# 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

#### 68 Citations

Learning Linear Influence Models in Social Networks from Transient Opinion Dynamics

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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

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- AAMAS
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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

- Computer Science
- 2017 IEEE International Conference on Data Mining (ICDM)
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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

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- ArXiv
- 2016

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

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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

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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

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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

#### References

SHOWING 1-10 OF 41 REFERENCES

Modeling opinion dynamics in social networks

- Computer Science
- WSDM
- 2014

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

- Computer Science
- ArXiv
- 2016

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

- Computer Science
- CIKM
- 2014

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

- Computer Science, Physics
- NIPS
- 2015

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

- Computer Science, Medicine
- NIPS
- 2014

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

- Mathematics, Computer Science
- 2010 Information Theory and Applications Workshop (ITA)
- 2010

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

- Computer Science
- ICWSM
- 2012

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

- Computer Science
- ICWSM
- 2013

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?

- Computer Science, Physics
- 2011 IEEE 52nd Annual Symposium on Foundations of Computer Science
- 2011

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

- Mathematics, Computer Science
- TEAC
- 2013

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