New metric improves social message classification on Twitter.
problem Efficient classification of short social messages on online networks.
method Dynamic Time Warping distance for message spreading classification.
result Good classification accuracies on Twitter data.
Paper proposes semi-supervised methods for faster, more scalable message stance classification.
problem Determining the veracity of messages in social networks.
method Uses Label Propagation and Label Spreading algorithms for semi-supervised learning.
result Semi-supervised learning outperforms supervised models in terms of accuracy, speed, and scalability.
A key aspect of word of mouth marketing are emotions. Emotions in texts help propagating messages in conventional advertising. In word of mouth scenarios, emotions help to engage consumers and incite to propagate the message further. While the function of emotions in offline marketing in general and word of mouth marke…
Study analyzes misinformation on social media during COVID-19.
problem Misinformation spreads on social media during the COVID-19 pandemic, affecting public health adherence.
method Analysis of model-labeled data, random forest classifier, sentiment analysis.
result Misinformation tweets show more negative sentiment and evolve over time, incorporating details from unrelated theories.
New neural network boosts authorship verification on social media.
problem Challenges in verifying authorship of short, diverse social media messages.
method Proposes a new neural network topology for similarity learning.
result Significantly improved performance on author verification tasks.
Study explores rumor spread on Twitter using supervised learning.
problem Rumors spread quickly on social networks, affecting decision-making.
method Microscopic-level analysis of user interactions and features.
result Models predict rumor diffusion with 90% accuracy.
Social media signals are most informative about financial volatility when sentiment is high.
problem Understanding when social media can predict financial market volatility.
method Cluster analysis of social and financial variables using information theory.
result Social media is most informative about financial volatility when the ratio of bullish to bearish sentiment is high.
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…
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.
Combines PCA and message passing for better graph node embeddings.
problem Challenges of learning long-range dependencies in graph data.
method PCAPass: PCA + message passing for unsupervised node embeddings.
result Empirically shows competitive performance on node classification benchmarks.
Paper proposes PP-GCN for fine-grained social event categorization.
problem Challenges in mining social events due to heterogeneous event elements and social network structures.
method Design an event meta-schema, build an HIN, propose PP-GCN, and use KIES.
result PP-GCN outperforms other techniques in social event detection and clustering.
MGpi model predicts social actions in group interactions.
problem Social interaction among multiple agents and groups.
method Deep neural network with Kinesic-Proxemic-Message gate for social signal gating.
result Achieves state-of-the-art performance in group identification.
New method estimates treatment effects in complex interference settings.
problem Challenges in estimating treatment effects due to unknown interference.
method Higher-order causal message passing for non-linear feature learning.
result Effective estimation of treatment effect dynamics in complex interference.
New algorithm estimates influence in social networks without sampling.
problem Estimating the number of influenced nodes in social networks.
method Dynamic message-passing approach for scalable influence estimation.
result Upper bound on expected spread for general graphs, exact for treelike networks.
The study analyzes sentiment of European tweets during the pandemic.
problem Understanding public sentiment during the COVID-19 pandemic.
method Cross-language sentiment analysis of multilingual tweets using neural networks and sentence embeddings.
result Sentiment analysis reveals that lockdown announcements correlate with a deterioration of mood, which recovers quickly.
Few-shot models detect tweets in emerging disasters efficiently.
problem Detecting relevant tweets in emerging disaster events is challenging.
method Few-shot models (matching networks and prototypical networks) are used to detect tweets in emerging disaster events.
result Few-shot models can generalize to unseen classes with a small amount of examples.
This paper discusses sentiment analysis on social media text.
problem Detecting sentiments in short text messages for applications like mental health monitoring.
method Combines concepts from Natural Language Processing and Machine Learning.
result Explains techniques used in sentiment analysis of textual data.
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.
Investor emotions predict earnings announcements, but excitement lowers returns.
problem The impact of investor emotions on earnings announcements and their returns.
method Social media data analysis over a decade to test the relationship between investor emotions and earnings announcements.
result Excitement about earnings announcements is associated with lower announcement returns.
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.
Mobile phone data predicts users' income levels.
problem Predicting users' income levels from mobile phone data.
method Comparison of feature extraction methods and machine learning techniques.
result Bayesian method based on the communication graph outperforms other methods.
Algorithm ensures fair information spread in social networks with community structure.
problem Disparities in information coverage between communities in social networks.
method Fits a model to the social network, uses community structure, and determines optimal seed allocations for fair coverage.
result Empirical accuracy demonstrated on simulated and real networks.
A new method detects communities in multi-relational networks.
problem Detect communities in heterogeneous multi-relational networks.
method Message passing based algorithm on a hierarchical structure of homogeneous networks.
result Effectiveness of the proposed method confirmed on synthetic and real-world networks.
We tackle the problem of inferring node labels in a partially labeled graph where each node in the graph has multiple label types and each label type has a large number of possible labels. Our primary example, and the focus of this paper, is the joint inference of label types such as hometown, current city, and employe…
We look at a collection of conjectures with the unifying message that smaller social systems, tend to be less complex and can be aligned better, towards fulfilling their intended objectives. We touch upon a framework, referred to as the four pronged approach that can aid the analysis of social systems. The four prongs …
The paper predicts when emails will be opened using survival analysis.
problem Predicting the time to open emails with varying user engagement levels.
method Survival analysis with Cox Proportional Hazards model and mixture model.
result The mixture model achieves the best accuracy in predicting email opening times.
System identifies language of transliterated text.
problem Users struggle to understand non-native language transliterated text.
method Feature extraction of phonetic syllables using LSTM network.
result System accurately identifies language of transliterated text.
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.
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.
TM-GCN learns dynamic graph embeddings using tensor algebra.
problem Handling dynamic graphs in graph neural networks.
method Tensor M-product for dynamic graph convolution.
result TM-GCN outperforms existing methods on edge classification and link prediction.
Computer generated academic papers have been used to expose a lack of thorough human review at several computer science conferences. We assess the problem of classifying such documents. After identifying and evaluating several quantifiable features of academic papers, we apply methods from machine learning to build a b…
Study investor sentiment and disagreement on StockTwits during COVID-19.
problem Understanding investor beliefs and sentiment during the pandemic.
method Analysis of social media data (StockTwits) for investor messages.
result Sentiment and disagreement sharply decreased in early March 2020, followed by a reversal.
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.
We study the relationship between social media output and National Football League (NFL) games, using a dataset containing messages from Twitter and NFL game statistics. Specifically, we consider tweets pertaining to specific teams and games in the NFL season and use them alongside statistical game data to build predic…
Social media hype can misprice IPO stocks, leading to short-term gains but long-term losses.
problem Mispricing of IPO stocks due to social media hype.
method Analysis of investor emotions on StockTwits and Twitter.
result High pre-IPO enthusiasm leads to higher short-term returns but lower long-term returns.
Our society has been computerised and globalised due to emergence and spread of information and communication technology (ICT). This enables us to investigate our own socio-economic systems based on large amounts of data on human activities. In this article, methods of treating complexity arising from a vast amount of …
NEAR improves graph classification by aggregating edge information.
problem Loss of local structure and relationships in 1-hop neighborhood GNNs.
method Proposes NEAR, a framework that aggregates edge information between nodes in the neighborhood.
result NEAR improves graph classification tasks over existing 1-hop based GNN algorithms.
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.
Sparse sampling method for tensor factorization and completion of high rank tensors.
problem Completion of high rank tensors with missing data in recommendation systems.
method Sparse measurements and message-passing algorithms in a high-dimensional limit.
result Theoretical insights and performance analysis of tensor factorization in dense limit.
Improved NER on Turkish tweets using semi-supervised learning and word embeddings.
problem Named Entity Recognition on informal Turkish text types.
method Semi-supervised learning with neural networks and word embeddings.
result Achieved better F-score performances than previous Turkish NER systems.
Graph auto-encoder predicts user-item interactions from graph data.
problem Matrix completion for recommender systems from graph data.
method Differentiable message passing on bipartite graphs.
result Competitive performance on collaborative filtering benchmarks.
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.
New method speeds up GNNs to constant time.
problem Scaling GNNs to large graphs.
method Analyzing uniform node sampling for GNNs.
result Node sampling complexity is constant, independent of graph size.
We propose to learn a kernel-based message operator which takes as input all expectation propagation (EP) incoming messages to a factor node and produces an outgoing message. In ordinary EP, computing an outgoing message involves estimating a multivariate integral which may not have an analytic expression. Learning suc…
Detects radical content on Twitter using textual, psychological, and behavioral signals.
problem Limit the spread of extremist narratives on social media.
method Analyzed extremist material, created contextual text-based model, inferred psychological properties, evaluated on Twitter.
result Radical users exhibit distinguishable textual, psychological, and behavioral properties.
In real-world and online social networks, individuals receive and transmit information in real time. Cascading information transmissions (e.g. phone calls, text messages, social media posts) may be understood as a realization of a diffusion process operating on the network, and its branching path can be represented by …
We analyze oversquashing in topological message-passing using relational structures.
problem Oversquashing in topological message-passing remains understudied.
method A unifying axiomatic framework that bridges graph and topological message-passing.
result Potential to advance topological deep learning.
Message passing is the key to graph neural networks, but new terms are needed to avoid confusion.
problem Current methods of graph neural networks cannot solve all problems over given input graphs.
method Demonstrates that any function of interest can be expressed using pairwise message passing over a modified graph.
result Message passing is the fundamental approach for graph neural networks, and new terms are needed to avoid confusion.