Combating Online Hostile Posts in Regional Languages during Emergency Situation (Communications in Computer and Information Science)

✍ Scribed by Tanmoy Chakraborty (editor), Kai Shu (editor), H. Russell Bernard (editor), Huan Liu (editor), Md Shad Akhtar (editor)

• Publisher: Springer
• Year: 2021
• Tongue: English
• Leaves: 268
• Category: Library

✦ Synopsis

This book constitutes selected and revised papers from the First International Workshop on Combating On​line Ho​st​ile Posts in ​Regional Languages dur​ing Emerge​ncy Si​tuation, CONSTRAINT 2021, Collocated with AAAI 2021, held as virtual event, in February 2021.
The 14 full papers and 9 short papers presented were thoroughly reviewed and selected from 62 qualified submissions. The papers present interdisciplinary approaches on multilingual social media analytics and non-conventional ways of combating online hostile posts.

✦ Table of Contents

Preface
Organization
Contents
Identifying Offensive Content in Social Media Posts
1 Introduction
2 Related Work
3 Dataset
4 Methodology
4.1 Feature Extraction and Machine Learning
4.2 Sentence Embedding and Machine Learning
4.3 Fine-Tuning BERT and DistilBERT
5 Results and Evaluation
5.1 Hyperparameter Tuning
6 Conclusion
References
Identification and Classification of Textual Aggression in Social Media: Resource Creation and Evaluation
1 Introduction
2 Related Work
3 Task Definition
3.1 Level A: Aggressive Text Identification
3.2 Level B: Classification of Aggressive Text
4 Aggressive Text Corpus
4.1 Corpora Development
4.2 Corpora Analysis
5 Methodology
6 Experiments and Result Analysis
6.1 Error Analysis
7 Conclusion
References
Fighting an Infodemic: COVID-19 Fake News Dataset
1 Introduction
2 Related Work
3 Dataset Development
3.1 Collection and Annotation
3.2 Fake News
3.3 Real News
3.4 Dataset Statistics
4 Baselines and Results
5 Conclusion and Future Work
References
Revealing the Blackmarket Retweet Game: A Hybrid Approach
1 Introduction
2 Dataset
3 A Hybrid Detection Framework
3.1 Indicators
4 Experiments
4.1 Classification
4.2 Quantitative Analysis
4.3 Graph-Based Analysis
5 Conclusion and Future Work
References
Overview of CONSTRAINT 2021 Shared Tasks: Detecting English COVID-19 Fake News and Hindi Hostile Posts
1 Introduction
2 Related Work
3 COVID-19 Fake News Detection in English
4 Hostile Post Detection in Hindi
5 Participation and Top Performing Systems
5.1 Winning Systems
5.2 Interesting Systems
6 Results
7 Conclusion and Future Work
References
LaDiff ULMFiT: A Layer Differentiated Training Approach for ULMFiT
1 Introduction
2 Overview
2.1 Task Description and Dataset
2.2 Preprocesing
3 Model Description
3.1 Layer Differentiated ULMFiT Training
3.2 Customized RoBERTa
3.3 Random Forest Classifiers and Logistic Regression
4 Results
5 Conclusions
References
Extracting Latent Information from Datasets in CONSTRAINT 2021 Shared Task
1 Introduction
2 Related Work
3 Method
3.1 English Fake News Detection Task
3.2 Hindi Hostile Post Detection Task
4 Experiment
4.1 Dataset
4.2 Baseline Model
4.3 Optimization Approach
5 Conclusion
References
Fake News and Hostile Posts Detection Using an Ensemble Learning Model
1 Introduction
2 Related Work
3 Datasets
4 Methodology
4.1 BERT
4.2 Ensemble
5 Experiment
6 Results
7 Conclusion
References
Transformer-Based Language Model Fine-Tuning Methods for COVID-19 Fake News Detection
1 Introduction
2 Related Work
2.1 Text Classification Task with Adversarial Training Methods
2.2 Model Fusion Approaches for Text Classification
2.3 Fake News Detection
3 Methodology
3.1 Problem Definition
3.2 Our Proposed Network
4 Experimental Results
4.1 Experimental Setting
4.2 Performance in Fake News Detection in English
4.3 Ablation Studies for Ro-CT-BERT
5 Conclusions
References
Tackling the Infodemic: Analysis Using Transformer Based Models
1 Introduction
2 Related Work
3 Dataset Analysis
4 Our Work
4.1 Dataset Collection
4.2 Dataset Cleaning
5 Training
5.1 Hardware Specifications
5.2 Model Descriptions
6 Results
6.1 Performance Scores
6.2 Result Analysis
7 Future Work
References
Exploring Text-Transformers in AAAI 2021 Shared Task: COVID-19 Fake News Detection in English
1 Introduction
2 Dataset
2.1 Data Source
2.2 Exploratory Data Analysis
3 Methodology
3.1 Text-RNN
3.2 Text-Transformers
4 Experiments
4.1 Experimental Settings
4.2 Training Strategy
4.3 Results
4.4 Analysis
5 Related Work
5.1 Pre-trained Language Models
5.2 K-fold Cross-validation
5.3 Fake News Detection and Categorization
6 Conclusion
References
g2tmn at Constraint@AAAI2021: Exploiting CT-BERT and Ensembling Learning for COVID-19 Fake News Detection
1 Introduction
2 Related Work
3 Task Definition
4 Dataset
5 Our Approach
5.1 Data Preprocessing
5.2 Models
5.3 Additional Data
5.4 Experimental Settings
6 Results and Discussion
6.1 Comparison of Models for Fake News Detection
6.2 Final Submissions
6.3 Error Analysis
7 Conclusion
References
Model Generalization on COVID-19 Fake News Detection
1 Introduction
2 Dataset
3 Methodology
3.1 Task and Objective
3.2 Approach 1: Fine-Tuning Pre-trained Transformer Based Language Models with Robust Loss Functions
3.3 Approach 2: Data Noise Cleansing Based on Training Instance Influence
4 Experiment 1: Fine-Tuning LMs with Robust Loss Functions
4.1 Experiment Set-Up
4.2 Experimental Results
5 Experiment 2: Data Cleansing with Influence Calculation
5.1 Experiment Set-Up
5.2 Experiment Result
6 Discussion
6.1 Data Distribution Between Different FakeNews-19 and Tweets-19 Test Sets
6.2 How Did Smaller Data Help for Generalization Ability of the Model?
7 Related Works
8 Conclusion
References
ECOL: Early Detection of COVID Lies Using Content, Prior Knowledge and Source Information
1 Introduction
2 Related Work
2.1 Textual Content Based Fake News Detection on Social Media
2.2 Extrinsic Features for Determining Truthfulness of Claims
3 ECOL Approach
3.1 Content (C)
3.2 Prior Knowledge (PK)
3.3 Source (S)
4 Experiments
4.1 Dataset
4.2 Baselines
4.3 Models
4.4 Implementation
5 Results and Discussion
6 Conclusion
References
Evaluating Deep Learning Approaches for Covid19 Fake News Detection
1 Introduction
2 Related Works
3 Architecture Details
3.1 CNN
3.2 LSTM
3.3 Bi-LSTM + Attention
3.4 HAN
3.5 Transformers
4 Experimental Setup
4.1 Dataset Details
4.2 Preprocessing of the Dataset
4.3 Training Details
5 Results and Discussion
6 Conclusion
References
A Heuristic-Driven Ensemble Framework for COVID-19 Fake News Detection
1 Introduction
2 Related Work
3 Dataset Description
4 Methodology
4.1 Text Preprocessing
4.2 Tokenization
4.3 Backbone Model Architectures
4.4 Ensemble
4.5 Heuristic Post-processing
5 Experiments and Results
5.1 System Description
5.2 Performance of Individual Models
5.3 Performance of Ensemble Models
5.4 Performance of Our Final Approach
5.5 Ablation Study
6 Conclusion
References
Identification of COVID-19 Related Fake News via Neural Stacking
1 Introduction
2 Related Work
3 Data Description
4 Proposed Method
4.1 Hand Crafted Features
4.2 Latent Semantic Analysis
4.3 Contextual Features
4.4 tax2vec Features
5 Meta Models
5.1 Neural Stacking
5.2 Linear Stacking
6 Experiments and Results
6.1 Train-Development-Test (TDT) Split
6.2 CV Split
6.3 Evaluating Word Features
6.4 Results
7 Conclusion and Further Work
References
Fake News Detection System Using XLNet Model with Topic Distributions: CONSTRAINT@AAAI2021 Shared Task
1 Introduction
2 Related Work
3 Proposed Method
4 Dataset Description
5 Experiments
5.1 Implementation
5.2 Comparison with Other Methods
5.3 Results and Discussion
6 Error Analysis
7 Conclusion
References
Coarse and Fine-Grained Hostility Detection in Hindi Posts Using Fine Tuned Multilingual Embeddings
1 Introduction
2 Related Works
3 Methodology
3.1 Coarse-Grained Classification
3.2 Fine-Grained Classification
4 Experimental Setup
5 Results and Discussion
5.1 Coarse-Grained Evaluation
5.2 Fine-Grained Evaluation
5.3 Comparison with Baseline
5.4 Additional Discussions and Analysis
6 Conclusion and Future Works
References
Hostility Detection in Hindi Leveraging Pre-trained Language Models
1 Introduction
2 Related Work
3 Methodology
3.1 Single Model Multi-label Classification
3.2 Multi-task Classification
3.3 Binary Classification
3.4 Auxiliary Task Based Binary Sub-classification
4 Experiment
4.1 Dataset Description
4.2 Pre-processing
4.3 Experimental Setup
5 Results
6 Error Analysis
7 Conclusion and Future Work
References
Stacked Embeddings and Multiple Fine-Tuned XLM-RoBERTa Models for Enhanced Hostility Identification
1 Introduction
2 Related Work
3 Task and Dataset Description
4 System Description
4.1 Preprocessing
4.2 Models
4.3 Data Augmentation
5 Results and Discussion
6 Analysis
7 Error Analysis
8 Conclusion and Future Work
References
Task Adaptive Pretraining of Transformers for Hostility Detection
1 Introduction
2 Dataset
3 Approach
3.1 Preprocessing and Feature Extraction
3.2 Architecture
3.3 Task Adaptive Pretraining
4 Results
5 Experimental Details
6 Conclusion
References
Divide and Conquer: An Ensemble Approach for Hostile Post Detection in Hindi
1 Introduction
2 Related Work
3 Proposed Methodology
3.1 Model
4 Implementation
4.1 Dataset
4.2 Experiments
4.3 Binary Relevance Considerations
5 Result and Analysis
6 Insights
7 Error Analysis
8 Outcome
9 Future Work
References
Author Index