Search Results for author:
Found 132 papers, 54 papers with code
DUTNLP Machine Translation System for WMT21 Triangular Translation Task
This paper describes DUT-NLP Lab’s submission to the WMT-21 triangular machine translation shared task.
STANCE-C3: Domain-adaptive Cross-target Stance Detection via Contrastive Learning and Counterfactual Generation
We also propose a modified self-supervised contrastive learning as a component of STANCE-C3 to prevent overfitting for the existing domain and target and enable cross-target stance detection.
Disinformation Detection: An Evolving Challenge in the Age of LLMs
A holistic exploration for the formation and detection of disinformation is conducted to foster this line of research.
LLMs as Counterfactual Explanation Modules: Can ChatGPT Explain Black-box Text Classifiers?
Large language models (LLMs) are increasingly being used for tasks beyond text generation, including complex tasks such as data labeling, information extraction, etc.
Unified Frequency-Assisted Transformer Framework for Detecting and Grounding Multi-Modal Manipulation
Moreover, to address the semantic conflicts between image and frequency domains, the forgery-aware mutual module is developed to further enable the effective interaction of disparate image and frequency features, resulting in aligned and comprehensive visual forgery representations.
ConDA: Contrastive Domain Adaptation for AI-generated Text Detection
Given the potential malicious nature in which these LLMs can be used to generate disinformation at scale, it is important to build effective detectors for such AI-generated text.
J-Guard: Journalism Guided Adversarially Robust Detection of AI-generated News
The rapid proliferation of AI-generated text online is profoundly reshaping the information landscape.
UAV 3-D path planning based on MOEA/D with adaptive areal weight adjustment
Unmanned aerial vehicles (UAVs) are desirable platforms for time-efficient and cost-effective task execution.
Neural Authorship Attribution: Stylometric Analysis on Large Language Models
Large language models (LLMs) such as GPT-4, PaLM, and Llama have significantly propelled the generation of AI-crafted text.
Group Pose: A Simple Baseline for End-to-End Multi-person Pose Estimation
State-of-the-art solutions adopt the DETR-like framework, and mainly develop the complex decoder, e. g., regarding pose estimation as keypoint box detection and combining with human detection in ED-Pose, hierarchically predicting with pose decoder and joint (keypoint) decoder in PETR.
Causality Guided Disentanglement for Cross-Platform Hate Speech Detection
By disentangling input into platform-dependent features (useful for predicting hate targets) and platform-independent features (used to predict the presence of hate), we learn invariant representations resistant to distribution shifts.
Fighting Fire with Fire: Can ChatGPT Detect AI-generated Text?
Large language models (LLMs) such as ChatGPT are increasingly being used for various use cases, including text content generation at scale.
UPREVE: An End-to-End Causal Discovery Benchmarking System
Discovering causal relationships in complex socio-behavioral systems is challenging but essential for informed decision-making.
FDCT: Fast Depth Completion for Transparent Objects
To address these challenges, we propose a Fast Depth Completion framework for Transparent objects (FDCT), which also benefits downstream tasks like object pose estimation.
Quantifying the Echo Chamber Effect: An Embedding Distance-based Approach
To facilitate measuring distances between users, we propose EchoGAE, a self-supervised graph autoencoder-based user embedding model that leverages users' posts and the interaction graph to embed them in a manner that reflects their ideological similarity.
Contrastive Meta-Learning for Few-shot Node Classification
First, we propose to enhance the intra-class generalizability by involving a contrastive two-step optimization in each episode to explicitly align node embeddings in the same classes.
PEACE: Cross-Platform Hate Speech Detection- A Causality-guided Framework
Hate speech detection refers to the task of detecting hateful content that aims at denigrating an individual or a group based on their religion, gender, sexual orientation, or other characteristics.
Inductive Linear Probing for Few-shot Node Classification
Meta-learning has emerged as a powerful training strategy for few-shot node classification, demonstrating its effectiveness in the transductive setting.
Virtual Node Tuning for Few-shot Node Classification
Our approach utilizes a pretrained graph transformer as the encoder and injects virtual nodes as soft prompts in the embedding space, which can be optimized with few-shot labels in novel classes to modulate node embeddings for each specific FSNC task.
Learning Strong Graph Neural Networks with Weak Information
Accordingly, we propose D$^2$PT, a dual-channel GNN framework that performs long-range information propagation not only on the input graph with incomplete structure, but also on a global graph that encodes global semantic similarities.
HyperFormer: Learning Expressive Sparse Feature Representations via Hypergraph Transformer
Learning expressive representations for high-dimensional yet sparse features has been a longstanding problem in information retrieval.
SwinFSR: Stereo Image Super-Resolution using SwinIR and Frequency Domain Knowledge
Stereo Image Super-Resolution (stereoSR) has attracted significant attention in recent years due to the extensive deployment of dual cameras in mobile phones, autonomous vehicles and robots.
A Data-Centric Solution to NonHomogeneous Dehazing via Vision Transformer
Although it is possible to augment the NH-HAZE23 dataset by leveraging other non-homogeneous dehazing datasets, we observe that it is necessary to design a proper data-preprocessing approach that reduces the distribution gaps between the target dataset and the augmented one.
Fairness through Aleatoric Uncertainty
We then propose a principled model to improve fairness when aleatoric uncertainty is high and improve utility elsewhere.
Contrastive Semi-supervised Learning for Underwater Image Restoration via Reliable Bank
Despite the remarkable achievement of recent underwater image restoration techniques, the lack of labeled data has become a major hurdle for further progress.
Stylometric Detection of AI-Generated Text in Twitter Timelines
However, tweets are inherently short, thus making it difficult for current state-of-the-art pre-trained language model-based detectors to accurately detect at what point the AI starts to generate tweets in a given Twitter timeline.
Towards Detecting Harmful Agendas in News Articles
Manipulated news online is a growing problem which necessitates the use of automated systems to curtail its spread.
STERLING: Synergistic Representation Learning on Bipartite Graphs
Self-Supervised Learning (SSL) is a promising paradigm to address this challenge.
Nothing Stands Alone: Relational Fake News Detection with Hypergraph Neural Networks
Nowadays, fake news easily propagates through online social networks and becomes a grand threat to individuals and society.
Ranked #1 on
Graph Classification
on UPFD-POL
Transductive Linear Probing: A Novel Framework for Few-Shot Node Classification
More recently, inspired by the development of graph self-supervised learning, transferring pretrained node embeddings for few-shot node classification could be a promising alternative to meta-learning but remains unexposed.
FECAM: Frequency Enhanced Channel Attention Mechanism for Time Series Forecasting
However, use of FT is problematic due to Gibbs phenomenon.
Distributional Shift Adaptation using Domain-Specific Features
Machine learning algorithms typically assume that the training and test samples come from the same distributions, i. e., in-distribution.
GOOD-D: On Unsupervised Graph Out-Of-Distribution Detection
As a pioneering work in unsupervised graph-level OOD detection, we build a comprehensive benchmark to compare our proposed approach with different state-of-the-art methods.
Improving ProtoNet for Few-Shot Video Object Recognition: Winner of ORBIT Challenge 2022
In this work, we present the winning solution for ORBIT Few-Shot Video Object Recognition Challenge 2022.
Domain Generalization -- A Causal Perspective
As a result models that rely on this assumption exhibit poor generalization capabilities.
Debiasing Word Embeddings with Nonlinear Geometry
Therefore, this work studies biases associated with multiple social categories: joint biases induced by the union of different categories and intersectional biases that do not overlap with the biases of the constituent categories.
Toward Robust Graph Semi-Supervised Learning against Extreme Data Scarcity
The success of graph neural networks on graph-based web mining highly relies on abundant human-annotated data, which is laborious to obtain in practice.
Trustworthy Recommender Systems
However, researchers lack a systematic overview and discussion of the literature in this novel and fast developing field of TRSs.
Estimating Topic Exposure for Under-Represented Users on Social Media
To reduce the bias induced by the contributors, in this work, we focus on highlighting the engagers' contributions in the observed data as they are more likely to contribute when compared to lurkers, and they comprise a bigger population as compared to the contributors.
Few-Shot Class-Incremental Learning via Entropy-Regularized Data-Free Replay
In this paper, we show through empirical results that adopting the data replay is surprisingly favorable.
class-incremental learning
Few-Shot Class-Incremental Learning
+2
CoVaxNet: An Online-Offline Data Repository for COVID-19 Vaccine Hesitancy Research
Despite the astonishing success of COVID-19 vaccines against the virus, a substantial proportion of the population is still hesitant to be vaccinated, undermining governmental efforts to control the virus.
Toward Understanding Bias Correlations for Mitigation in NLP
In particular, we examine bias mitigation in two common NLP tasks -- toxicity detection and word embeddings -- on three social identities, i. e., race, gender, and religion.
NTIRE 2022 Challenge on Efficient Super-Resolution: Methods and Results
The aim was to design a network for single image super-resolution that achieved improvement of efficiency measured according to several metrics including runtime, parameters, FLOPs, activations, and memory consumption while at least maintaining the PSNR of 29. 00dB on DIV2K validation set.
Characterizing Multi-Domain False News and Underlying User Effects on Chinese Weibo
False news that spreads on social media has proliferated over the past years and has led to multi-aspect threats in the real world.
Causal Disentanglement with Network Information for Debiased Recommendations
Aside from the user conformity, aspects of confounding such as item popularity present in the network information is also captured in our method with the aid of \textit{causal disentanglement} which unravels the learned representations into independent factors that are responsible for (a) modeling the exposure of an item to the user, (b) predicting the ratings, and (c) controlling the hidden confounders.
Logistics in the Sky: A Two-phase Optimization Approach for the Drone Package Pickup and Delivery System
Different from the previous urban distribution mode that depends on trucks, this paper proposes a novel package pick-up and delivery mode and system in which multiple drones collaborate with automatic devices.
Supervised Graph Contrastive Learning for Few-shot Node Classification
Graphs are present in many real-world applications, such as financial fraud detection, commercial recommendation, and social network analysis.
Text Transformations in Contrastive Self-Supervised Learning: A Review
Contrastive self-supervised learning has become a prominent technique in representation learning.
Few-Shot Learning on Graphs
In light of this, few-shot learning on graphs (FSLG), which combines the strengths of graph representation learning and few-shot learning together, has been proposed to tackle the performance degradation in face of limited annotated data challenge.
Deep Graph Learning for Anomalous Citation Detection
To address this open issue, we propose a novel deep graph learning model, namely GLAD (Graph Learning for Anomaly Detection), to identify anomalies in citation networks.
Physics-Informed Graph Learning
The graph learning models suffer from the inability to efficiently learn graph information.
Eliciting Structural and Semantic Global Knowledge in Unsupervised Graph Contrastive Learning
In general, the contrastive learning process in GCL is performed on top of the representations learned by a graph neural network (GNN) backbone, which transforms and propagates the node contextual information based on its local neighborhoods.
Data Augmentation for Deep Graph Learning: A Survey
In this survey, we formally formulate the problem of graph data augmentation and further review the representative techniques and their applications in different deep graph learning problems.
Domain Adaptive Fake News Detection via Reinforcement Learning
With social media being a major force in information consumption, accelerated propagation of fake news has presented new challenges for platforms to distinguish between legitimate and fake news.
"This is Fake! Shared it by Mistake": Assessing the Intent of Fake News Spreaders
To distinguish between intentional versus unintentional spreading, we study the psychological explanations of unintentional spreading.
Evaluation Methods and Measures for Causal Learning Algorithms
To bridge from conventional causal inference (i. e., based on statistical methods) to causal learning with big data (i. e., the intersection of causal inference and machine learning), in this survey, we review commonly-used datasets, evaluation methods, and measures for causal learning using an evaluation pipeline similar to conventional machine learning.
MetaFSCIL: A Meta-Learning Approach for Few-Shot Class Incremental Learning
The learning objective of these methods is often hand-engineered and is not directly tied to the objective (i. e. incrementally learning new classes) during testing.
class-incremental learning
Few-Shot Class-Incremental Learning
+2
Towards Multi-Domain Single Image Dehazing via Test-Time Training
Motivated by this observation, we propose a test-time training method which leverages a helper network to assist the dehazing model in better adapting to a domain of interest.
Graph Few-shot Class-incremental Learning
The ability to incrementally learn new classes is vital to all real-world artificial intelligence systems.
class-incremental learning
Few-Shot Class-Incremental Learning
+3
Estimating Causal Effects of Multi-Aspect Online Reviews with Multi-Modal Proxies
This work empirically examines the causal effects of user-generated online reviews on a granular level: we consider multiple aspects, e. g., the Food and Service of a restaurant.
Meta Propagation Networks for Graph Few-shot Semi-supervised Learning
Inspired by the extensive success of deep learning, graph neural networks (GNNs) have been proposed to learn expressive node representations and demonstrated promising performance in various graph learning tasks.
A Survey on Echo Chambers on Social Media: Description, Detection and Mitigation
These mechanisms could be manifested in two forms: (1) the bias of social media's recommender systems and (2) internal biases such as confirmation bias and homophily.
A Novel Sequence Tagging Framework for Consumer Event-Cause Extraction
To this end, we introduce a fresh perspective to revisit the relational event-cause extraction task and propose a novel sequence tagging framework, instead of extracting event types and events-causes separately.
Pseudo Supervised Monocular Depth Estimation with Teacher-Student Network
Despite recent improvement of supervised monocular depth estimation, the lack of high quality pixel-wise ground truth annotations has become a major hurdle for further progress.
Effects of Multi-Aspect Online Reviews with Unobserved Confounders: Estimation and Implication
Online review systems are the primary means through which many businesses seek to build the brand and spread their messages.
Cross-Domain Lossy Compression as Optimal Transport with an Entropy Bottleneck
We study the problem of cross-domain lossy compression where the reconstruction distribution is different from the source distribution in order to account for distributional shift due to processing.
Learning to Selectively Learn for Weakly-supervised Paraphrase Generation
In this work, we go beyond the existing paradigms and propose a novel approach to generate high-quality paraphrases with weak supervision data.
Semantics-Guided Contrastive Network for Zero-Shot Object detection
To address these issues, we develop a novel Semantics-Guided Contrastive Network for ZSD, named ContrastZSD, a detection framework that first brings contrastive learning mechanism into the realm of zero-shot detection.
Ranked #3 on
Zero-Shot Object Detection
on MS-COCO
Contrastive Learning
Generalized Zero-Shot Object Detection
+2
NI-UDA: Graph Adversarial Domain Adaptation from Non-shared-and-Imbalanced Big Data to Small Imbalanced Applications
We propose a new general Graph Adversarial Domain Adaptation (GADA) based on semantic knowledge reasoning of class structure for solving the problem of unsupervised domain adaptation (UDA) from the big data with non-shared and imbalanced classes to specified small and imbalanced applications (NI-UDA), where non-shared classes mean the label space out of the target domain.
Mitigating Bias in Session-based Cyberbullying Detection: A Non-Compromising Approach
The element of repetition in cyberbullying behavior has directed recent computational studies toward detecting cyberbullying based on a social media session.
Robust Graph Meta-learning for Weakly-supervised Few-shot Node Classification
Graphs are widely used to model the relational structure of data, and the research of graph machine learning (ML) has a wide spectrum of applications ranging from drug design in molecular graphs to friendship recommendation in social networks.
FBAdTracker: An Interactive Data Collection and Analysis Tool for Facebook Advertisements
The growing use of social media has led to drastic changes in our decision-making.
Graph Learning: A Survey
In this survey, we present a comprehensive overview on the state-of-the-art of graph learning.
Causal Learning for Socially Responsible AI
The goal of this survey is to bring forefront the potentials and promises of CL for SRAI.
A Two-branch Neural Network for Non-homogeneous Dehazing via Ensemble Learning
Recently, there has been rapid and significant progress on image dehazing.
DW-GAN: A Discrete Wavelet Transform GAN for NonHomogeneous Dehazing
By utilizing wavelet transform in DWT branch, our proposed method can retain more high-frequency knowledge in feature maps.
Few-shot Network Anomaly Detection via Cross-network Meta-learning
Network anomaly detection aims to find network elements (e. g., nodes, edges, subgraphs) with significantly different behaviors from the vast majority.
Causal Mediation Analysis with Hidden Confounders
An important problem in causal inference is to break down the total effect of a treatment on an outcome into different causal pathways and to quantify the causal effect in each pathway.
Causal Inference for Time series Analysis: Problems, Methods and Evaluation
In this paper, we focus on two causal inference tasks, i. e., treatment effect estimation and causal discovery for time series data, and provide a comprehensive review of the approaches in each task.
Indirect Domain Shift for Single Image Dehazing
Therefore, it is capable of consolidating the expressibility of different architectures, resulting in a more accurate indirect domain shift (IDS) from the hazy images to that of clear images.
Socially Responsible AI Algorithms: Issues, Purposes, and Challenges
In this survey, we provide a systematic framework of Socially Responsible AI Algorithms that aims to examine the subjects of AI indifference and the need for socially responsible AI algorithms, define the objectives, and introduce the means by which we may achieve these objectives.
"Let's Eat Grandma": Does Punctuation Matter in Sentence Representation?
Neural network-based embeddings have been the mainstream approach for creating a vector representation of the text to capture lexical and semantic similarities and dissimilarities.
Fact-Enhanced Synthetic News Generation
The existing text generation methods either afford limited supplementary information or lose consistency between the input and output which makes the synthetic news less trustworthy.
MM-COVID: A Multilingual and Multimodal Data Repository for Combating COVID-19 Disinformation
The COVID-19 epidemic is considered as the global health crisis of the whole society and the greatest challenge mankind faced since World War Two.
Social and Information Networks Computers and Society
Be More with Less: Hypergraph Attention Networks for Inductive Text Classification
Text classification is a critical research topic with broad applications in natural language processing.
Improving Cyberbully Detection with User Interaction
Cyberbullying, identified as intended and repeated online bullying behavior, has become increasingly prevalent in the past few decades.
Topic-Preserving Synthetic News Generation: An Adversarial Deep Reinforcement Learning Approach
In this paper, we consider realistic news as news that cannot be easily detected by a fake news classifier.
Disinformation in the Online Information Ecosystem: Detection, Mitigation and Challenges
We then proceed to discuss the inherent challenges in disinformation research, and then elaborate on the computational and interdisciplinary approaches towards mitigation of disinformation, after a short overview of the various directions explored in detection efforts.
Toward Privacy and Utility Preserving Image Representation
Face images are rich data items that are useful and can easily be collected in many applications, such as in 1-to-1 face verification tasks in the domain of security and surveillance systems.
Causal Adversarial Network for Learning Conditional and Interventional Distributions
The LGN is a GAN-based architecture which learns and samples from the causal model over labels.
Long-Term Effect Estimation with Surrogate Representation
Second, short-term outcomes are often directly used as the proxy of the primary outcome, i. e., the surrogate.
Combating Disinformation in a Social Media Age
The creation, dissemination, and consumption of disinformation and fabricated content on social media is a growing concern, especially with the ease of access to such sources, and the lack of awareness of the existence of such false information.
Graph Prototypical Networks for Few-shot Learning on Attributed Networks
By constructing a pool of semi-supervised node classification tasks to mimic the real test environment, GPN is able to perform \textit{meta-learning} on an attributed network and derive a highly generalizable model for handling the target classification task.
Adversarial Attacks and Defenses: An Interpretation Perspective
In this paper, we review recent work on adversarial attacks and defenses, particularly from the perspective of machine learning interpretation.
Leveraging Multi-Source Weak Social Supervision for Early Detection of Fake News
Social media has greatly enabled people to participate in online activities at an unprecedented rate.
Causal Interpretability for Machine Learning -- Problems, Methods and Evaluation
In this work, models that aim to answer causal questions are referred to as causal interpretable models.
Social Science Guided Feature Engineering: A Novel Approach to Signed Link Analysis
The existence of negative links piques research interests in investigating whether properties and principles of signed networks differ from those of unsigned networks, and mandates dedicated efforts on link analysis for signed social networks.
Mining Disinformation and Fake News: Concepts, Methods, and Recent Advancements
In recent years, disinformation including fake news, has became a global phenomenon due to its explosive growth, particularly on social media.
Counterfactual Evaluation of Treatment Assignment Functions with Networked Observational Data
When such data comes with network information, the later can be potentially useful to correct hidden confounding bias.
Privacy-Aware Recommendation with Private-Attribute Protection using Adversarial Learning
The attacker seeks to infer users' private-attribute information according to their items list and recommendations.
Deep Reinforcement Learning-based Text Anonymization against Private-Attribute Inference
Experiments show the effectiveness of this approach in terms of preserving both privacy and utility.
Deep causal representation learning for unsupervised domain adaptation
Recent research on deep domain adaptation proposed to mitigate this problem by forcing the deep model to learn more transferable feature representations across domains.
Detecting Fake News with Weak Social Supervision
This is especially the case for many real-world tasks where large scale annotated examples are either too expensive to acquire or unavailable due to privacy or data access constraints.
Feature Interaction-aware Graph Neural Networks
Inspired by the immense success of deep learning, graph neural networks (GNNs) are widely used to learn powerful node representations and have demonstrated promising performance on different graph learning tasks.
Multi-task Generative Adversarial Learning on Geometrical Shape Reconstruction from EEG Brain Signals
In light of this, we propose a novel multi-task generative adversarial network to convert the individual's EEG signals evoked by geometrical shapes to the original geometry.
Learning Individual Causal Effects from Networked Observational Data
In fact, an important fact ignored by the majority of previous work is that observational data can come with network information that can be utilized to infer hidden confounders.
Deep Anomaly Detection on Attributed Networks
In particular, our proposed deep model: (1) explicitly models the topological structure and nodal attributes seamlessly for node embedding learning with the prevalent graph convolutional network (GCN); and (2) is customized to address the anomaly detection problem by virtue of deep autoencoder that leverages the learned embeddings to reconstruct the original data.
Applications of Social Media in Hydroinformatics: A Survey
Floods of research and practical applications employ social media data for a wide range of public applications, including environmental monitoring, water resource managing, disaster and emergency response. Hydroinformatics can benefit from the social media technologies with newly emerged data, techniques and analytical tools to handle large datasets, from which creative ideas and new values could be mined. This paper first proposes a 4W (What, Why, When, hoW) model and a methodological structure to better understand and represent the application of social media to hydroinformatics, then provides an overview of academic research of applying social media to hydroinformatics such as water environment, water resources, flood, drought and water Scarcity management.
A Novel Trend Symbolic Aggregate Approximation for Time Series
The experimental results on diverse time series data sets demonstrate that our proposed representation significantly outperforms the original SAX representation and an improved SAX representation for classification.
The Role of User Profile for Fake News Detection
In an attempt to understand connections between user profiles and fake news, first, we measure users' sharing behaviors on social media and group representative users who are more likely to share fake and real news; then, we perform a comparative analysis of explicit and implicit profile features between these user groups, which reveals their potential to help differentiate fake news from real news.
Hierarchical Propagation Networks for Fake News Detection: Investigation and Exploitation
In an attempt to understand the correlations between news propagation networks and fake news, first, we build a hierarchical propagation network from macro-level and micro-level of fake news and true news; second, we perform a comparative analysis of the propagation network features of linguistic, structural and temporal perspectives between fake and real news, which demonstrates the potential of utilizing these features to detect fake news; third, we show the effectiveness of these propagation network features for fake news detection.
Social and Information Networks
Signed Link Prediction with Sparse Data: The Role of Personality Information
Predicting signed links in social networks often faces the problem of signed link data sparsity, i. e., only a small percentage of signed links are given.
Graph Neural Networks for User Identity Linkage
In particular, we provide a principled approach to jointly capture local and global information in the user-user social graph and propose the framework {\m}, which jointly learning user representations for user identity linkage.
Online Newton Step Algorithm with Estimated Gradient
In particular, we extend the ONS algorithm with the trick of expected gradient and develop a novel second-order online learning algorithm, i. e., Online Newton Step with Expected Gradient (ONSEG).
A Survey of Learning Causality with Data: Problems and Methods
This work considers the question of how convenient access to copious data impacts our ability to learn causal effects and relations.
FakeNewsNet: A Data Repository with News Content, Social Context and Dynamic Information for Studying Fake News on Social Media
However, fake news detection is a non-trivial task, which requires multi-source information such as news content, social context, and dynamic information.
Social and Information Networks
Multi-Level Network Embedding with Boosted Low-Rank Matrix Approximation
As opposed to manual feature engineering which is tedious and difficult to scale, network representation learning has attracted a surge of research interests as it automates the process of feature learning on graphs.
Linked Causal Variational Autoencoder for Inferring Paired Spillover Effects
Modeling spillover effects from observational data is an important problem in economics, business, and other fields of research.
Learning Representations of Ultrahigh-dimensional Data for Random Distance-based Outlier Detection
However, existing unsupervised representation learning methods mainly focus on preserving the data regularity information and learning the representations independently of subsequent outlier detection methods, which can result in suboptimal and unstable performance of detecting irregularities (i. e., outliers).
Exploiting Tri-Relationship for Fake News Detection
Recent Social and Psychology studies show potential importance to utilize social media data: 1) Confirmation bias effect reveals that consumers prefer to believe information that confirms their existing stances; 2) Echo chamber effect suggests that people tend to follow likeminded users and form segregated communities on social media.
Social and Information Networks
Cross-Platform Emoji Interpretation: Analysis, a Solution, and Applications
We apply our solution to sentiment analysis, a task that can benefit from the emoji calibration technique we use in this work.
Fake News Detection on Social Media: A Data Mining Perspective
First, fake news is intentionally written to mislead readers to believe false information, which makes it difficult and nontrivial to detect based on news content; therefore, we need to include auxiliary information, such as user social engagements on social media, to help make a determination.
Attributed Network Embedding for Learning in a Dynamic Environment
To our best knowledge, we are the first to tackle this problem with the following two challenges: (1) the inherently correlated network and node attributes could be noisy and incomplete, it necessitates a robust consensus representation to capture their individual properties and correlations; (2) the embedding learning needs to be performed in an online fashion to adapt to the changes accordingly.
NeuroRule: A Connectionist Approach to Data Mining
One of the major reasons cited is that knowledge generated by neural networks is not explicitly represented in the form of rules suitable for verification or interpretation by humans.
Challenges of Feature Selection for Big Data Analytics
We are surrounded by huge amounts of large-scale high dimensional data.
SlangSD: Building and Using a Sentiment Dictionary of Slang Words for Short-Text Sentiment Classification
To this end, we propose to build the first sentiment dictionary of slang words to aid sentiment analysis of social media content.
PPP: Joint Pointwise and Pairwise Image Label Prediction
However, pointwise labels in image classification and tag annotation are inherently related to the pairwise labels.
Feature Selection: A Data Perspective
To facilitate and promote the research in this community, we also present an open-source feature selection repository that consists of most of the popular feature selection algorithms (\url{http://featureselection. asu. edu/}).
A Survey of Signed Network Mining in Social Media
Many real-world relations can be represented by signed networks with positive and negative links, as a result of which signed network analysis has attracted increasing attention from multiple disciplines.
Finding Eyewitness Tweets During Crises
These agencies look for tweets from within the region affected by the crisis to get the latest updates of the status of the affected region.
mTrust: Discerning Multi-Faceted Trust in a Connected World
Traditionally, research about trust assumes a single type of trust between users.
