Papers Veracity Classification

Multimodal Fact-Checking with Vision Language Models: A Probing Classifier based Solution with Embedding Strategies

This study evaluates the effectiveness of Vision Language Models (VLMs) in representing and utilizing multimodal content for fact-checking. To be more specific, we investigate whether incorporating multimodal content imp…

Contrastive Learning to Improve Retrieval for Real-world Fact Checking

Recent work on fact-checking addresses a realistic setting where models incorporate evidence retrieved from the web to decide the veracity of claims. A bottleneck in this pipeline is in retrieving relevant evidence: trad…

MFC-Bench: Benchmarking Multimodal Fact-Checking with Large Vision-Language Models

Large vision-language models (LVLMs) have significantly improved multimodal reasoning tasks, such as visual question answering and image captioning. These models embed multimodal facts within their parameters, rather tha…

JustiLM: Few-shot Justification Generation for Explainable Fact-Checking of Real-world Claims

Justification is an explanation that supports the veracity assigned to a claim in fact-checking. However, the task of justification generation is previously oversimplified as summarization of fact-check article authored …

EX-FEVER: A Dataset for Multi-hop Explainable Fact Verification

Fact verification aims to automatically probe the veracity of a claim based on several pieces of evidence. Existing works are always engaging in accuracy improvement, let alone explainability, a critical capability of fa…

Weakly Supervised Veracity Classification with LLM-Predicted Credibility Signals

Credibility signals represent a wide range of heuristics typically used by journalists and fact-checkers to assess the veracity of online content. Automating the extraction of credibility signals presents significant cha…

Benchmarking the Generation of Fact Checking Explanations

Fighting misinformation is a challenging, yet crucial, task. Despite the growing number of experts being involved in manual fact-checking, this activity is time-consuming and cannot keep up with the ever-increasing amoun…

Less is More: Facial Landmarks can Recognize a Spontaneous Smile

Smile veracity classification is a task of interpreting social interactions. Broadly, it distinguishes between spontaneous and posed smiles. Previous approaches used hand-engineered features from facial landmarks or cons…

Aggregating Pairwise Semantic Differences for Few-Shot Claim Veracity Classification

As part of an automated fact-checking pipeline, the claim veracity classification task consists in determining if a claim is supported by an associated piece of evidence. The complexity of gathering labelled claim-eviden…

Aggregating Pairwise Semantic Differences for Few-Shot Claim Veracity Classification

As part of an automated fact-checking pipeline, the claim veracity classification task consists in determining if a claim is supported by an associated piece of evidence. The complexity of gathering labelled claim-eviden…

Calling to CNN-LSTM for Rumor Detection: A Deep Multi-channel Model for Message Veracity Classification in Microblogs

Reputed by their low-cost, easy-access, real-time and valuable information, social media also wildly spread unverified or fake news. Rumors can notably cause severe damage on individuals and the society. Therefore, rumor…

Learning Disentangled Latent Topics for Twitter Rumour Veracity Classification

A Vector-Based Approach to Few-Shot Veracity Classification for Automated Fact-Checking

As progress on automated fact-checking continues to be called, veracity classification has gained more attention. It is the task of predicting the veracity of a given claim by comparing it with retrieved pieces of eviden…

Dynamically Addressing Unseen Rumor via Continual Learning

Rumors are often associated with newly emerging events, thus, an ability to deal with unseen rumors is crucial for a rumor veracity classification model. Previous works address this issue by improving the model's general…

Fine-Tune Longformer for Jointly Predicting Rumor Stance and Veracity

Increased usage of social media caused the popularity of news and events which are not even verified, resulting in spread of rumors allover the web. Due to widely available social media platforms and increased usage caus…

Tree LSTMs with Convolution Units to Predict Stance and Rumor Veracity in Social Media Conversations

Learning from social-media conversations has gained significant attention recently because of its applications in areas like rumor detection. In this research, we propose a new way to represent social-media conversations…

Can Rumour Stance Alone Predict Veracity?

Prior manual studies of rumours suggested that crowd stance can give insights into the actual rumour veracity. Even though numerous studies of automatic veracity classification of social media rumours have been carried o…

Early Detection of Social Media Hoaxes at Scale

The unmoderated nature of social media enables the diffusion of hoaxes, which in turn jeopardises the credibility of information gathered from social media platforms. Existing research on automated detection of hoaxes ha…

Simple Open Stance Classification for Rumour Analysis

Stance classification determines the attitude, or stance, in a (typically short) text. The task has powerful applications, such as the detection of fake news or the automatic extraction of attitudes toward entities or ev…

Detection and Resolution of Rumours in Social Media: A Survey

Despite the increasing use of social media platforms for information and news gathering, its unmoderated nature often leads to the emergence and spread of rumours, i.e. pieces of information that are unverified at the ti…