An official website of the United States government
Large cascades can develop in online social networks as people share information with one another. Though simple reshare cascades have been studied extensively, the full range of cascading behaviors on social media is much more diverse. Here we study how diffusion protocols, or the social exchanges that enable information transmission, affect cascade growth, analogous to the way communication protocols define how information is transmitted from one point to another. Studying 98 of the largest information cascades on Facebook, we find a wide range of diffusion protocols - from cascading reshares of images, which use a simple protocol of tapping a single button for propagation, to the ALS Ice Bucket Challenge, whose diffusion protocol involved individuals creating and posting a video, and then nominating specific others to do the same. We find recurring classes of diffusion protocols, and identify two key counterbalancing factors in the construction of these protocols, with implications for a cascade's growth: the effort required to participate in the cascade, and the social cost of staying on the sidelines. Protocols requiring greater individual effort slow down a cascade's propagation, while those imposing a greater social cost of not participating increase the cascade's adoption likelihood. The predictability of transmission also varies with protocol. But regardless of mechanism, the cascades in our analysis all have a similar reproduction number (≈1.8), meaning that lower rates of exposure can be offset with higher per-exposure rates of adoption. Last, we show how a cascade's structure can not only differentiate these protocols, but also be modeled through branching processes. Together, these findings provide a framework for understanding how a wide variety of information cascades can achieve substantial adoption across a network.
more »
« less
Information Cascades and Social Learning
Social learning is the updating of beliefs based on observation of others. Such observation can lead to efficient aggregation of information, but also to inaccurate decisions, fragility of mass behaviors, and, in the case of information cascades, to complete blockage of learning. We review the theory of information cascades and social learning and discuss important themes, insights, and applications of this literature as it has developed over the last 30 years. We also highlight open questions and promising directions for further theoretical and empirical exploration. (JEL D71, D82, D83, D91, Z13)
more »
« less
- Award ID(s):
- 1944153
- PAR ID:
- 10625492
- Publisher / Repository:
- American Economic Association
- Date Published:
- Journal Name:
- Journal of Economic Literature
- Volume:
- 62
- Issue:
- 3
- ISSN:
- 0022-0515
- Page Range / eLocation ID:
- 1040 to 1093
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
The standard assumption in social learning environments is that agents learn from others through choice outcomes. We argue that in many settings, agents can also infer information from others’ response times (RT), which can increase efficiency. To investigate this, we conduct a standard information cascade experiment and find that RTs do contain information that is not revealed by choice outcomes alone. When RTs are observable, subjects extract this private information and are more likely to break from incorrect cascades. Our results suggest that in environments where RTs are publicly available, the information structure may be richer than previously thought.more » « less
-
Direct exposure to stimuli in their environment is not the only way that animals learn about important information. Individuals can infer fear from a social context through observation. Like humans, rats are very social animals, and may learn to infer information about their environment through their interactions with conspecifics. Here, we first review different models for social transmission of information in rodents. Second, we examine different modes of communication that are important to social learning. Then, we cover the different proximate factors that are thought to modulate the social transmission of information. Next, we identify social and environmental conditions that impact social learning, and finally, we conclude by revisiting social transmission through the lens of the Tinbergen framework.more » « less
-
null (Ed.)Recently, aligning users among different social networks has received significant attention. However, most of the existing studies do not consider users’ behavior information during the aligning procedure and thus still suffer from the poor learning performance. In fact, we observe that social network alignment and behavior analysis can benefit from each other. Motivated by such an observation, we propose to jointly study the social network alignment problem and user behavior analysis problem. We design a novel end-to-end framework named BANANA. In this framework, to leverage behavior analysis for social network alignment at the distribution level, we design an earth mover’s distance based alignment model to fuse users’ behavior information for more comprehensive user representations. To further leverage social network alignment for behavior analysis, in turn, we design a temporal graph neural network model to fuse behavior information in different social networks based on the alignment result. Two models above can work together in an end-to-end manner. Through extensive experiments on real-world datasets, we demonstrate that our proposed approach outperforms the state-of-the-art methods in the social network alignment task and the user behavior analysis task, respectively.more » « less
-
Recent years have witnessed tremendous interest in understanding and predicting information spread on social media platforms such as Twitter, Facebook, etc. Existing diffusion prediction methods primarily exploit the sequential order of influenced users by projecting diffusion cascades onto their local social neighborhoods. However, this fails to capture global social structures that do not explicitly manifest in any of the cascades, resulting in poor performance for inactive users with limited historical activities. In this paper, we present a novel variational autoencoder framework (Inf-VAE) to jointly embed homophily and influence through proximity-preserving social and position-encoded temporal latent variables. To model social homophily, Inf-VAE utilizes powerful graph neural network architectures to learn social variables that selectively exploit the social connections of users. Given a sequence of seed user activations, Inf-VAE uses a novel expressive co-attentive fusion network that jointly attends over their social and temporal variables to predict the set of all influenced users. Our experimental results on multiple real-world social network datasets, including Digg, Weibo, and Stack-Exchanges demonstrate significant gains (22% MAP@10) for Inf-VAE over state-of-the-art diffusion prediction models; we achieve massive gains for users with sparse activities, and users who lack direct social neighbors in seed sets.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1257/jel.20241472
