# On the Role of Images for Analyzing Claims in Social Media

arXiv: https://arxiv.org/abs/2103.09602 · HTML: https://mlsanity.com/paper/2103.09602/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 49 references that are in the map.
- First posted on arXiv 17 Mar 2021.

## Builds on (most cited first, 21 of 21)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [ImageNet Large Scale Visual Recognition Challenge](https://mlsanity.com/paper/1409.0575/) (2014)
- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [ViLBERT: Pretraining Task-Agnostic Visiolinguistic Representations for Vision-and-Language Tasks](https://mlsanity.com/paper/1908.02265/) (2019)
- [AraBERT: Transformer-based Model for Arabic Language Understanding](https://mlsanity.com/paper/2003.00104/) (2020)
- [BERTweet: A pre-trained language model for English Tweets](https://mlsanity.com/paper/2005.10200/) (2020)
- [12-in-1: Multi-Task Vision and Language Representation Learning](https://mlsanity.com/paper/1912.02315/) (2019)
- [COVID-Twitter-BERT: A Natural Language Processing Model to Analyse COVID-19 Content on Twitter](https://mlsanity.com/paper/2005.07503/) (2020)
- [Fact-Checking Meets Fauxtography: Verifying Claims About Images](https://mlsanity.com/paper/1908.11722/) (2019)
- [Overview of CheckThat! 2020: Automatic Identification and Verification of Claims in Social Media](https://mlsanity.com/paper/2007.07997/) (2020)
- [1.5 billion words Arabic Corpus](https://mlsanity.com/paper/1611.04033/) (2016)
- [What is the Essence of a Claim? Cross-Domain Claim Identification](https://mlsanity.com/paper/1704.07203/) (2017)
- [IMHO Fine-Tuning Improves Claim Detection](https://mlsanity.com/paper/1905.07000/) (2019)
- [LESA: Linguistic Encapsulation and Semantic Amalgamation Based Generalised Claim Detection from Online Content](https://mlsanity.com/paper/2101.11891/) (2021)
- [Accenture at CheckThat! 2020: If you say so: Post-hoc fact-checking of claims using transformer-based models](https://mlsanity.com/paper/2009.02431/) (2020)
- [Team Alex at CLEF CheckThat! 2020: Identifying Check-Worthy Tweets With Transformer Models](https://mlsanity.com/paper/2009.02931/) (2020)
- [Check_square at CheckThat! 2020: Claim Detection in Social Media via Fusion of Transformer and Syntactic Features](https://mlsanity.com/paper/2007.10534/) (2020)
- [TIB's Visual Analytics Group at MediaEval '20: Detecting Fake News on Corona Virus and 5G Conspiracy](https://mlsanity.com/paper/2101.03529/) (2021)

## Built on by (most cited first, 6 of 6)

- [DESYR: Definition and Syntactic Representation Based Claim Detection on the Web](https://mlsanity.com/paper/2108.08759/) (2021)
- [UPV at CheckThat! 2021: Mitigating Cultural Differences for Identifying Multilingual Check-worthy Claims](https://mlsanity.com/paper/2109.09232/) (2021)
- [MM-Claims: A Dataset for Multimodal Claim Detection in Social Media](https://mlsanity.com/paper/2205.01989/) (2022)
- [Multimodal Dual Emotion with Fusion of Visual Sentiment for Rumor Detection](https://mlsanity.com/paper/2204.11515/) (2022)
- [ViClaim: A Multilingual Multilabel Dataset for Automatic Claim Detection in Videos](https://mlsanity.com/paper/2504.12882/) (2025)
- [IDRL: An Individual-Aware Multimodal Depression-Related Representation Learning Framework for Depression Diagnosis](https://mlsanity.com/paper/2603.11644/) (2026)

## Abstract (arXiv)

Fake news is a severe problem in social media. In this paper, we present an empirical study on visual, textual, and multimodal models for the tasks of claim, claim check-worthiness, and conspiracy detection, all of which are related to fake news detection. Recent work suggests that images are more influential than text and often appear alongside fake text. To this end, several multimodal models have been proposed in recent years that use images along with text to detect fake news on social media sites like Twitter. However, the role of images is not well understood for claim detection, specifically using transformer-based textual and multimodal models. We investigate state-of-the-art models for images, text (Transformer-based), and multimodal information for four different datasets across two languages to understand the role of images in the task of claim and conspiracy detection.