360° Stance Detection aggregates diverse news perspectives for unbiased opinions.
problem Forming unbiased opinions in the age of fake news and filter bubbles.
method Aggregates news with multiple perspectives on a topic and presents them on a spectrum.
result Users can base their opinion on multiple pieces of diverse evidence.
Improved speech enhancement with larger neural networks using novel embeddings and biases.
problem Decreased robustness of speech enhancement models to real-world use cases.
method Frequency-positional embeddings, semi-supervised training, biased loss function.
result Better performance on real recordings with improved large neural network architecture.
Bayesian truth discovery uses social network info to improve reliability estimates.
problem Truth discovery from unreliable or biased agents with correlated biases in the same community.
method Laplace variational inference and stochastic variational inference for large networks.
result Our methods outperform other inference methods in sparse observation scenarios.
Study reveals why polls were inaccurate in 2016 US election.
problem Why opinion polls were inaccurate in 2016 US election.
method Proposes clean sensing framework for optimal parameter estimation from heterogeneous data sources.
result Optimal polling strategy allocates resources based on data quality and quantity.
A DL model predicts dynamic uncertain opinions in network data.
problem Predicting uncertain opinions in real-world dynamic network data.
method Integrates Graph Convolutional Network (GCN), Gated Recurrent Units (GRU), and robust statistics.
result Outperforms existing methods in predicting dynamic, uncertain opinions.
Balance corrects biased survey data for more accurate insights.
problem Bias in survey data leads to inaccurate insights and underperforming models.
method Three steps: bias understanding, weight adjustment, and evaluation.
result Corrected data leads to more accurate ML model training and insights.
Study examines complexity of opinions in online discussions.
problem Understanding the complexity and similarity of opinions in online discussions.
method Developed a modeling framework to construct latent representations of opinions, connecting them to voting models and sign-rank of a matrix.
result Found that unidimensional models often fail to accurately represent online discussions, revealing insights into human judgements and opinions.
The paper proposes a method to evaluate ML models for subjective inference, focusing on sentence toxicity.
problem Bias in ML models for subjective inference, especially in real-life applications.
method Proposes a list of specifications to evaluate ML models for subjective inference, illustrated with a sentence toxicity example.
result Demonstrates the importance of considering subjectivity and bias in evaluating ML models.
The paper studies expert opinions in financial markets using diffusion approximations.
problem Estimating hidden drift in financial markets with expert opinions.
method Investigates asymptotic behavior of filter for high-frequency expert opinions, derives diffusion approximations.
result Expert opinions can be approximated by a diffusion process, simplifying utility maximization problems.
FAML addresses biased evidence learning in multi-view learning, improving fairness and prediction reliability.
problem Biased evidence learning in multi-view learning, leading to unreliable uncertainty estimation.
method FAML introduces an adaptive prior and fairness constraint to balance evidence allocation across views.
result FAML enhances fairness and improves prediction reliability compared to state-of-the-art methods.
The paper introduces a method to incorporate expert opinion on observable quantities into statistical models.
problem Tackling the challenge of integrating expert knowledge on observable quantities into statistical models.
method The approach involves updating a prior belief using a loss function that reflects expert opinion on observable quantities.
result The method allows for a flexible specification of expert opinion and is straightforward to implement.
Analysis of opinion dynamics in social networks plays an important role in today's life. For applications such as predicting users' political preference, it is particularly important to be able to analyze the dynamics of competing opinions. While observing the evolution of polar opinions of a social network's users ove…
Paper uses Twitter data to analyze public opinion on economic issues during elections.
problem Limited, expensive, and time-consuming surveys for economic issues.
method Combines sentiment analysis and topic modeling for Twitter data.
result Effective analysis of economic concerns during the 2012 US presidential election.
Study improves fair opinion aggregation by balancing voter attributes.
problem Aggregation of opinions can be biased by voter attributes.
method Combines majority voting and D&S model with fairness options.
result Effective combination of Soft D&S and fairness options for different data types.
ChatGPT improves financial reasoning, overcoming biases in gold investment.
problem Improving financial reasoning and overcoming biases in investment decisions.
method Applied advanced prompt engineering and semantic news information to enhance LLMs' performance.
result ChatGPT with CoT prompt provides more explainable predictions and higher investment returns.
Paper proposes spamGAN to detect and generate opinion spam using limited labeled data.
problem Detecting and preventing opinion spam in online reviews with limited labeled data.
method Generative adversarial network (GAN) trained on semi-supervised data.
result spamGAN outperforms existing techniques in detecting opinion spam with limited labeled data.
Opinions and beliefs determine the evolution of social systems. This is of particular interest in finance, as the increasing complexity of financial systems is coupled with information overload. Opinion formation, therefore, is not always the result of optimal information processing. On the contrary, agents are bounded…
SOLA framework predicts structured outputs with abstention for opinion analysis.
problem Accurate opinion prediction in structured outputs.
method Decomposes problem into structured abstention and prediction, uses asymmetric losses, surrogate regression, and pre-image problem.
result Extends statistical guarantees on excess risk for fine-grained opinion mining.
New ML fairness measure excludes subjective opinions.
problem Lack of adaptation of ML models to specific applications.
method Formalised new notion of unfairness, quantified and visualised.
result Insights into ML systems' functioning to mitigate unfairness.
Optimal investment strategy with expert opinions in uncertain conditions.
problem Optimizing wealth in a model with unobservable drift and costly expert opinions.
method Embedding into a full information problem, using viscosity solutions and stochastic Perron's method.
result Constructing optimal trading and expert opinion strategies under sufficient regularity conditions.
Crowd opinions in microblogs can predict event outcomes, matching with expert opinions.
problem Utilizing crowd wisdom for event outcome prediction in microblogs.
method Multi-label sentiment classification of tweets to gauge crowd opinion and compare with expert predictions.
result Crowd opinions in microblogs often match with expert opinions, especially in non-debate events.
The paper studies how expert opinions improve stock return predictions in a market with a hidden drift.
problem Improving stock return predictions in a market with a hidden Gaussian drift.
method Uses Kalman filter techniques to estimate the hidden drift from noisy expert opinions and stock returns.
result The Kalman filter estimates of the drift converge to the hidden drift as the frequency of expert opinions increases.
The paper develops a method for inferring second opinions from experts using counterfactual inference.
problem Designing efficient decision support systems for second opinions.
method Set invariant Gumbel-Max structural causal model for multiclass classification.
result The proposed model can infer second opinions more accurately than non-causal models.
A new method combines experts' opinions to train regression models with noisy labels.
problem Training regression models with noisy labels from multiple experts.
method Estimate each labeler's expertise and combine opinions using learned weights.
result Empirically outperforms existing techniques on simulated and real data.
The paper studies and mitigates bias in recommender systems.
problem Bias in recommender systems caused by user feedback loops.
method Modeling user exposure and proposing debiasing strategies.
result Recommender systems are biased and depend on user exposure.
SINN combines social science and deep learning for predicting opinion dynamics.
problem Predicting opinion dynamics in social networks using traditional models requires extensive calibration with real data.
method SINN integrates theoretical models and social media data using physics-informed neural networks (PINNs) and matrix factorization.
result SINN outperforms six baseline methods in predicting opinion dynamics on real-world and synthetic datasets.
Well-defined formal definitions for sentiment and opinion are extended to incorporate the necessary elements to provide a formal quantitative definition of reputation. This definition takes the form of a time-based index, in which each element is a function of a collection of opinions mined during a given time period. …
Study shows social reinforcement learning can lead to persistent but metastable polarization.
problem Understanding the persistence and dynamics of opinion polarization.
method Simulation and mathematical analysis of a reinforcement learning model of opinion dynamics.
result Polarization observed in the model is metastable and will eventually lead to consensus.
We propose a simple model for the behaviour of longterm investors on a stock market, consisting of three particles, which represent the current price of the stock and the opinion of the buyers, respectively sellers, about the right trading price. As time evolves, both groups of traders update their opinions with respec…
Models predict uncertainty to help identify patients needing second opinions.
problem Disagreements among medical experts, especially in diagnosis.
method Direct Uncertainty Prediction (DUP) training to predict uncertainty scores directly from patient features.
result DUP outperforms Uncertainty Via Classification in identifying cases needing second opinions.
Unsupervised summarization generates novel reviews reflecting consensus opinions.
problem Creating summaries that reflect subjective information in multiple documents.
method Generative model with hierarchical variational autoencoder, pointer-generator mechanism.
result Model produces fluent and coherent summaries reflecting common opinions.
Method infers trader lead-lag networks to reveal market dynamics.
problem Understanding herding and liquidity in financial markets.
method Kinetic Ising model and inference algorithm to reconstruct trader opinions.
result Identifies leading players and links herding to liquidity.
This paper investigates optimal trading strategies in a financial market with multidimensional stock returns where the drift is an unobservable multivariate Ornstein-Uhlenbeck process. Information about the drift is obtained by observing stock returns and expert opinions. The latter provide unbiased estimates on the cu…
Study market efficiency under partial information using SDEs and optimization.
problem Market efficiency under partial information constraints.
method McKean-Vlasov-type SDEs, Wasserstein barycenters, KL divergence, convex optimization, optimal control, nonlinear filtering.
result Convergence of reduced-information market price processes to true price process under increasing information flow.
The dynamics of market prices is described as the evolution of opinions in the trading community regarding future market behavior. The price then is a function of the voting process of the market players in favor to raise or reduce the value of a stock. The model presented in this paper is suited for pricing of options…
Modeling financial market dynamics with noise and fundamentalist agents.
problem Understanding opinion formation and market behavior in financial markets.
method Agent-based model with Erdös-Rényi random graph structure, incorporating anxiety parameter.
result Model accurately reproduces key market features like fat-tailed returns and volatility clustering.
Financial statements contain quantitative information and manager's subjective evaluation of firm's financial status. Using information released in U.S. 10-K filings. Both qualitative and quantitative appraisals are crucial for quality financial decisions. To extract such opinioned statements from the reports, we built…
An increasing number of people are using online social networking services (SNSs), and a significant amount of information related to experiences in consumption is shared in this new media form. Text mining is an emerging technique for mining useful information from the web. We aim at discovering in particular tweets s…
In the spirit of behavioral finance, we study the process of opinion formation among investors using a variant of the 2D Voter Model with a tunable social temperature. Further, a feedback acting on the temperature is introduced, such that social temperature reacts to market imbalances and thus becomes time dependent. I…
We propose a stochastic map model of economic dynamics. In the last decade, an array of observations in economics has been investigated in the econophysics literature, a major example being the universal features of inequality in terms of income and wealth. Another area of inquiry is the formation of opinion in a socie…
This paper improves multi-label classification of user reactions in online news.
problem Classifying user reactions in online news with multi-label classification.
method Problem transformation methods combined with different classification algorithms.
result Classifier Chains method combined with Random Forest achieved the highest correct predictions.
A new platform models how narratives influence financial markets.
problem Explaining irrational market behaviors through narratives.
method Integrated opinion dynamics and agent-based modeling.
result Initial results show how narratives shape financial outcomes.
Research analyzes public opinions on Twitter about diabetes, diet, exercise, and obesity.
problem Understanding public health opinions on social media.
method Developed a multi-component semantic and linguistic framework to collect and analyze Twitter data.
result Strongest correlation found between exercise and obesity; other notable correlations between diabetes and obesity, diet and obesity.
This work uses variational inference to estimate parameters of opinion dynamics models.
problem Challenges in parameter estimation for ABMs of social phenomena.
method Transformed ABM parameter estimation into an optimization problem using variational inference.
result Estimates parameters more accurately than simulation-based and MCMC methods.
Analyzes Twitter users' opinions on self-driving cars.
problem Understanding public perception of self-driving cars.
method Annotated Twitter dataset, topic modeling, sentiment classification using Twitter features.
result People are generally optimistic but also concerned about self-driving cars.
Bayesian model fuses multiple classifiers with explicit correlation modeling.
problem Combining outputs of multiple classifiers with explicit correlation.
method Hierarchical Bayesian model with correlated Dirichlet distribution.
result Fused classifier performance can be Bayes optimal even for highly correlated base classifiers.
To quantify an operational risk capital charge under Basel II, many banks adopt a Loss Distribution Approach. Under this approach, quantification of the frequency and severity distributions of operational risk involves the bank's internal data, expert opinions and relevant external data. In this paper we suggest a new …
The paper optimizes portfolios in a market with hidden drift and random expert opinions.
problem Optimizing portfolios in a market with hidden Gaussian drift and random expert signals.
method Modeling the hidden drift using Kalman filters and solving the utility maximization problem with dynamic programming.
result Derivation of optimal portfolio weights and utility maximization under the given market conditions.