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.
On-device research index
arXiv research
A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
168,878 papers · 148 categories
Trend · papers per month
48 results for “social media analytics”
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.
Study uses machine learning to detect depression in Twitter users.
problem Detecting depression in Twitter users using social media data.
method Machine learning techniques applied to Twitter activity and tweet features.
result More features improve accuracy in detecting depressed users.
Study compares sentiment spillover networks from news and social media in tech companies.
problem Understanding how sentiment information flows between companies through news and social media.
method Network-based transfer entropy method to measure and compare sentiment spillover.
result News shows stronger information flow among tech companies after COVID-19.
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…
Finding Social Media Trolls: Dynamic Keyword Selection Methods for Rapidly-Evolving Online Debatescs.LG
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.
Social media analytics allows us to extract, analyze, and establish semantic from user-generated contents in social media platforms. This study utilized a mixed method including a three-step process of data collection, topic modeling, and data annotation for recognizing exercise related patterns. Based on the findings,…
In recent years, we have been faced with a series of natural disasters causing a tremendous amount of financial, environmental, and human losses. The unpredictable nature of natural disasters' behavior makes it hard to have a comprehensive situational awareness (SA) to support disaster management. Using opinion surveys…
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.
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.
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.
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.
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…
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%.
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.
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.
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.
Integrating Crowdsourcing and Active Learning for Classification of Work-Life Events from Tweetscs.CL
Crowdsourcing and active learning reduce manual annotation in social media event classification.
problem Manual annotation is time-consuming and resource-intensive in social media event classification.
method Crowdsourcing pipeline combined with active learning strategies.
result Active learning strategies help reduce the number of tweets needed for classification.
The paper proposes a method to learn user representations invariant to social media behavior changes.
problem Difficulty in comparing users over time due to evolving behavior.
method Learning a mapping from user activity to a vector space capturing invariant features.
result The learned mapping enables efficient comparisons of users not seen at training time.
Graph-Hist: Graph Classification from Latent Feature Histograms With Application to Bot Detectioncs.LG
Graph-Hist classifies social media graphs using feature histograms.
problem Classifying large, sparse social media graphs.
method Extracts latent features, bins nodes, and classifies based on multi-channel histograms.
result Improves bot detection in social media graphs.
Study finds meme stocks have unique price and social media dynamics.
problem Exploring unique properties of meme stocks.
method Regime-switching cointegration model.
result Meme stocks exhibit a distinct 'mementum' compared to other high-volume stocks.
Analyze food popularity trends using social media data.
problem Identify trends and popularity of cuisine types across space and time.
method Use off-the-shelf machine learning techniques, Kernel Density Estimation, and Bayesian Networks.
result Model dependencies among food cuisines popularity.
Paper shows pretrained models can categorize Thai social media content effectively.
problem Lack of labeled data for Thai social media content classification.
method Pretrained language models on a large noisy Thai social media corpus, fine-tuned for downstream tasks.
result State-of-the-art results achieved on Thai social text categorization tasks.
Study finds social media investor emotions predict stock prices.
problem Validation of social media sentiment models in predicting market behavior.
method Employed EmTract, an emotion model, to test social media sentiment against lab experiments.
result Firm-specific investor emotions forecast daily asset price movements.
Interactive Learning for Identifying Relevant Tweets to Support Real-time Situational Awarenesscs.SI
Interactive learning improves real-time tweet classification for situational awareness.
problem Difficulty in identifying relevant tweets from noisy social media data.
method Interactive learning framework that incorporates user feedback in real-time.
result Our approach outperforms state-of-the-art models in real-time tweet classification.
Study uses social media to analyze COVID-19 impact.
problem Understanding the global impact of COVID-19.
method Machine learning and linguistic tools to analyze social media posts.
result Automatic detection of positive reports of COVID-19.
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.
Model financial markets with social media influences using hierarchical networks.
problem Understanding social media's impact on financial markets.
method Agent-based model with hierarchical influence network.
result Model accurately simulates real-world financial market behaviors.
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.
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…
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.
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.
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…
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.
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.
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 echo chamber effect resounds on financial markets: a social media alert system for meme stocksq-fin.TR
Paper detects social media influencers affecting financial markets.
problem Impact of social media influencers on financial markets.
method Developed an early warning system for detecting suspicious social network activity.
result Discrepancy in meme and non-meme stocks' reactions to social networks.
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.
Using a method rooted in information theory, we present results that have identified a large set of stocks for which social media can be informative regarding financial volatility. By clustering stocks based on the joint feature sets of social and financial variables, our research provides an important contribution by …
Loss Data Analyticsq-fin.RM
Loss Data Analytics is an interactive, online, freely available text. The idea behind the name Loss Data Analytics is to integrate classical loss data models from applied probability with modern analytic tools. In particular, we seek to recognize that big data (including social media and usage based insurance) are here…
Sentiment analysis from news and social media predicts forex market movements.
problem Forecasting forex market movements using sentiment analysis.
method Lexicon-based analysis and Naive Bayes machine learning.
result Sentiment analysis is effective in predicting forex market movements.
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.
Opinion polls have been the bridge between public opinion and politicians in elections. However, developing surveys to disclose people's feedback with respect to economic issues is limited, expensive, and time-consuming. In recent years, social media such as Twitter has enabled people to share their opinions regarding …
Cryptocurrency Bubble Detection: A New Stock Market Dataset, Financial Task & Hyperbolic Modelscs.CL
New dataset and models detect cryptocurrency bubbles using social media data.
problem Detecting anomalous market behavior in cryptocoins and meme stocks.
method Developed a novel multi-span identification task and sequence-to-sequence hyperbolic models.
result Models effectively detect cryptocoins and meme stocks bubbles in zero-shot settings.
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,…
Tweet Influence on Market Trends: Analyzing the Impact of Social Media Sentiment on Biotech Stocksq-fin.ST
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.
Develops NFCF to reduce gender bias in social media recommendation systems.
problem Reduces gender bias in collaborative filtering systems on social media data.
method Pre-training and fine-tuning neural collaborative filtering with bias correction techniques.
result Achieves better performance and fairness in gender de-biased recommendations.
Paper proposes deep learning for fake claim detection on social media.
problem Spread of false information on social media.
method Extended LIAR dataset with sentiment analysis; BERT-Base model for classification.
result 70% accuracy in classifying claims as genuine or fake.