Paper proposes AI solutions for social problems in low-income areas.
problem Effective preventive measures against complex social problems in low-income regions.
method Design framework using data-driven machine learning for adaptive solutions.
result Illustrated design approach with survey data from India.
Predict social trust using 1-bit measurements and non-uniform sampling.
problem Predict social trust in social networks with sign measurements and non-uniform sampling.
method Propose a 1-bit max-norm constrained formulation and use a projected gradient decent algorithm.
result Demonstrated superior performance on benchmark datasets.
AI framework for automated policy-making connects with econometrics and social choice.
problem Improving government and economic policy-making through AI.
method Social Environment Design framework integrating Reinforcement Learning, EconCS, and Computational Social Choice.
result Promotes ethical and responsible decision making by solving open problems.
Enhances traditional MV model for socially responsible investors.
problem Traditional MV models ignore ESG scores relevant to socially responsible investors.
method Implemented an amended MV model considering ESG scores.
result SR investors can achieve competitive SR portfolios with a trade-off between Sharpe Ratio and ESG scores.
Paper tackles online task allocation in multi-attribute social sensing.
problem Optimized task allocation in dynamic, multi-attribute social sensing.
method Quality-Cost-Aware Online Task Allocation (QCO-TA) scheme using online reinforcement learning.
result Significantly outperforms state-of-the-art baselines in sensing accuracy and cost.
Paper improves gender detection on social media using deep learning.
problem Traditional classifiers struggle with social media data volume.
method Ensemble deep learning with multi-model architectures.
result Improved gender detection accuracy on social media posts.
Paper proposes using word embeddings to detect trolls in social media debates.
problem Preventing online harassment through rapid detection of offensive posts.
method Word embedding models for identifying fast-changing topics and negative content.
result GloVe model helps in discovering new keywords for trolling detection.
We show social events can be accurately predicted, but often undesirably.
problem The predictability of social events and its implications.
method Recent ideas from performative prediction and outcome indistinguishability.
result Social events can be efficiently predicted accurately, but often undesirably.
Predicting event attendance using social influence from social networks.
problem Predicting people's participation in real-world events.
method Modeling social influence, using non-geotagged posts and social group structures, applying graph embedding techniques, and training a neural network.
result The proposed classifier achieves 89% accuracy on the VFestival dataset, outperforming state-of-the-art methods.
Paper uses GAN to predict opioid relapse from social media data.
problem Accurate relapse prediction for opioid addiction.
method Generative Adversarial Networks (GAN) model trained on sentiment images and social influences.
result GAN model predicts relapse better than alternatives, showing relapse is linked to joy and negative emotions.
Optimizes social interactions for profit, people, and planet using mathematical models.
problem Determining the most effective social configurations for mutual objectives.
method Formulated as a mathematical optimization problem, using (meta)relational models theory.
result Identifies the most suitable combination of sociality forms for mutual objectives.
Model detects cyberbullying by analyzing participant-vocabulary consistency.
problem Identifying cyberbullying on social media platforms.
method Formulated an objective function based on participant-vocabulary consistency to detect cyberbullying.
result The model can detect new bullying vocabulary, victims, and bullies.
The paper develops a method to estimate trust weights in social networks using active sensing.
problem Estimating the relative trust agents place on each other in social networks.
method Regression model based on the steady state equation of the linear DeGroot model, using stubborn agents as influencers.
result The network structure can be revealed when a sufficient number of stubborn agents influence ordinary agents.
This paper evaluates methods for predicting credit risk in social lending, especially for imbalanced data.
problem Predicting credit risk in imbalanced social lending environments.
method Empirical comparison of various classifier-resampling techniques, focusing on random forest and random under-sampling.
result Combining random forest and random under-sampling may be an effective strategy for predicting credit risk in social lending.
New framework uses social media images to estimate wildlife populations.
problem Lack of basic data for wildlife species due to inadequate traditional methods.
method Developed a new computer vision tool to account for social media bias.
result Showed that wildlife population size estimates are learnable from social media.
New framework converts fairness criteria to social welfare maximization.
problem Interpreting fairness criteria in terms of societal welfare and distribution.
method Converts constrained loss minimization to social welfare maximization problem.
result Predictions and fairness constraints shape societal welfare and distribution.
Enhances social spam detection using multi-level dependency of relational sequences.
problem Social spam detection in multi-relation social networks.
method Developed the Multi-level Dependency Model (MDM) to exploit long-term and short-term dependencies in user relational sequences.
result MDM improves social spam detection accuracy on a real-world multi-relational social network.
Study fairness in vaccine allocation in social networks.
problem Fairness implications of vaccine allocation strategies in social networks.
method Defined precision disease control problem, used ML Fairness Gym to simulate and analyze.
result Different treatment strategies distribute disease burden differently across subgroups.
A new distance measure SND analyzes polar opinions in social networks.
problem Detecting and predicting polar opinion dynamics in social networks.
method Social Network Distance (SND) measures network evolution cost under polar opinion propagation models.
result SND achieves high true positive rates and opinion prediction accuracy.
A deep learning strategy improves recommendation accuracy by leveraging trust and distrust relationships.
problem Data scarcity and cold-start problem in recommender systems.
method Social deep pairwise learning with a ranking loss function and social negative sampling.
result The proposed model achieves an 11.49% improvement over state-of-the-art methods.
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.
The study reveals nations drive scientific research for social and economic interests.
problem Why do nations produce scientific research?
method Synthesizes previous concepts of science and scientific research, defines them, and identifies key drivers.
result Scientific research is driven by nations' social and economic interests, not just for philosophical inquiries.
Predicting depression on Twitter using social media posts.
problem Identifying depression in online personas.
method Crowdsourced Twitter users, Bag of Words approach, statistical classifiers.
result 81% accuracy rate in depression risk estimation.
The paper tackles adaptive policy selection to maximize social welfare, achieving optimal regret bounds.
problem Maximizing social welfare through adaptive policy selection, considering both private utility and public revenue.
method The approach involves learning response functions through experimentation, deriving lower and upper bounds for regret, and using algorithms like Exp3.
result The algorithm achieves optimal regret bounds, showing that welfare maximization is harder than multi-armed bandit problems.
Study tackles hate speech against journalists on social media.
problem Hate speech against journalists on social media remains prevalent despite efforts.
method Defined journalist-specific hate speech, annotated tweets, trained deep learning models, and proposed an ensemble model.
result Proposed ensemble model outperforms individual models in detecting journalist-targeted hate speech.
Highly precise globalness detection on social network pages.
problem Differentiating local from global nodes in online social networks.
method Four-stage operational flow for globalness detection.
result High precision (89%) and recall (88%) of local pages predictions.
RLINK uses deep reinforcement learning to improve user identity linkage across social networks.
problem Recognizing the same user across different social networks.
method Converts user identity linkage into a sequence decision problem and uses deep reinforcement learning to optimize the linkage strategy.
result Achieves better performance than state-of-the-art methods in experiments on various datasets.
Proposes a semi-supervised approach to predict user-level sentiments in social media.
problem Detect and analyze sentiment in social media, especially user-level sentiments.
method Semi-supervised approach using a heterogeneous graph built from social networks, incorporating user influences and multiple types of links.
result Predicts user-level sentiments for specific topics more effectively than previous supervised learning approaches.
Detecting depression early from social media texts.
problem Early diagnosis and prevention of depression.
method Topic analysis and learned confidence scores.
result Achieved good results compared to state of the art.
Social-sparsity brain decoders improve speed and interpretability.
problem Computational cost and interpretability in brain decoding models.
method Introduced social-sparsity, a structured shrinkage operator.
result Social-sparsity performs almost as well as total-variation models and better than graph-net, with a fraction of the computational cost.
MONSTOR estimates influence in unseen networks with high accuracy.
problem Estimating and maximizing influence in social networks.
method Inductive machine learning approach replacing Monte Carlo simulations.
result Highly accurate estimates with strong correlation to actual influence.
Project classifies Hinglish social content on platforms like Twitter, Reddit.
problem Classifying abusive and hate-inducing content in Hinglish on social media.
method Used deep learning with bi-directional sequence models and text augmentation techniques.
result Produced a state-of-the-art classifier that outperforms previous work.
An unsupervised neural network learns event truths from social network data.
problem Estimating event truths from conflicting opinions in social networks.
method Autoencoder learns relationships, Bayesian network models agent reliability and social relationships, variational inference estimates hidden variables and parameters.
result The approach outperforms state-of-the-art methods on real datasets.
New algorithm improves group fairness in social classification problems by exploiting performativity.
problem Inequities in social classification problems due to performativity.
method Develops algorithmic fairness practices that leverage performativity to achieve stronger group fairness guarantees.
result Achieves stronger group fairness guarantees compared to non-performative settings.
Paper infers multiplex network structure from social interactions.
problem Challenges in understanding diffusion in social networks due to hidden structure and multiple interaction patterns.
method Proposes Multiplex Diffusion Model (MDM) using multivariate marked Hawkes process and topic model.
result MDM more effectively uncovers multiplex network structure compared to previous methods.
Efficiently selects seed nodes to maximize content influence in unknown social networks.
problem Maximizing content spread in social networks with unknown network model.
method Formulated as an infinite-horizon discounted MDP, uses model-based reinforcement learning to select seed users adaptively.
result Established a regret bound of O ~ ( T ) \widetilde O(\sqrt{T}) O ( T ) for the algorithm. Improved approximation for socially fair clustering with ℓ p \ell_p ℓ p -objective.
problem Finding a set of centers minimizing the maximum distance to all points in each group.
method Introduced a strengthened LP relaxation with an integrality gap of Θ ( log ℓ log log ℓ ) \Theta(\frac{\log \ell}{\log\log\ell}) Θ ( l o g l o g ℓ l o g ℓ ) . result Improved approximation algorithm with ( e O ( p ) log ℓ log log ℓ ) (e^{O(p)} \frac{\log \ell}{\log\log\ell}) ( e O ( p ) l o g l o g ℓ l o g ℓ ) -approximation. New framework for fair ranking with noisy protected attributes.
problem Errors in socially-salient attributes undermine fairness guarantees.
method Modeling perturbations in protected attributes and incorporating probabilistic information.
result Framework provides provable guarantees on fairness and utility.
Study online and offline social interactions using game theory.
problem Effects of online social networking on offline interactions and collective well-being.
method Evolutionary game theory approach to model socialization strategies.
result Self-protective behaviors can lead to non-socially optimal outcomes.
PHASE dataset simulates complex social interactions in physical environments.
problem Lack of datasets for evaluating physically grounded perception of complex social interactions.
method Created PHASE dataset of 2D animations with procedural generation and physics engine.
result SIMPLE model outperforms neural networks in recognizing complex social interactions.
Aims to optimize influence spread in social networks using bandit algorithms.
problem Maximizing influence spread in unknown social networks.
method Combines Thompson Sampling and Epsilon Greedy algorithms with automatic ensemble learning.
result Demonstrates effectiveness of automatic ensemble learning for combinatorial bandit problems.
GCAN detects fake news on social media with explanations.
problem Detecting fake news on social media with explanations.
method Graph-aware Co-Attention Networks (GCAN).
result GCAN significantly outperforms state-of-the-art methods in accuracy.
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.
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.
Bayesian method predicts social unrest from social media data.
problem Uncertainty in predicting events from diverse, unstructured social media data.
method Machine learning for classification and empirical Bayesian approach for event probability calculation.
result Predicted social unrest events in Australian cities with high accuracy.
The paper analyzes fairness and social welfare in machine learning classification.
problem The relationship between fairness and social welfare in machine learning classification.
method Welfare-based analysis of classification and fairness regimes; algorithm for linear hyperplanes.
result More strict fairness criteria can worsen welfare outcomes for disadvantaged groups.
Survey on deep learning for social network analysis.
problem Encoding social network data into useful low-dimensional representations.
method Review of neural network models for node and subgraph embeddings in various network types.
result Advancements in deep learning for complex network analysis.
Improved neural model for social recommendation by integrating social and interest networks.
problem Data sparsity and lack of higher-order relationships in social recommendation.
method DiffNet++ models neural influence diffusion and interest diffusion in a unified framework using a multi-level attention network.
result Extensive experiments on real-world datasets show the effectiveness of DiffNet++.