Corpus ID: 7074828

Learning and Forecasting Opinion Dynamics in Social Networks

@inproceedings{De2016LearningAF,
  title={Learning and Forecasting Opinion Dynamics in Social Networks},
  author={Abir De and Isabel Valera and Niloy Ganguly and Sourangshu Bhattacharya and Manuel Gomez-Rodriguez},
  booktitle={NIPS},
  year={2016}
}
Social media and social networking sites have become a global pinboard for exposition and discussion of news, topics, and ideas, where social media users often update their opinions about a particular topic by learning from the opinions shared by their friends. In this context, can we learn a data-driven model of opinion dynamics that is able to accurately forecast opinions from users? In this paper, we introduce SLANT, a probabilistic modeling framework of opinion dynamics, which represents… Expand
Learning Linear Influence Models in Social Networks from Transient Opinion Dynamics
TLDR
This article begins an investigation into a family of novel data-driven influence models that accurately learn and fit realistic observations that are robust to missing observations for several timesteps after an actor has changed its opinion. Expand
Neural opinion dynamics model for the prediction of user-level stance dynamics
TLDR
This paper proposes to use a Recurrent Neural Network to model each user’s posting behaviors on Twitter and incorporate their neighbors’ topic-associated context as attention signals using an attention mechanism for user-level stance prediction. Expand
Shaping Opinion Dynamics in Social Networks
TLDR
Experiments on several synthetic and real datasets gathered from Twitter show that SmartShape can accurately determine the quality of a set of control users as well as shape the opinion dynamics more effectively than several baselines. Expand
SLANT+: A Nonlinear Model for Opinion Dynamics in Social Networks
TLDR
This paper proposes SLANT+, a novel nonlinear generative model for opinion dynamics, by extending the earlier linear opinion model SLANT [7], which relies on a network-guided recurrent neural network architecture which learns a proper temporal representation of the messages as well as the underlying network. Expand
Steering Opinion Dynamics in Information Diffusion Networks
TLDR
This paper proposes a unified multivariate jump diffusion process framework for modeling opinion dynamics over networks and determining the control over such networks, and shows that the framework is robust, able to control both stable and unstable dynamics systems with fast convergence speed, less variance and low control cost. Expand
Learning Linear In uence Models in Social Networks from Transient
Social networks, forums, and social media have emerged as global platforms for forming and shaping opinions on a broad spectrum of topics like politics, sports and entertainment. Users (also calledExpand
A Review on Social Opinion Dynamics Models
This is study of prediction of opinions and understanding of the dynamics of opinions. In recent years people uses social networking sites and Social platforms to express their opinions. InternetExpand
Tracking Dynamics of Opinion Behaviors with a Content-Based Sequential Opinion Influence Model
TLDR
A content-based sequential opinion influence framework is developed and two opinion sentiment prediction models with alternative prediction strategies are proposed and it is found that an individuals influence is correlated to her/his style of expressions. Expand
Demarcating Endogenous and Exogenous Opinion Diffusion Process on Social Networks
TLDR
CherryPick is designed, a novel learning machinery that classifies the opinions and users by solving a joint inference task in message and user set, from a temporal stream of sentiment messages and can precisely determine the quality of a set of control users, which together with the proposed online shaping strategy, consistently steers the opinion dynamics more effectively than several state-of-the-art baselines. Expand
A Distance Measure for the Analysis of Polar Opinion Dynamics in Social Networks
TLDR
The Social Network Distance (SND) is introduced—a distance measure that quantifies the likelihood of evolution of one snapshot of a social network into another snapshot under a chosen model of polar opinion dynamics and is applicable to large-scale online social networks. Expand
...
1
2
3
4
5
...

References

SHOWING 1-10 OF 41 REFERENCES
Modeling opinion dynamics in social networks
TLDR
It is shown that consensus and polarization of opinions arise naturally in this model under easy to interpret initial conditions on the network, and this leads to a new, nuanced model that is referred to as the BVM. Expand
Steering Opinion Dynamics in Information Diffusion Networks
TLDR
This paper proposes a unified multivariate jump diffusion process framework for modeling opinion dynamics over networks and determining the control over such networks, and shows that the framework is robust, able to control both stable and unstable dynamics systems with fast convergence speed, less variance and low control cost. Expand
Learning a Linear Influence Model from Transient Opinion Dynamics
TLDR
Novel algorithms to estimate edge influence strengths from an observed series of opinion values at nodes, adopting a linear (but not stochastic) influence model are presented. Expand
COEVOLVE: A Joint Point Process Model for Information Diffusion and Network Co-evolution
TLDR
This work proposes a temporal point process model, COEVOLVE, allowing the intensity of one process to be modulated by that of the other, and develops a convex optimization framework to learn the parameters of the model from historical diffusion and network evolution traces. Expand
Shaping Social Activity by Incentivizing Users
TLDR
This paper model social events using multivariate Hawkes processes, which can capture both endogenous and exogenous event intensities, and derive a time dependent linear relation between the intensity of exogenous events and the overall network activity. Expand
Voting models in random networks
TLDR
This paper considers a particular model of interaction between a group of individuals connected through a network of acquaintances and shows how different updating rule of the agent' state lead to different emerging patterns, namely, agreement and disagreement. Expand
Tweetin' in the Rain: Exploring Societal-Scale Effects of Weather on Mood
TLDR
Using machine learning techniques on the Twitter corpus correlated with the weather at the time and location of the tweets, it is found that aggregate sentiment follows distinct climate, temporal, and seasonal patterns. Expand
Sentiment Prediction Using Collaborative Filtering
TLDR
This paper presents a novel, collaborative filtering-based approach for sentiment prediction in twitter conversation threads that assumes a set of sentiment holders and sentiment targets knows the true sentiments for a small fraction of holder-target pairs. Expand
How Bad is Forming Your Own Opinion?
TLDR
A tight bound on the cost at equilibrium relative to the optimum is provided, a connection between these agreement models and extremal problems for generalized eigenvalues is drawn and a natural network design problem is considered in this setting, where adding links to the underlying network can reduce the cost of disagreement at equilibrium. Expand
Binary Opinion Dynamics with Stubborn Agents
TLDR
It is shown that the presence of stubborn agents with opposing opinions precludes convergence to consensus; instead, opinions converge in distribution with disagreement and fluctuations. Expand
...
1
2
3
4
5
...