Bayesian method models dynamic text networks and topics.
problem Discovering community structure and topics in evolving text networks.
method Bayesian approach combining network and topic modeling.
result Complex community structure identified, dependent on blogger interests.
Study examines fake news as modern myths using AI.
problem Misinformation and propaganda in fake news.
method Machine learning to generate fake articles.
result Details of fake news generation pipeline.
Paper presents a novel neural network method for topic detection in micro-blogs.
problem Challenging topic detection without known topic count.
method Unsupervised neural sentence embedding model with attention mechanism.
result Improved clustering algorithm (RADBSCAN) discovers topics based on dataset character.
Paper presents a privacy-preserving algorithm for estimating peer effects using the Ising model.
problem Privacy concerns in estimating peer effects using network data.
method Developed a (ε,δ)-differentially private algorithm using Ising model. result Established regret bounds and validated performance on synthetic and real-world networks.
Many algorithms have been proposed for fitting network models with communities, but most of them do not scale well to large networks, and often fail on sparse networks. Here we propose a new fast pseudo-likelihood method for fitting the stochastic block model for networks, as well as a variant that allows for an arbitr…
The study of networks has received increased attention recently not only from the social sciences and statistics but also from physicists, computer scientists and mathematicians. One of the principal problem in networks is community detection. Many algorithms have been proposed for community finding but most of them do…
Suppose that a graph is realized from a stochastic block model where one of the blocks is of interest, but many or all of the vertices' block labels are unobserved. The task is to order the vertices with unobserved block labels into a ``nomination list'' such that, with high probability, vertices from the interesting b…
A new model corrects SBM's bias for power-law degree networks.
problem SBM's incapability to handle power-law degree distributions.
method Introducing degree decay variables to encode varying degree distributions.
result PLD-SBM approximately preserves the scale-free feature in real networks and corrects SBM's bias.
A latent space model for a family of random graphs assigns real-valued vectors to nodes of the graph such that edge probabilities are determined by latent positions. Latent space models provide a natural statistical framework for graph visualizing and clustering. A latent space model of particular interest is the Rando…
Method detects communities in networks using matrix factorization.
problem Community detection in complex networks.
method Orthogonal symmetric non-negative matrix tri-factorization of the normalized Laplacian matrix.
result Consistent for community detection in graphs from stochastic block models.
Paper finds political networks reduce bond issuance costs in China.
problem The financial value of within-government political networks in China.
method Using municipal leaders' working experience to measure political networks, the study examines the effect on bond issuance yield spreads.
result Political networks reduce bond issuance yield spreads by improving issuer credit ratings, especially in less developed financial markets.
Two computational models analyze political topics in social media tweets.
problem Measuring political attention in social media is labor-intensive and restrictive.
method Two computational models: supervised classifier and unsupervised topic model.
result Models provide different benefits: supervised classifier reduces labor, unsupervised model uncovers political and non-political uses.
The paper uses NMF to detect political communities in Twitter networks.
problem Detecting pure political communities in Twitter networks.
method Developed three NMF frameworks to analyze user connectivity and content.
result User content and endorsement filtered connectivity are complementary.
Directed graphs have asymmetric connections, yet the current graph clustering methodologies cannot identify the potentially global structure of these asymmetries. We give a spectral algorithm called di-sim that builds on a dual measure of similarity that correspond to how a node (i) sends and (ii) receives edges. Using…
Study analyzes fintech terms in news and blogs, revealing specialized attributes of fintech companies.
problem Understanding specialized attributes of fintech companies through term analysis.
method Large scale analysis of fintech terms in news and blogs, using complex networks and statistically validated networks.
result Companies with fintech terms have over-expressions of specific attributes related to geography and economy.
Scores political leanings in Web3 betting markets.
problem Understanding political motivations in decentralized prediction markets.
method Constructing PBLS from Polymarket data, analyzing 15k addresses, 4k events, 8k markets.
result Validated PBLS through internal and external comparisons, revealing political and profit motives.
MONET debiases graph embeddings by training on metadata-orthogonal dimensions.
problem Graph embeddings can be biased by node attributes, affecting fairness and interpretability.
method MONET trains embeddings on a hyperplane orthogonal to node metadata.
result MONET effectively removes bias from node embeddings, improving fairness and interpretability.
Blog post discusses various implementations of Fisher Information for EWC in continual learning.
problem Improving Elastic Weight Consolidation (EWC) results by optimizing Fisher Information computation.
method Empirically compares different implementations of Fisher Information for EWC.
result Many reported EWC results can be improved by changing Fisher Information computation methods.
New method predicts political ideology from online activity.
problem Predicting political ideology from digital footprints.
method Statistical learning approaches applied to reddit data.
result Activity in non-political forums can predict political ideology with high accuracy.
TBIP uses texts to quantify lawmakers' political positions.
problem Quantifying lawmakers' political positions from speeches, tweets, etc.
method Unsupervised probabilistic topic model analyzing texts.
result TBIP separates lawmakers by party and infers ideal points close to vote-based.
Study benchmarks machine learning models for fake news detection.
problem Dataset bias and performance of fake news detection models.
method Benchmarked different machine learning models on three datasets.
result BERT and similar pre-trained models perform best for fake news detection.
The UN General Debate Corpus analyzes speeches from UN member states to reveal their political positions.
problem Lack of data on state preferences in international politics.
method Text analysis of over 7,700 speeches from 1970-2016.
result Demonstrates how the UN General Debate Corpus can reveal country positions on various policy dimensions.
System detects financial opportunities in tweets with high precision.
problem Detecting valuable financial insights in micro-blogging data.
method Stacked Machine Learning classification system with NLP features.
result System achieves precision up to 83% in detecting financial opportunities.
Model analyzes how political competition affects economic development.
problem Complex interactions between political competition and economic development.
method Kinetic model with stochastic game interactions.
result Non-monotonic relationship between economic liberalization and political competition.
We consider the problem of signal recovery on graphs as graphs model data with complex structure as signals on a graph. Graph signal recovery implies recovery of one or multiple smooth graph signals from noisy, corrupted, or incomplete measurements. We propose a graph signal model and formulate signal recovery as a cor…
One important effect of price shocks in the United States has been increased political attention paid to the structure and performance of oil and natural gas markets, along with some governmental support for energy conservation. This paper describes how price changes helped lead the emergence of a political agenda acco…
Blog debunks seven common myths in machine learning.
problem Misconceptions about machine learning practices and datasets.
method Analysis of common myths in machine learning research.
result Myths about TensorFlow, image datasets, validation, and neural networks are debunked.
Based mainly on examples of interest in mechanics, we define the notion of a polite group action. One may view this as not only trying to give a more general notion than properness of a group action, but also to more fully understand the role of invariant functions in describing just about everything of interest in red…
Human stablecoin transactions predict political risk in cryptocurrency markets.
problem Predicting political risk in cryptocurrency markets.
method Structural break analysis and surrogate-based robustness tests.
result Human-driven stablecoin transactions shift significantly before major political events.
Community detection, which aims to cluster N nodes in a given graph into r distinct groups based on the observed undirected edges, is an important problem in network data analysis. In this paper, the popular stochastic block model (SBM) is extended to the generalized stochastic block model (GSBM) that allows for ad…
Improves information cascade models using contrastive training and DSTs.
problem Improving models of information cascades using limited labeled data.
method Proposes a contrastive training procedure for models of information cascades as directed spanning trees (DSTs).
result Unsupervised training with additional content features achieves significantly better results, reaching half the accuracy of a fully supervised model.
Blog post comparing neural network methods for causal inference.
problem Estimating heterogeneous treatment effects in causal inference.
method Developed and compared a fully connected neural network implementation of Bayesian Causal Forest.
result Improvements in performance in simulation settings.
Model forecasts hourly electricity demand influenced by weather, socio-economic, and political factors.
problem Accurate hourly electricity demand forecasting in the face of multifaceted uncertainties.
method Interpretable probabilistic mid-term forecasting model using Generalized Additive Models (GAMs).
result Highlights vulnerability of countries to extreme weather scenarios under electric heating adoption.
Italy's vaccine coverage fell, leading to political debates and online social media discussions.
problem Low vaccine coverage and political debates on immunization in Italy.
method Sentiment analysis of tweets in Italian during 2018 to assess public opinion on vaccines.
result There was disorientation among the public due to political announcements, as evidenced by Twitter data.
Smart contracts create digital financial derivatives.
problem Creating a new digital financial derivative contract.
method Applied existing smart contract technologies to develop two prototypes.
result Demonstrated feasibility of digital financial derivatives on centralized and DLT platforms.
Study of negative ads on social media during U.S. midterm elections.
problem Understanding the effectiveness and mechanisms of negative advertising on social media.
method Machine learning for sentiment analysis, AI image recognition, ordinal regressions.
result Negative ads are less effective than previously thought, anger is a key mechanism.
Jointly predicts news media trustworthiness and political ideology.
problem Estimating trustworthiness and detecting political ideology of news outlets.
method Multi-task ordinal regression framework.
result Joint models outperform isolated models by significant performance gains.
Managing large-scale transportation infrastructure projects is difficult due to frequent misinformation about the costs which results in large cost overruns that often threaten the overall project viability. This paper investigates the explanations for cost overruns that are given in the literature. Overall, four categ…
This study examines how political uncertainty affects U.S. stock markets, finding mixed results.
problem The impact of political uncertainty on U.S. stock markets during presidential election periods.
method Event-study methodology examining abnormal return behavior around election dates.
result Positive abnormal returns were found following election results, contradicting the uncertain information hypothesis.
The paper uses Black-Scholes model to analyze political support and coalition agreements.
problem Determining the minimum support level for a minor party in a pre-electoral coalition.
method Modeling political support as a stochastic process with a deterministic growth rate and applying Black-Scholes option pricing theory.
result The minimum support level for a minor party to gain a representative in a pre-electoral coalition.
Model predicts political ideology using context vectors to mitigate bias and scarcity.
problem Scarcity and selection bias in political ideology prediction.
method Proposes a statistical model decomposing embeddings into context and position vectors, training an end-to-end model for deployment.
result Model can predict ideological labels even with minimal biased data, outperforming state-of-the-art methods.
Study examines Trump's crypto influence on markets, revealing conflicts and vulnerabilities.
problem Presidential power and cryptocurrency markets during Trump's second term.
method Mixed-methods approach combining quantitative and qualitative data.
result Political-linked digital assets became a distinct class with systemic vulnerabilities.
Paper uses neural word embeddings to analyze UN speeches for policy preferences and voting behavior.
problem Analyzing policy preferences and paradigm shifts in international politics.
method Applied neural word embeddings (Word2vec) to UN General Debate speeches.
result Found statistical relation between speech semantic content and voting behavior, contrary to hypothesis.
News attention to financial intermediaries and crises predicts excess bond premium and macroeconomic movements.
problem Drivers of the excess bond premium (EBP).
method News attention to 180 topics captures up to 80% of EBP variation and forecasts macroeconomic movements.
result News attention to financial intermediaries and crises drives up the EBP and predicts macroeconomic downturns.
Prediction markets can shape political behavior through persistent signals, not just forecast accuracy.
problem The role of prediction markets beyond forecasting.
method Transaction-level evidence from the 2024 U.S. presidential election, Signal Credibility Index (SCI).
result Price signals in prediction markets are more influential due to persistence, breadth of trader types, and cross-platform consensus.
Anti-ELAB protests affected Hong Kong firms' stock prices, especially those linked to pan-democrats.
problem Impact of anti-ELAB protests on Hong Kong firms' stock prices.
method Daily protesting intensity measured by number of protestors from 2019/6/6 to 2020/1/17; analyzed stock price changes of firms.
result Anti-ELAB protests negatively affected firms linked to pan-democrats, positively affected red chips.
Deep learning detects radical content on social media.
problem Detecting extremist content on social media platforms.
method Employed an LSTM based feed forward neural network to classify radical content.
result Achieved a precision of 85.9% in detecting radical content.
The study shows how trade uncertainty affects stock-bond correlations over time.
problem Impact of trade policy uncertainty on stock-bond correlations.
method Daily data analysis using GARCH-based models (CCC, STCC, DCC) with TPU and political dummy variables.
result Time-varying correlation models better capture the dynamics of stock-bond correlations than constant models.