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…
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48 results for “social data”
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.
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…
The paper examines how NFT valuations correlate with market data and social trends.
problem Predicting NFT valuations based on market data and social trends.
method Utilizes public market data, NFT metadata, and social trends data; employs linear regression and recurrent neural networks.
result Identifies correlations between NFT valuations and various features.
Precision medicine has received attention both in and outside the clinic. We focus on the latter, by exploiting the relationship between individuals' social interactions and their mental health to develop a predictive model of one's likelihood to be depressed or anxious from rich dynamic social network data. To our kno…
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.
EmTract extracts emotions from financial social media text.
problem Understanding investor emotions in financial markets.
method Annotated data, DistilBERT model, embedding space augmentation.
result EmTract outperforms existing emotion classifiers.
Opioid addiction is a severe public health threat in the U.S, causing massive deaths and many social problems. Accurate relapse prediction is of practical importance for recovering patients since relapse prediction promotes timely relapse preventions that help patients stay clean. In this paper, we introduce a Generati…
This paper studies how social media posts, especially by executives, affect stock prices.
problem Predicting stock market movements using social media data.
method Integrated sentiment analysis of Twitter and Reddit posts with historical stock data using time series models and deep learning.
result Improvements in stock price prediction when social media data, especially executive posts, are included.
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.
The study examines how social biases are reinforced in machine learning models used for credit scoring.
problem Reinforcement of societal biases in machine learning algorithms for credit scoring.
method Analysis of machine learning models predicting gender or ethnicity based on loan applications data.
result Machine learning models can reflect and reinforce social biases present in the data.
The study combines social interaction data into a single network, identifying stable groups of chimpanzees.
problem Identifying stable groups of chimpanzees based on social interactions over time.
method Network representation, weighted proximity weights, principled loss function, statistical tests.
result The approach detects stable groups of chimpanzees that stay related for a significant length of time.
LoCEC classifies user relationships in large social networks, addressing sparsity issues.
problem Sparse relationship feature and label data in real social platforms.
method Local Community-based Edge Classification (LoCEC) framework with three-phase processing.
result Effective and efficient classification of user relationships in large-scale networks.
The ever-growing volume of data of user-generated content on social media provides a nearly unlimited corpus of unlabeled data even in languages where resources are scarce. In this paper, we demonstrate that state-of-the-art results on two Thai social text categorization tasks can be realized by pretraining a language …
Finding Social Media Trolls: Dynamic Keyword Selection Methods for Rapidly-Evolving Online Debatescs.LG
Online harassment is a significant social problem. Prevention of online harassment requires rapid detection of harassing, offensive, and negative social media posts. In this paper, we propose the use of word embedding models to identify offensive and harassing social media messages in two aspects: detecting fast-changi…
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.
Visualization of Board of Director Connections for Analysis in Socially Responsible Investingq-fin.GN
Visualizes board connections for socially responsible investing insights.
problem Understanding corporate governance and sustainability through board connections.
method Data Visualization tool to reveal connections between Directors and Executives.
result Strength of tool in investigating corporate governance and sustainability.
LISBET automates social behavior analysis using machine learning.
problem Manual annotation of social behaviors is time-consuming, biased, and misses subtle interactions.
method Self-supervised learning on body tracking data.
result Automated detection and segmentation of social interactions.
The paper argues for a social-economic approach to AI development.
problem The current AI development overlooks social and cultural aspects of intelligence.
method A blend of economic and social concepts with computational and inferential concepts in algorithm design.
result A social-economic approach is necessary for proper AI development.
Social media signals have been successfully used to develop large-scale predictive and anticipatory analytics. For example, forecasting stock market prices and influenza outbreaks. Recently, social data has been explored to forecast price fluctuations of cryptocurrencies, which are a novel disruptive technology with si…
Does social media information affect individual investor disposition effect? Evidence from Xueqiuq-fin.GN
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.
CovidSens uses social media to track COVID-19 spread.
problem Accurate and timely dissemination of COVID-19 information.
method Social sensing to analyze online user data.
result Real-time COVID-19 spread tracking system.
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++.
State-of-the-art link prediction utilizes combinations of complex features derived from network panel data. We here show that computationally less expensive features can achieve the same performance in the common scenario in which the data is available as a sequence of interactions. Our features are based on social vec…
With the rise of social media, people can now form relationships and communities easily regardless of location, race, ethnicity, or gender. However, the power of social media simultaneously enables harmful online behavior such as harassment and bullying. Cyberbullying is a serious social problem, making it an important…
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…
TTERGM models improve social network predictions by incorporating triadic relationships.
problem Lack of models capturing triadic relationships and social learning theories in temporal network data.
method Introduced TTERGM, a generative model that includes triadic relationships and social learning theory as additional probability distributions. Parameters are estimated via Monte Carlo maximum likelihood.
result TTERGM achieves improved accuracy and fidelity compared to existing models on social network data.
Ensemble Deep Learning on Time-Series Representation of Tweets for Rumor Detection in Social Mediacs.LG
Deep learning ensemble detects social media rumors faster.
problem Timely detection of rumors on social media platforms.
method Ensemble model using deep neural networks and time-series tweet data.
result Improved rumor detection performance by 7.9%.
Events are happening in real-world and real-time, which can be planned and organized occasions involving multiple people and objects. Social media platforms publish a lot of text messages containing public events with comprehensive topics. However, mining social events is challenging due to the heterogeneous event elem…
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.
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.
Media Moments and Corporate Connections: A Deep Learning Approach to Stock Movement Classificationq-fin.ST
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.
Predicting future stock market structure by combining social and financial network informationq-fin.ST
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…
Modeling social network activity through user and topic interaction.
problem Understanding complex dynamics of opinion formation in social networks.
method Mixture of Interacting Cascades (MIC) model of marked multidimensional Hawkes processes.
result MIC achieves superior performance in modeling information cascade spread.
Framework detects fake news using weak social signals from multiple sources.
problem Lack of annotated data for early fake news detection.
method Jointly uses weak social signals and clean data to train deep neural networks in a meta-learning framework.
result Framework outperforms state-of-the-art baselines for early fake news detection.
User engagement in online social networking depends critically on the level of social activity in the corresponding platform--the number of online actions, such as posts, shares or replies, taken by their users. Can we design data-driven algorithms to increase social activity? At a user level, such algorithms may incre…
The use of financial and sustainability ratios to map a sector. An approach using compositional dataq-fin.ST
The study visualizes Spanish fish and meat processing companies using financial, environmental, and social ratios.
problem Mapping financial, environmental, and social performance of Spanish processing companies.
method Used compositional data and principal-component analysis biplot for statistical analysis.
result Identified clusters of companies with similar financial, environmental, and social performance.
No Free Delivery Service: Epistemic limits of passive data collection in complex social systemscs.AI
New findings show AI models can't be validated in complex social systems.
problem AI models in complex social systems can't be validated due to data collection practices.
method Formal impossibility results using the MovieLens benchmark.
result AI models in complex social systems are invalid under current data collection practices.
Analyzes ESG impact on stock market performance using social media and news data.
problem Understanding the impact of ESG news on stock market performance.
method Summarized live ESG data from social media and news, created sentiment index, calculated stock price changes, and compared sentiment to performance.
result ESG sentiment correlates with stock price changes, indicating its impact on market performance.
Social activities play an important role in people's daily life since they interact. For recommendations based on social activities, it is vital to have not only the activity information but also individuals' social relations. Thanks to the geo-social networks and widespread use of location-aware mobile devices, massiv…
Study shows social media impacts shareholder returns on ESG risks.
problem Investor sentiment and public opinion on ESG risks.
method Event study design using social media data.
result Statistically significant reduction in abnormal returns after ESG-risk events.
Domestic Violence (DV) is considered as big social issue and there exists a strong relationship between DV and health impacts of the public. Existing research studies have focused on social media to track and analyse real world events like emerging trends, natural disasters, user sentiment analysis, political opinions,…
Social-based recommendation systems exploit the selections of friends to combat the data sparsity on user preferences, and improve the recommendation accuracy of the collaborative filtering strategy. The main challenge is to capture and weigh friends' preferences, as in practice they do necessarily match. In this paper…
Recent works have shown that social media platforms are able to influence the trends of stock price movements. However, existing works have majorly focused on the U.S. stock market and lacked attention to certain emerging countries such as China, where retail investors dominate the market. In this regard, as retail inv…
We demonstrate that a number of sociology models for social network dynamics can be viewed as continuous time Bayesian networks (CTBNs). A sampling-based approximate inference method for CTBNs can be used as the basis of an expectation-maximization procedure that achieves better accuracy in estimating the parameters of…
Systematic review of ML models for detecting social media deception.
problem Detecting fake news, spam, and fake accounts on social media.
method 36 studies evaluated using PROBAST tool, identifying biases and limitations.
result Over-reliance on accuracy in imbalanced data settings is a flaw.
This research tackles unsupervised topic extraction in noisy social media data.
problem Capturing customer insights from social media data is challenging due to noise and heterogeneity.
method The research presents three nonparametric approaches based on the Variational Autoencoder framework: Embedded Dirichlet Process, Embedded Hierarchical Dirichlet Process, and time-aware Dynamic Embedded Dirichlet Process.
result The models achieve equal to better performance than state-of-the-art methods in topic extraction from noisy social media data.
A growing number of empirical studies suggest that negative advertising is effective in campaigning, while the mechanisms are rarely mentioned. With the scandal of Cambridge Analytica and Russian intervention behind the Brexit and the 2016 presidential election, people have become aware of the political ads on social m…