Estimating Social Influence from Observational Data

• URL: http://arxiv.org/abs/2204.01633v1
• Date: Thu, 24 Mar 2022 20:21:24 GMT
• Title: Estimating Social Influence from Observational Data
• Authors: Dhanya Sridhar and Caterina De Bacco and David Blei
• Abstract summary: We consider the problem of estimating social influence, the effect that a person's behavior has on the future behavior of their peers. Key challenge is that shared behavior between friends could be equally explained by influence or by two other confounding factors. This paper addresses the challenges of estimating social influence with three contributions.
• Score: 5.156484100374057
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We consider the problem of estimating social influence, the effect that a person's behavior has on the future behavior of their peers. The key challenge is that shared behavior between friends could be equally explained by influence or by two other confounding factors: 1) latent traits that caused people to both become friends and engage in the behavior, and 2) latent preferences for the behavior. This paper addresses the challenges of estimating social influence with three contributions. First, we formalize social influence as a causal effect, one which requires inferences about hypothetical interventions. Second, we develop Poisson Influence Factorization (PIF), a method for estimating social influence from observational data. PIF fits probabilistic factor models to networks and behavior data to infer variables that serve as substitutes for the confounding latent traits. Third, we develop assumptions under which PIF recovers estimates of social influence. We empirically study PIF with semi-synthetic and real data from Last.fm, and conduct a sensitivity analysis. We find that PIF estimates social influence most accurately compared to related methods and remains robust under some violations of its assumptions.

Related papers

• Investigating Role of Big Five Personality Traits in Audio-Visual Rapport Estimation [2.6163803834101054]
  We investigate whether the estimation performance of rapport can be improved by using the participant's personality traits as the model's input. Our experimental results show that adding Big Five features (BFFs) to nonverbal features can improve the estimation performance of self-reported rapport. Our study is the first step toward understanding why personality-aware estimation models of interpersonal perception accomplish high estimation performance.
  arXiv  Detail & Related papers  (2024-10-07T08:52:33Z)
• Decoding Susceptibility: Modeling Misbelief to Misinformation Through a Computational Approach [61.04606493712002]
  Susceptibility to misinformation describes the degree of belief in unverifiable claims that is not observable. Existing susceptibility studies heavily rely on self-reported beliefs. We propose a computational approach to model users' latent susceptibility levels.
  arXiv  Detail & Related papers  (2023-11-16T07:22:56Z)
• Decoding the Silent Majority: Inducing Belief Augmented Social Graph with Large Language Model for Response Forecasting [74.68371461260946]
  SocialSense is a framework that induces a belief-centered graph on top of an existent social network, along with graph-based propagation to capture social dynamics. Our method surpasses existing state-of-the-art in experimental evaluations for both zero-shot and supervised settings.
  arXiv  Detail & Related papers  (2023-10-20T06:17:02Z)
• Flexible social inference facilitates targeted social learning when rewards are not observable [58.762004496858836]
  Groups coordinate more effectively when individuals are able to learn from others' successes. We suggest that social inference capacities may help bridge this gap, allowing individuals to update their beliefs about others' underlying knowledge and success from observable trajectories of behavior.
  arXiv  Detail & Related papers  (2022-12-01T21:04:03Z)
• The world seems different in a social context: a neural network analysis of human experimental data [57.729312306803955]
  We show that it is possible to replicate human behavioral data in both individual and social task settings by modifying the precision of prior and sensory signals. An analysis of the neural activation traces of the trained networks provides evidence that information is coded in fundamentally different ways in the network in the individual and in the social conditions.
  arXiv  Detail & Related papers  (2022-03-03T17:19:12Z)
• Empirical Estimates on Hand Manipulation are Recoverable: A Step Towards Individualized and Explainable Robotic Support in Everyday Activities [80.37857025201036]
  Key challenge for robotic systems is to figure out the behavior of another agent. Processing correct inferences is especially challenging when (confounding) factors are not controlled experimentally. We propose equipping robots with the necessary tools to conduct observational studies on people.
  arXiv  Detail & Related papers  (2022-01-27T22:15:56Z)
• SSAGCN: Social Soft Attention Graph Convolution Network for Pedestrian Trajectory Prediction [59.064925464991056]
  We propose one new prediction model named Social Soft Attention Graph Convolution Network (SSAGCN) SSAGCN aims to simultaneously handle social interactions among pedestrians and scene interactions between pedestrians and environments. Experiments on public available datasets prove the effectiveness of SSAGCN and have achieved state-of-the-art results.
  arXiv  Detail & Related papers  (2021-12-05T01:49:18Z)
• Causal Effect Estimation using Variational Information Bottleneck [19.6760527269791]
  Causal inference is to estimate the causal effect in a causal relationship when intervention is applied. We propose a method to estimate Causal Effect by using Variational Information Bottleneck (CEVIB)
  arXiv  Detail & Related papers  (2021-10-26T13:46:12Z)
• Conductance and Social Capital: Modeling and Empirically Measuring Online Social Influence [9.556358888163983]
  Social influence pervades our everyday lives and lays the foundation for complex social phenomena. Existing literature studying online social influence suffers from several drawbacks. This work bridges the gap and presents three contributions towards modeling and empirically quantifying online influence.
  arXiv  Detail & Related papers  (2021-10-25T01:05:49Z)
• Expertise and confidence explain how social influence evolves along intellective tasks [10.525352489242396]
  We study interpersonal influence in small groups of individuals who collectively execute a sequence of intellective tasks. We report empirical evidence on theories of transactive memory systems, social comparison, and confidences on the origins of social influence. We propose a cognitive dynamical model inspired by these theories to describe the process by which individuals adjust interpersonal influences over time.
  arXiv  Detail & Related papers  (2020-11-13T23:48:25Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.