups in a social network.

Heterophily, or love of the different, is the tendency of individuals to collect in diverse groups; it is the opposite of homophily. This phenomenon can be seen in relationships between individuals. As a result, it can be analyzed in the workplace to create a more efficient and innovative workplace. It has also become an area of social network analysis.

<span class="mw-page-title-main">Social contagion</span> Spontaneous spread of behavior or emotions among a group

Social contagion involves behaviour, emotions, or conditions spreading spontaneously through a group or network. The phenomenon has been discussed by social scientists since the late 19th century, although much work on the subject was based on unclear or even contradictory conceptions of what social contagion is, so exact definitions vary. Some scholars include the unplanned spread of ideas through a population as social contagion, though others prefer to class that as memetics. Generally social contagion is understood to be separate from the collective behaviour which results from a direct attempt to exert social influence.

<span class="mw-page-title-main">Social network</span> Social structure made up of a set of social actors

A social network is a social structure made up of a set of social actors, sets of dyadic ties, and other social interactions between actors. The social network perspective provides a set of methods for analyzing the structure of whole social entities as well as a variety of theories explaining the patterns observed in these structures. The study of these structures uses social network analysis to identify local and global patterns, locate influential entities, and examine network dynamics.

Three Degrees of Influence is a theory in the realm of social networks, proposed by Nicholas A. Christakis and James H. Fowler in 2007. It has since been explored by scientists in numerous disciplines using diverse statistical, psychological, sociological, and biological approaches.

Networks are crucial parts of any action taken in a marketplace. Peter Drucker even described the future economy as one of a society of networks. Companies embedded in such networks stand to gain a lot. There are a number of different network models, which have distinct relevance to customers, and marketing initiatives. A network in marketing can be formed either strategically or completely randomly. Marketing channels and business networks have been referred to, by Achrol & Kotler as:

“Interdependent systems of organizations and relations that are involved in carrying out all of the production and marketing activities involved in creating and delivering value in the form of products and services to intermediate and final customers.”

Rumor is an important form of social communications, and the spread of rumors plays a significant role in a variety of human affairs. There are two approaches to investigate the rumor spreading process: the microscopic models and the macroscopic models. The macroscopic models propose a macro view about this process are mainly based on the widely used Daley-Kendall and Maki-Thompson models. Particularly, we can view rumor spread as a stochastic process in social networks. While the microscopic models are more interested more on the micro interactions between individuals.

The sociological theory of diffusion is the study of the diffusion of innovations throughout social groups and organizations. The topic has seen rapid growth since the 1990s, reflecting curiosity about the process of social change and "fueled by interest in institutional arguments and in network and dynamic analysis." The theory uses a case study of the growth of business computing to explain different mechanisms of diffusion.

In mathematical modeling of social networks, link-centric preferential attachment is a node's propensity to re-establish links to nodes it has previously been in contact with in time-varying networks. This preferential attachment model relies on nodes keeping memory of previous neighbors up to the current time.

<span class="mw-page-title-main">Global cascades model</span>

Global cascades models are a class of models aiming to model large and rare cascades that are triggered by exogenous perturbations which are relatively small compared with the size of the system. The phenomenon occurs ubiquitously in various systems, like information cascades in social systems, stock market crashes in economic systems, and cascading failure in physics infrastructure networks. The models capture some essential properties of such phenomenon.

Structural holes is a concept from social network research, originally developed by Ronald Stuart Burt. A structural hole is understood as a gap between two individuals who have complementary sources to information. The study of structural holes spans the fields of sociology, economics, and computer science. Burt introduced this concept in an attempt to explain the origin of differences in social capital. Burt’s theory suggests that individuals hold certain positional advantages/disadvantages from how they are embedded in neighborhoods or other social structures.

<span class="mw-page-title-main">Damon Centola</span> Researcher in network science and related at the University of Pennsylvania

Damon Centola is a sociologist and the Elihu Katz Professor of Communication, Sociology and Engineering at the University of Pennsylvania, where he is Director of the Network Dynamics Group and Senior Fellow at the Leonard Davis Institute of Health Economics.

References

  1. 1 2 3 4 5 6 Centola, Damon; Macy, Michael. "Complex Contagions and the Weakness of Long Ties." Archived 2020-11-18 at the Wayback Machine University of Chicago, 2007.
  2. Centola, Damon (2010). "The Spread of Behavior in an Online Social Network Experiment". Science. 329 (5996): 1194–1197. Bibcode:2010Sci...329.1194C. doi:10.1126/science.1185231. PMID   20813952. S2CID   3265637. Archived from the original on 2023-01-23. Retrieved 2023-02-04.
  3. 1 2 3 4 Easley, David; Kleinberg, Jon. Networks, Crowds, and Markets: Reasoning about a Highly Connected World. Archived 2015-03-16 at the Wayback Machine Cambridge University Press, 2010.
  4. Sadagopan, S. (28 March 2011). Differences in the mechanics of information diffusion across topics | Proceedings of the 20th international conference on World wide web. WWW '11. pp. 695–704. doi:10.1145/1963405.1963503. ISBN   9781450306324. S2CID   207186115. Archived from the original on 2020-06-28. Retrieved 2020-08-11.{{cite book}}: |website= ignored (help)