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.
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.
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.
TIMME detects Twitter users' ideology from sparse, heterogeneous data.
problem Predicting political tendencies from Twitter data.
method TIMME, a multi-task multi-relational embedding model.
result TIMME outperforms state-of-the-art models for ideology detection.
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.
This study analyzes Twitter posts from US west coast libraries.
problem Lack of automated methods to analyze thousands of library tweets.
method Used Twitter API to collect and analyze 138,000 tweets.
result Identified 20 topics in library tweets, categorized into public relations, book, event, training, and social good.
Twitter releases dataset to study user engagement on Home Timeline, focusing on privacy.
problem Limited exploration of user feedback on Twitter's Home Timeline.
method Release of a large-scale dataset of 160 million Tweets with engagement information.
result Demonstrates challenges in predicting user engagements and privacy concerns.
Paper introduces score embedding for Twitter sentiment analysis of health care issues.
problem Analyzing public opinion on health care plans using social media.
method Score embedding, a neural network model for word representations.
result Score embedding effectively captures sentiment and outperforms existing methods.
Twitmo analyzes geo-tagged Twitter data for topic modeling and visualization.
problem Analyzing public discourse on Twitter for various topics, parties, or individuals.
method Collects and preprocesses geo-tagged Tweets, applies LDA, CTM, STM, and visualizes results.
result Automatic pooling of Tweets into pseudo-documents improves topic coherence.
Visualizes text models with in-text and word-as-pixel highlighting.
problem Diagnosing and understanding text models, especially for slang and historical texts.
method In-text annotations and word-as-pixel graphics.
result Interconnected methods help diagnose and understand text models.
Image of an entity can be defined as a structured and dynamic representation which can be extracted from the opinions of a group of users or population. Automatic extraction of such an image has certain importance in political science and sociology related studies, e.g., when an extended inquiry from large-scale data i…
Study finds strong link between crypto narratives and prices.
problem Understanding the impact of crypto narratives on prices.
method Topic modeling of Twitter data combined with sentiment analysis.
result Strong correlation between narratives and crypto prices.
Study shows Twitter sentiments predict stock price fluctuations.
problem Predicting stock prices using public opinions.
method Time series analysis and natural language processing with LSTM model.
result Positive, negative, and subjective sentiments correlate with stock price changes.
Fast, global, and sensitively reacting to political, economic and social events of any kind, these are attributes that social media like Twitter share with foreign exchange markets. The leading assumption of this paper is that information which can be distilled from public debates on Twitter has predictive content for …
Bayesian method infers ages of 700M Twitter users from follow data.
problem Lack of demographic data on Twitter users.
method Bayesian framework to generalize age information from a few users to the entire network.
result Highly accurate age inference for 700 million Twitter accounts.
This study uses Twitter to analyze traveler behavior in Manhattan.
problem Analyzing traveler behavior using social media data.
method Systematic method to extract displacement information from geo-tagged tweets.
result Twitter reveals unique demographics and travel behavior patterns.
Twitter promotes cryptocurrency pump-and-dumps, affecting trading behavior and returns.
problem The influence of Twitter on cryptocurrency pump-and-dump events.
method Analysis of abnormal returns, trading volume, and tweet activity.
result Investors relying on Twitter information sell later, leading to significant losses.
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.
Proposes a neural network model to improve predictions in biased datasets.
problem Reduces bias in predictions from datasets with selection bias.
method Integrates meta information about bias direction and quantity into a neural network model.
result Improves prediction accuracy in electoral polls with biased training data.
Analysis of opinion dynamics in social networks plays an important role in today's life. For applications such as predicting users' political preference, it is particularly important to be able to analyze the dynamics of competing opinions. While observing the evolution of polar opinions of a social network's users ove…
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.
Study validates Twitter search protocol for health and disease events.
problem Validating search protocols for health-related tweets on Twitter.
method Extracted and analyzed health and disease tweets using predefined keywords, trained CART algorithms for predictions.
result Validation showed good accuracy (AUC > 0.8) in detecting relevant health and disease tweets.
A novel deep learning method predicts Twitter users' locations using multiple data types.
problem Predicting Twitter users' locations on large social networks.
method Combines content-based and network-based approaches using a multi-entry neural network architecture (MENET).
result MENET outperforms state-of-the-art methods by a large margin on three benchmark datasets.
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.
Research shows Twitter is permeable to financial events, influencing its content and sentiment.
problem Investigating how Twitter reacts to financial events.
method Conducted experiments on a specific financial event (Tesco PLC and Booker Group PLC merger announcement).
result Twitter is permeable to financial events, affecting its content and sentiment.
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.
Study examines how cluster number affects short-text clustering, introducing a stability metric.
problem Challenges in finding meaningful clusters in short-text data.
method Introduces a stability metric to determine cluster robustness and visualizes cluster subdivisions.
result Choosing a cluster number involves balancing informativeness and complexity, not seeking a single 'optimal' solution.
Twitter has been proven to be a notable source for predictive modelling on various domains such as the stock market, the dissemination of diseases or sports outcomes. However, such a study has not been conducted in football (soccer) so far. The purpose of this research was to study whether data mined from Twitter can b…
Amobee's system won 3rd place in Twitter sentiment classification.
problem Sentiment detection on Twitter using deep learning.
method RNN models trained on a sentiment treebank, combined with classifiers.
result 3rd place in SemEval 2017 task 4, 5-label classification.
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.
State-of-the-art Twitter sentiment analysis using CNNs and LSTMs.
problem Improving Twitter sentiment classification accuracy.
method Pre-trained word embeddings, distant supervision, fine-tuning, ensemble of CNNs and LSTMs.
result First rank on all five English subtasks of SemEval-2017.
Twitter data reveals global movement patterns for social good.
problem Lack of accessible, unbiased, high-resolution spatial data.
method Used geolocation data from Twitter to estimate global mobility patterns.
result Demonstrated Twitter data can address spatial data shortcomings.
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.
Twitter system detects unexpected epidemics.
problem Detecting sudden, unexpected epidemic outbreaks in Twitter.
method Dynamic classification, alert generation, and ranking/recommendation.
result Empirical evaluation and validation with domain experts.
Study maps Spanish dialects on Twitter, revealing urban vs regional variations.
problem Understanding linguistic diversity across Spanish-speaking regions.
method Geographically tagged Twitter corpus, machine learning for dialect analysis.
result Urban dialects show international characteristics, rural dialects regional uniformity.
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…
ED-Filter improves eating disorder classification on Twitter.
problem High dimensionality and extensive feature sets in Twitter data for ED classification.
method Informed branch and bound search technique with hybrid greedy-based deep learning.
result Significant improvements in classification accuracy and efficiency.
New algorithm detects network outliers with missing links.
problem Identifying outliers in networks with missing links.
method Proposes a new algorithm that detects outliers and predicts missing links.
result Proves the algorithm exactly detects outliers and achieves best error for missing link prediction.
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…
Study enhances cryptocurrency sentiment analysis using TikTok and Twitter data.
problem Lack of comprehensive sentiment analysis in cryptocurrency markets.
method Multimodal analysis of TikTok and Twitter data using large language models.
result TikTok's video sentiment influences speculative assets and short-term trends.
Study uses deep learning to predict gender and analyze HPV vaccine perceptions on Twitter.
problem Analyzing gender differences in public perceptions on HPV vaccine using social media data.
method Convolutional neural network model trained on Twitter text for gender prediction, then applied to HPV vaccine related tweets.
result Identified gender differences in public perceptions on HPV vaccine, consistent with previous studies.
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.
This paper examines feature selection for extracting user intentions from Twitter.
problem Extracting user intentions from informal, misspelled tweets.
method Developed a dataset from Twitter feeds, used two feature selection techniques (Information Gain and hybrid forward selection), and applied four classification algorithms.
result The hybrid feature selection approach outperformed the Information Gain method.
Crowdsourced algorithms identify fake news on Twitter.
problem Identifying fake news on social media platforms.
method Evaluation of reputation algorithms on a large dataset of Twitter news.
result Simple crowdsourcing-based algorithms can identify a significant portion of fake news with low false positive rates.
Twitter uses embedding models to maintain model performance despite covariate shift.
problem Model staleness due to covariate shift in Twitter data.
method Employ learned features (embedding models) to represent relevant aspects of the data distribution.
result Embedding models reduce staleness and maintain model performance.
Study examines perceptions and attitudes about breast cancer on Twitter.
problem Understanding public perceptions and attitudes towards breast cancer on social media.
method Identified and collected tweets, used topic modeling and sentiment analysis.
result Identified themes and quantified users' perceptions and emotions about breast cancer.
Study shows frequent 'stock' mentions on Twitter correlate with stock market declines.
problem Understanding how social media impacts financial markets during pandemics.
method Collected over 26 million tweets about COVID-19, analyzed using natural language processing.
result More frequent use of 'stock' in tweets predicts substantial declines in stock market indices.