SPMF improves social recommendation by considering trust and preference domains.
problem Ignoring trust and preference domain differences in social recommendations.
method SPMF uses matrix factorization with trust and preference segmentation.
result SPMF outperforms state-of-the-art recommendation algorithms.
New method for online influence maximization in social networks.
problem Identifying influential nodes in social networks.
method Factorization of activation probabilities into latent factors on nodes, using upper confidence bound online learning.
result Significant reduction in regret with proposed algorithm.
Proposes a trust model for SIoT nodes using Hellinger distance and matrix factorization.
problem Trust management in SIoT to reduce risk from malicious nodes.
method Flexible bipartite graph, Hellinger distance, centrality, similarity measures, matrix factorization.
result The proposed trust prediction mechanism outperforms existing methods in accuracy and resilience.
Online voting is an emerging feature in social networks, in which users can express their attitudes toward various issues and show their unique interest. Online voting imposes new challenges on recommendation, because the propagation of votings heavily depends on the structure of social networks as well as the content …
Deep learning predicts stock movements using social media data.
problem Predicting stock movements with traditional methods is challenging.
method Graph neural network combining financial data and social media.
result Improvement of 28% in cumulative returns.
The paper analyzes frameworks for integrating sustainability into investment decisions.
problem Understanding how ESG factors influence investment choices.
method Examined and analyzed various theoretical frameworks including Behavioral Finance, Modern Portfolio, and Risk Management.
result Investors increasingly integrate ESG factors to optimize financial outcomes and societal goals.
Network analysis helps prevent money laundering by identifying risky clients and suspicious clusters.
problem Preventing money laundering using social network analysis.
method Real-world data analysis, network metrics, predictive models, visual analysis.
result Risk profiles can be predicted using social network metrics.
NAS model improves social recommendation accuracy using neural attention.
problem Capturing and weighing friends' preferences in social recommendation systems.
method Proposes a Neural Attention mechanism (NAS) for Social collaborative filtering.
result NAS model outperforms state-of-the-art methods in publicly available datasets.
Matrix factorization is a key component of collaborative filtering-based recommendation systems because it allows us to complete sparse user-by-item ratings matrices under a low-rank assumption that encodes the belief that similar users give similar ratings and that similar items garner similar ratings. This paradigm h…
We consider a simple stochastic differential equation for modeling bubbles in social context. A prime example is bubbles in asset pricing, but similar mechanisms may control a range of social phenomena driven by psychological factors (for example, popularity of rock groups, or a number of students pursuing a given majo…
Social media reduces individual investors' disposition effect through negative information.
problem The disposition effect in individual investors selling profitable assets too early and holding onto losing assets for too long.
method Analysis of post data and trading data from Xueqiu.com.
result Social media information significantly reduces the disposition effect.
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.
Study explores factors influencing saving behavior among Dhaka employees.
problem Factors influencing saving behavior among Dhaka employees.
method Quantitative approach with cross-sectional survey design, structured questionnaire, descriptive statistics, reliability analysis, regression analysis.
result Only financial management practices had a significant positive relationship with saving behavior.
Stocks of more resilient firms outperformed during the pandemic, reflecting disaster risk.
problem The impact of social distancing on firms' operations and stock performance.
method Cross-sectional analysis of firms' resilience and stock performance, controlling for risk factors.
result Stocks of more resilient firms are expected to yield significantly lower returns than less resilient ones, reflecting disaster risk.
We consider factoring low-rank tensors in the presence of outlying slabs. This problem is important in practice, because data collected in many real-world applications, such as speech, fluorescence, and some social network data, fit this paradigm. Prior work tackles this problem by iteratively selecting a fixed number …
Paper tackles fair low-rank approximation and column subset selection.
problem Minimize loss over sub-populations in machine learning.
method Developed algorithms for fair low-rank approximation and fair column subset selection.
result Achieved polynomial time algorithms for fair low-rank approximation.
Understanding tie strength in social networks, and the factors that influence it, have received much attention in a myriad of disciplines for decades. Several models incorporating indicators of tie strength have been proposed and used to quantify relationships in social networks, and a standard set of structural networ…
Model predicts community health outcomes from social media language.
problem Linking social media language to community health outcomes.
method Sentence embeddings, regression model, clustering, without additional data.
result Predicts community-level medical outcomes from social media language.
Integrates ESG factors into Bachelier's model for asset pricing.
problem Incorporating ESG factors into classical finance models.
method Defines ESG price process and integrates into Bachelier's model.
result Enables option pricing valuation with ESG factors.
Spatial ABM predicts housing market trends in Sydney.
problem Inadequate spatial modeling in housing market forecasts.
method Graph-based spatial agent-based model incorporating social and economic factors.
result Model accurately predicts market trends and local area-specific forecasts.
Study predicts U.S. county COVID-19 growth using demographic and social distancing data.
problem Predicting county-level COVID-19 growth during the pandemic.
method Spectral clustering, correlation matrix, demographic features, social distancing scores, LSTM model.
result Effective prediction of future county growth using demographic and social distancing data.
Study uses social network data to analyze regional inflation trends.
problem Analyzing inflation trends using social media data.
method BERT neural networks for identifying pro-inflationary and disinflationary keywords.
result Models can visualize and classify inflationary keywords in different contexts.
New method evaluates multiple social disparities using machine learning.
problem Reduction of educational disparities across multiple dimensions.
method Triply-Robust Machine Learning Approach for Causal Decomposition Analysis.
result Simultaneous interventions across multiple domains reduce disparities.
The paper models crime risk using Foursquare check-ins and mobility data.
problem Understanding and predicting crime risk in urban areas.
method Directed graph of aggregated movement data, region risk factor derivation, DIFFER features.
result Reliable correlations between DIFFER features and crime count observed.
Develops platforms to analyze social media data for human behavior and emotions.
problem Understanding human behavior and emotions from social media data.
method Self-structuring incremental machine learning, event detection, natural language processing.
result Captured salient topics and events from social media data, validated against news.
Modeling true and false news diffusion in social networks using homogeneity.
problem Difficulties in distinguishing true from false news in social networks.
method Proposes a Bayesian nonparametric model that incorporates homogeneity of news stories to predict their genuineness.
result Homogeneity values of news stories strongly correlate with their genuineness and content.
Study examines how social media sentiment impacts biotech stocks.
problem Understanding the impact of social media on biotech stock prices.
method VADER sentiment analysis, ARIMA, and VAR models were used to forecast stock market performance.
result Complex interplay between tweet sentiment and stock market performance was identified.
Gang violence is a severe issue in major cities across the U.S. and recent studies [Patton et al. 2017] have found evidence of social media communications that can be linked to such violence in communities with high rates of exposure to gang activity. In this paper we partnered computer scientists with social work rese…
Detects anomalous behavior in social media users by analyzing content and connections.
problem Identifying disruptive patterns in user behavior on social media platforms.
method Joint representation learning of content and connection to detect anomalous behavior.
result Observed densely connected users engaging in local politics and exhibiting troll-like behavior.
Add antidote data to improve polarization and fairness in recommender systems.
problem Improving the social desirability of recommender system outputs.
method Formalize antidote data problem, develop optimization-based solutions, and propose measures for polarization and fairness.
result A modest budget for antidote data can lead to significant improvements in the polarization or fairness of recommendations.
The study identifies factors predicting stock returns and maximum drawdown using various models.
problem Predicting stock returns and maximum drawdown in the US equity market.
method Supervised learning with multiple models (OLS, penalized linear regressions, tree-based models, neural networks) over 49 years of data.
result Non-linear models outperformed linear models in predicting stock returns and maximum drawdown, especially during calm periods.
Paper uses NLP and IRT to score ESG factors from news articles.
problem Lack of precise ESG metrics in finance.
method Combines NLP and IRT models on ESG-related news data.
result Method offers more precise ESG metrics with temporal dynamics.
FATE predicts user engagement on social apps with explainable explanations.
problem Accurate user engagement prediction for social apps with explainability.
method FATE, a flexible neural framework incorporating friendships, actions, and temporal dynamics.
result FATE outperforms state-of-the-art approaches by 10% error and 20% runtime reduction.
Study measures inequality in social-economic systems using Fokker-Planck equations and Lotka-Volterra dynamics.
problem Measuring inequality in oscillatory social-economic systems described by Fokker-Planck equations and Lotka-Volterra dynamics.
method Used Fokker-Planck equations and Lotka-Volterra dynamics to model inequality, focusing on coefficient of variation as a measure.
result Inequality initially tends to decrease in oscillatory systems, contrary to steady-state models.
We look at a collection of conjectures with the unifying message that smaller social systems, tend to be less complex and can be aligned better, towards fulfilling their intended objectives. We touch upon a framework, referred to as the four pronged approach that can aid the analysis of social systems. The four prongs …
Proposes a new algorithm for graph-based semi-parametric contextual bandits.
problem Non-stationarity in human behavior and social interaction.
method SemiGraphTS algorithm for graph-based semi-parametric reward model.
result Derives an upper bound of cumulative regret for graph-based semi-parametric model.
Computer science scans LLMs to understand and manipulate their economic forecasts.
problem Understanding and controlling the reasoning of large language models in economics.
method Brain scanning techniques applied to LLMs to identify and manipulate underlying concepts.
result LLMs can be steered to generate forecasts with specific biases, allowing for correction or simulation.
Optimistic Hedge achieves optimal regret bounds in two-player zero-sum games.
problem Achieving optimal regret bounds for optimistic Hedge in two-player zero-sum games.
method Refined regret analysis and optimization problem formulation.
result Optimistic Hedge achieves O ( log m log n ) O(\sqrt{\log m \log n}) O ( log m log n ) regret bounds, matching upper and lower bounds. New method discovers context effects in choice data.
problem Identifying context effects from choice data is challenging.
method Automatic discovery of context effects from observed choices.
result Automatic discovery of context effects from observed choices.
Paper reviews and compares NMF, PLSA, LBA, EMA, and LCA models.
problem Identifiability of latent models.
method Comparison and proof of identifiability.
result Identifiability of LBA, EMA, LCA, PLSA is unique if and only if NMF is unique.
Innovation is among the key factors driving a country's economic and social growth. But what are the factors that make a country innovative? How do they differ across different parts of the world and different stages of development? In this work done in collaboration with the World Economic Forum (WEF), we analyze the …
Newsroom in online ecosystem is difficult to untangle. With prevalence of social media, interactions between journalists and individuals become visible, but lack of understanding to inner processing of information feedback loop in public sphere leave most journalists baffled. Can we provide an organized view to charact…
Develops a personalized reinforcement learning algorithm for dyadic health interventions.
problem Personalizing health interventions for dyadic relationships in mobile health.
method Dyadic Reinforcement Learning (dyadic RL), a Bayesian and hierarchical online algorithm.
result Established a regret bound and demonstrated empirical performance through simulations and real data.
Sensitivity analysis for individualized effects in OTRs with binary risk factors.
problem Addressing omitted confounding in individualized effects of OTRs.
method Simulation-based sensitivity analysis to simulate unmeasured confounders.
result Benchmarking the strength of omitted confounding for binary risk factors.
We prove near-tight concentration of measure for polynomial functions of the Ising model under high temperature. For any degree d d d , we show that a degree- d d d polynomial of a n n n -spin Ising model exhibits exponential tails that scale as exp ( − r 2 / d ) \exp(-r^{2/d}) exp ( − r 2/ d ) at radius r = Ω ~ d ( n d / 2 ) r=\tildeΩ_d(n^{d/2}) r = Ω ~ d ( n d /2 ) . Our concentration radius is opti…
AICov integrates population covariates for better COVID-19 forecasting.
problem Forecasting COVID-19 with broader social context.
method Integrative deep learning framework with LSTM and multiple data sources.
result Improved prediction of COVID-19 cases and deaths with population risk factors.
Tensor factorization uncovers hidden patterns in student behavior data.
problem Discovering low-dimensional structure in high-dimensional behavioral data.
method Non-negative tensor factorization applied to wearable sensor data.
result Tensor factorization reveals clusters of students with different behaviors.
Improved TF-IDF for word relevance in health-care social media documents.
problem Determining word relevance in informal documents.
method Semantic Sensitive TF-IDF (STF-IDF) method.
result Decreased TF-IDF mean error rate by 50% to 13.7%.