Paper predicts demographics at finer geographic resolutions using geotagged tweets.
problem Limited traditional survey methods for demographics estimates at finer geographic resolutions.
method Adapting prior work to predict gender and race/ethnicity counts at the blockgroup-level.
result Achieves high correlations (0.671 for gender, 0.692 for race) compared to prior work.
This paper tackles tweet classification by identifying purpose and position.
problem Difficulties in determining user intention and attitude in short, informal tweets.
method Transformed tweet classification into a multi-label problem and applied a multi-label classification method with post-processing.
result The method effectively classifies tweet purpose and position, outperforming individual classification methods.
The study analyzes meta-data of viral tweets to distinguish fake news from real ones.
problem Identifying political fake news on Twitter using metadata.
method Analysis of 1.5M tweets from the 2016 US election, focusing on viral tweets.
result Significant differences found in followers, URLs, and user verification.
Deep autoencoders improve health tweet clustering.
problem Accurate clustering of health-related tweets.
method Deep convolutional autoencoders for learning compact tweet representations.
result Clustering performance significantly outperforms conventional methods.
The paper filters tweets to identify those relevant to social unrest.
problem Filtering tweets for social unrest to anticipate events.
method Trained a supervised classifier to label Arabic tweets as relevant to unrest.
result The classifier achieves high reliability in labeling tweets as relevant to unrest.
I-AID categorizes disaster tweets into useful information types.
problem Filtering useful information from social media during disasters.
method Multimodel approach using BERT, GAT, and Relation Network.
result I-AID outperforms state-of-the-art approaches in F1 scores.
Study analyzes tweets from Northeastern libraries to understand public relations efforts.
problem Analyzing vast social media data for public relations.
method Computational methods to extract and analyze tweets.
result Libraries use Twitter to represent services and disseminate information.
Analyzes how Trump's tweets impact global stock markets.
problem Understanding the financial impact of presidential tweets on stock markets.
method Examined tweets from Donald Trump's presidency, collected from The Guardian and Bloomberg, and analyzed their effect on equity indices.
result Identified tweets that significantly influenced stock market indices.
ArSentD-LEV dataset improves sentiment analysis in Levantine Arabic tweets.
problem Challenges in sentiment analysis of Arabic tweets, especially Levantine dialect.
method Created a dataset of 4,000 Levantine Arabic tweets with detailed sentiment and topic annotations.
result Improved performance of sentiment classifiers with detailed annotations.
Paper presents a model for identifying informative COVID-19 tweets.
problem Identifying informative COVID-19 tweets on Twitter.
method Leveraged transformers (RoBERTa, XLNet, BERTweet) trained in Semi-Supervised Learning (SSL) setting.
result Achieved F1 score of 0.9011 on test set, ranking 7th on leaderboard.
Deep learning model extracts location references from tweets during emergencies.
problem Challenges in extracting reliable location information from tweets during crises.
method Convolutional Neural Network (CNN) based model.
result Achieved high accuracy in extracting location references from tweets.
Improved neural model predicts gender from tweets.
problem Predicting gender from Twitter text.
method RNN model with attention, LSA-reduced n-gram features.
result Improved model achieves state-of-the-art performance on English tweets.
Cluster analysis is a field of data analysis that extracts underlying patterns in data. One application of cluster analysis is in text-mining, the analysis of large collections of text to find similarities between documents. We used a collection of about 30,000 tweets extracted from Twitter just before the World Cup st…
Deep learning model classifies tweets for disaster rescue scheduling.
problem Efficiently processing and categorizing tweets for disaster rescue.
method Combining attention-based Bi-directional LSTM and CNN with pre-trained word vectors for classification and feature engineering.
result Proposed model outperforms other methods in Precision, Recall, F1-score, and Accuracy.
Study categorizes and analyzes emotions in sexist tweets.
problem Lack of defined categories for sexism in NLP.
method Used a new dataset from SemEval-2018 to classify and analyze emotions in sexist tweets.
result Demonstrated the mental state and affectual state of users who tweet in different categories of sexism.
D2KLab's approach predicts tweet engagement using two stages.
problem Predicting user engagement with tweets.
method Two-stage approach: feature learning and ensemble XGBoost.
result Ranked 22 in the 2020 RecSys Challenge leaderboard.
Paper proposes Experts Model for better emotion detection in tweets.
problem Estimating intensity of emotion in tweets.
method Inspired by Mixture of Experts (MoE) model, each expert learns different features.
result Our Experts Model stands at top-5 results in emotion detection.
Dataset analyzes tweets' impact on stock returns.
problem Understanding how public opinion affects stock market outcomes.
method Created a dataset of 862,231 labeled tweets, provided baselines and multi-view learning approach.
result Demonstrated the impact of tweets on stock returns over various time frames.
Framework detects cyber threats from Twitter tweets.
problem Time-consuming manual extraction of cyber threat intelligence.
method Novelty detection model trained on CVE data.
result F1-score of 0.643 for classifying cyber threat tweets.
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.
New method detects psychosocial factors linked to gang violence on social media.
problem Detecting psychosocial factors in gang-related social media posts.
method Multimodal analysis of tweets with images and text, using various classification methods.
result Multimodal approach improves classification performance by 18%.
We perform a large-scale analysis of language diatopic variation using geotagged microblogging datasets. By collecting all Twitter messages written in Spanish over more than two years, we build a corpus from which a carefully selected list of concepts allows us to characterize Spanish varieties on a global scale. A clu…
New tool detects weak and strong Islamophobic hate speech on social media.
problem Detecting Islamophobic hate speech on social media is challenging due to its varied nature.
method Built a multi-class classifier distinguishing between non-Islamophobic, weak Islamophobic, and strong Islamophobic content using GloVe word embeddings.
result Accuracy of 77.6% and balanced accuracy of 83% on a dataset of 109,488 tweets.
Simple model classifies traffic tweets from Twitter using word embeddings.
problem Sparsity and curse of dimensionality in bag-of-words representation for traffic tweets.
method Proposes a word embedding-based framework to classify traffic-related tweets from non-traffic ones.
result State-of-the-art test accuracy of 95.9% achieved with a simple model.
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.
Research uses Twitter data to identify health issues in gay users.
problem Lack of information on health issues of LGBTQ people.
method Collected and analyzed tweets from gay users on health topics.
result Identified 11 diseases in 7 categories.
This paper tackles spam detection on Twitter by analyzing correlated features.
problem Spam detection on social media, especially Twitter, to improve user experience.
method Extracted tweet-based and user-based features, identified correlated features, and used artificial neural networks for classification.
result Achieved 97.57% accuracy in classifying tweets as spam or non-spam.
Improved offensive language detection in tweets with multiple deep learning models.
problem Detecting offensive language in tweets using machine learning.
method Combination of multiple deep learning architectures for classification.
result Achieved macro-average F1-scores of 0.76, 0.68, 0.54 for different tasks.
A dynamic keyword selection model for topic modeling of tweets.
problem Adjusting keywords dynamically to mimic past topics with novelty.
method Generative process selects keywords and documents, trained with variational lower bound and stochastic gradient optimization.
result Keyword-based topic model outperforms a sophisticated baseline model by 67%.
Study predicts cryptocurrency price movements using Twitter sentiment analysis.
problem Predicting short-term price movements of cryptocurrencies.
method Conditional examination of return and excess return rates following tweet publication.
result Statistically significant increases in return rates within the first three minutes after tweet publication.
Study analyzes Twitter tweets to identify negative sentiments about obesity, diet, diabetes, and exercise.
problem Understanding public opinions and sentiments about obesity, diet, diabetes, and exercise.
method Uses sentiment analysis and topic modeling on 6 million Twitter tweets.
result Identifies prominent topics related to negative sentiments about DDEO (diet, diabetes, exercise, obesity).
System detects financial forecasts in tweets, achieving high precision.
problem Detecting financial forecasts in social media messages.
method Natural Language Processing and Machine Learning techniques for real-time analysis.
result Achieves over 90% precision for financial forecasts.
Study uses LLMs to categorize financial tweets, revealing useful sentiment signals.
problem Discovering meaningful sentiment signals from unstructured financial social media data.
method Leveraged LLMs to automatically label financial tweets with event categories and aligned with returns.
result Certain event labels consistently yield negative alpha, with statistically significant Sharpe ratios and information coefficients.
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.
This paper classifies tweets into positive and negative sentiments using distributed word and sentence representations.
problem Classifying tweets into positive and negative sentiments.
method Used distributed representations of words and sentences, and LSTM and CNN networks for classification.
result Achieved accuracies as high as 81%.
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.
Adversarial tweets can fool stock prediction models, causing financial loss.
problem Vulnerability of stock prediction models to adversarial attacks on social media.
method Solving combinatorial optimization problems with semantic and budget constraints to generate adversarial tweets.
result Adversarial tweets can fool stock prediction models and cause significant financial loss.
Study shows tweets about COVID-19 can predict stock market performance.
problem Understanding the impact of COVID-19 on stock markets.
method Text sentiment analysis of Twitter data to correlate tweets about COVID-19 with stock market performance.
result Strong relationship between COVID-19 sentiment and stock market performance can be predicted.
This paper improves typhoon intensity prediction using social media data and semantic word embeddings.
problem Short-term disaster prediction from historical data alone is limited.
method Combining semantically-enriched word embeddings with traditional word2vec for social media data, and an end-to-end learning framework.
result Our approach outperforms state-of-the-art baselines in typhoon intensity prediction.
Study analyzes global public sentiment on DeFi from 2012-2022.
problem Global public sentiment on DeFi is understudied.
method Sentiment analysis, spatial econometrics, clustering, topic modeling.
result Economic development significantly influences DeFi engagement, especially after 2015.
Analyzes Twitter users' opinions on self-driving cars.
problem Understanding public perception of self-driving cars.
method Annotated Twitter dataset, topic modeling, sentiment classification using Twitter features.
result People are generally optimistic but also concerned about self-driving cars.
The study builds a classifier to filter trustworthy tweets from rumours on Twitter.
problem Misinformation on social media, specifically rumours on Twitter.
method Collected 100 million tweets, identified 72 rumours, used 80 measures for trustworthiness, trained machine learning classifiers.
result The model was more accurate than previous studies, identifying key attributes of trustworthy tweets.
Study predicts Bitcoin volatility using Twitter data.
problem Forecasting Bitcoin volatility with social media data.
method Deep learning models using Twitter data, including semantic and user statistics.
result Temporal convolutional networks outperform other models in volatility prediction.
Taureau uses Twitter sentiment analysis to predict stock market movement.
problem Predicting stock market movement using public opinion on Twitter.
method Obtained historical tweets, filtered and labeled, generated word embeddings, assessed sentiment scores, correlated with stock price movement, designed and evaluated predictive model.
result Taureau can predict stock price movement from lagged sentiment scores.
New algorithms improve semantic similarity in mobile datasets by incorporating context.
problem Traditional models fail to capture external context in mobile datasets.
method Developed new algorithms to integrate external features into sentence embeddings and semantic similarity scores.
result Applying PCA with eight components and appending multimodal features improves semantic understanding in tweets.
Predicts morning traffic congestion using social media data from the previous evening.
problem Challenges in predicting early morning traffic dynamics.
method Mining Twitter messages to understand evening/midnight work and rest patterns.
result People's tweeting patterns before the morning commute are associated with traffic congestion.
Predicts Bitcoin price using Twitter sentiment analysis.
problem Volatility and varied opinions in cryptocurrency markets.
method Developed a model combining sentiment analysis of tweets and historical price data.
result Sentiment prediction MAPE of 9.45%, price prediction MAPE of 3.6%
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.