Consumers adjust their spending based on firms' social stances, influencing firm profits.
problem How consumer spending responds to firms' social stances.
method Using payment card transactions to predict and measure consumer responses to firms' social stances.
result Consumers' spending increases by 19% and decreases by 12% in response to firms' social stances, with effects lasting up to a year.
TESTED improves multi-domain stance detection with topic-guided sampling and contrastive learning.
problem Challenges in multi-domain stance detection due to domain-specific variations and imbalanced annotations.
method Topic-guided diversity sampling and contrastive learning objective.
result Significant improvement in F1 scores, up to 10.2 points out-of-domain.
Social media communications are becoming increasingly prevalent; some useful, some false, whether unwittingly or maliciously. An increasing number of rumours daily flood the social networks. Determining their veracity in an autonomous way is a very active and challenging field of research, with a variety of methods pro…
This paper studies the problem of stance detection which aims to predict the perspective (or stance) of a given document with respect to a given claim. Stance detection is a major component of automated fact checking. As annotating stances in different domains is a tedious and costly task, automatic methods based on ma…
According to Dennett, the same system may be described using a `physical' (mechanical) explanatory stance, or using an `intentional' (belief- and goal-based) explanatory stance. Humans tend to find the physical stance more helpful for certain systems, such as planets orbiting a star, and the intentional stance for othe…
System classifies Twitter and Reddit posts' stance towards hidden rumour threads.
problem Classifying posts' stance towards hidden rumour threads.
method Used pre-trained deep bidirectional transformers (BERT) for stance classification.
result Reached F1 score of 61.67% on test data, 2nd place in competition.
STANCE learns string similarity using optimal transport alignment.
problem Computing similarity between strings for record linkage and entity resolution.
method Character encoding, optimal transport alignment, convolutional neural network scoring.
result STANCE outperforms state-of-the-art models on alias detection datasets.
The proliferation of fake news and filter bubbles makes it increasingly difficult to form an unbiased, balanced opinion towards a topic. To ameliorate this, we propose 360° Stance Detection, a tool that aggregates news with multiple perspectives on a topic. It presents them on a spectrum ranging from support to opposit…
Machine learning improves accuracy of running gait event detection from tibial acceleration.
problem Accurate detection of running gait events from tibial acceleration data.
method Structured machine learning models compared to heuristic methods.
result Structured recurrent neural network model offers most accurate estimation of gait events.
This paper introduces modal epistemic tools for risk management.
problem Identifying and certifying risk claims when institutions lack the necessary epistemic stance.
method Develops crisp and fuzzy modal semantics for assurance and working commitment, distinguishing between object-level risk claims and meta-level epistemic diagnostics.
result Risk governance should model evidential incompleteness and failures of escalation, not just hazards and losses.
FAKTA automates fact checking across media sources.
problem Automating fact checking across diverse media sources.
method Unified framework integrating document retrieval, stance detection, evidence extraction, and linguistic analysis.
result FAKTA predicts factuality and provides evidence for claims.
Fake news are nowadays an issue of pressing concern, given their recent rise as a potential threat to high-quality journalism and well-informed public discourse. The Fake News Challenge (FNC-1) was organized in 2017 to encourage the development of machine learning-based classification systems for stance detection (i.e.…
Novel GNN method for semi-supervised clustering of signed networks.
problem Lack of effective node embeddings for signed network clustering.
method SSSNET: Probabilistic balanced normalized cut loss for GNN.
result SSSNET achieves comparable or better results than state-of-the-art methods.
AI enhances ESG practices in finance, but requires careful consideration.
problem Regulatory pressures and stakeholder awareness drive ESG adoption.
method Industrial survey categorizing AI applications in ESG.
result AI improves analytical capabilities, risk assessment, and customer engagement.
We introduce simple cost and risk proxy metrics that can be attached to Treasury issuance strategy to complement analysis of the resulting portfolio weighted-average maturity (WAM). These metrics are based on mapping issuance fractions to their long-term, asymptotic portfolio implications for cost and risk under mechan…
Paper organizes sampling methods for generative modeling.
problem Challenges in sampling with diffusion models.
method Reviews and organizes existing sampling methods.
result Reveals links between methods to overcome challenges.
Research aims to make fact-checking models more transparent.
problem Making fact-checking models explainable in a complex field.
method Combines fact-checking methods with explainable AI techniques.
result Developed initial solutions for explainable fact-checking.
Geotechnics adopts data-driven methods from materials informatics.
problem Soil complexity and lack of comprehensive data.
method Leveraging deep learning and transfer learning for feature extraction.
result Revolutionary potential of advanced computational tools in geotechnics.
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.
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++.
Large financial dataset tracks FOMC communications and their impact.
problem Understanding how FOMC communications influence financial markets.
method Constructed a large annotated dataset of FOMC speeches, minutes, and transcripts. Developed a hawk-dove classification task. Evaluated various models on the dataset and used RoBERTa-large for monetary policy stance measurement.
result Monetary policy stance measures derived from FOMC documents predict market performance.
Social media enhances or diminishes scientific status, depending on usage.
problem Impact of social media on scientific stratification and mobility.
method Logistic Attribution Analysis combining statistical and machine learning methods.
result Social media promotes stratification and mobility, but beyond a threshold, it negatively impacts status.
Social learning can make financial markets inefficient, but individual learning can fix this.
problem Inefficiencies in financial markets due to social learning.
method Study of the Minority Game model with social and individual learning mechanisms.
result Individual learning can rescue a population from the inefficiencies caused by social learning.
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.
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 predicts increased social unrest during COVID-19 using social media data.
problem Detecting rising conflict potential in societies during pandemics.
method Neural implicit motive pattern recognition from social media texts.
result Significant increase in conflict indicators during the pandemic.
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.
It has been suggested that adversarial examples cause deep learning models to make incorrect predictions with high confidence. In this work, we take the opposite stance: an overly confident model is more likely to be vulnerable to adversarial examples. This work is one of the most proactive approaches taken to date, as…
Economies are complex man-made systems where organisms and markets interact according to motivations and principles not entirely understood yet. The increasing dissatisfaction with the postulates of traditional economics i.e. perfectly rational agents, interacting through efficient markets in the search of equilibrium,…
The rise in online social networking has brought about a revolution in social relations. However, its effects on offline interactions and its implications for collective well-being are still not clear and are under-investigated. We study the ecology of online and offline interaction in an evolutionary game framework wh…
Paper proposes PP-GCN for fine-grained social event categorization.
problem Challenges in mining social events due to heterogeneous event elements and social network structures.
method Design an event meta-schema, build an HIN, propose PP-GCN, and use KIES.
result PP-GCN outperforms other techniques in social event detection and clustering.
A new method for recommending groups and activities based on geo-social data.
problem Recommendation of groups and activities based on geo-social data with social constraints.
method Proposes an attentive geo-social group recommendation method with an attention mechanism and a spatial query algorithm.
result Significantly outperforms baseline methods in real-world datasets.
MGpi model predicts social actions in group interactions.
problem Social interaction among multiple agents and groups.
method Deep neural network with Kinesic-Proxemic-Message gate for social signal gating.
result Achieves state-of-the-art performance in group identification.
Secure social recommendation framework using secret sharing.
problem Privacy concerns and reluctance to share social data in recommender systems.
method Secret Sharing based Matrix Multiplication (SSMM) protocol for secure data sharing and collaborative recommendation.
result SeSoRec framework improves recommendation performance and is secure.
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.
Algorithm improves learning by integrating diverse agents' behaviors.
problem Lack of social learning in reinforcement learning algorithms.
method Free energy approach for social bandit learning.
result Algorithm converges to optimal policy and enhances learning.
Model predicts depression and anxiety from social network dynamics.
problem Predicting mental health from social interactions.
method Developed a predictive model using dynamic social network data.
result Dynamic social network model outperforms static models.
SBO uses dual voting to build consensus in noisy feedback settings.
problem Collective decision-making under social influence.
method Dual voting system with noisy public votes and private interviews.
result Efficient consensus-building through noisy public votes, debiased by estimated social graph.
The combination of large open data sources with machine learning approaches presents a potentially powerful way to predict events such as protest or social unrest. However, accounting for uncertainty in such models, particularly when using diverse, unstructured datasets such as social media, is essential to guarantee t…
Online social networks offer a new way to investigate financial markets' dynamics by enabling the large-scale analysis of investors' collective behavior. We provide empirical evidence that suggests social media and stock markets have a nonlinear causal relationship. We take advantage of an extensive data set composed o…
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.
Mobile big data contains vast statistical features in various dimensions, including spatial, temporal, and the underlying social domain. Understanding and exploiting the features of mobile data from a social network perspective will be extremely beneficial to wireless networks, from planning, operation, and maintenance…
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.
Heterogeneous SVO leads to diverse policies in sequential social dilemmas.
problem Understanding how diverse social value orientations affect behavior in sequential social dilemmas.
method Extending prior reinforcement learning studies, we instantiated heterogeneous SVO in a sequential social dilemma setting and measured task-specific diversity metrics.
result Heterogeneous SVO leads to meaningfully diverse policies across various incentive structures.
Actors in realistic social networks play not one but a number of diverse roles depending on whom they interact with, and a large number of such role-specific interactions collectively determine social communities and their organizations. Methods for analyzing social networks should capture these multi-faceted role-spec…
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.
Agents learn social skills from each other, improving performance.
problem Independent RL agents fail to use social learning.
method Imposed constraints, model-based auxiliary loss, mixed training.
result Agents trained with social learning outperform solo-trained agents.
We demonstrate that future market correlation structure can be predicted with high out-of-sample accuracy using a multiplex network approach that combines information from social media and financial data. Market structure is measured by quantifying the co-movement of asset prices returns, while social structure is meas…