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 in uential 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, speci cally using transformer-based textual and multimodal models. We investigate state-of-the-art models for images, text (Transformer-based), and multimodal information for four di erent datasets across two languages to understand the role of images in the task of claim and conspiracy detection.
Social media platforms have become an integral part of our everyday lives, where
we use them to connect with people and consume news, entertainment, and buy
or sell products. In the last decade, social media has seen exponential growth,
with more than a couple of billion users and the increasing presence of prominent
people like politicians and celebrities (also called In uencers), organizations, and
political parties. On the one hand, this allows in uential people or organizations
to reach millions of users directly, but it also allows for fake and unveri ed
information to rise and spread faster [
As social media is inherently multimodal in nature, fact-checking
initiatives and computation methods consider not only text but also image
content [
Since we are interested in the impact of using images in a multimodal
framework, to keep our models simple, we focus on extracting only semantic or
contextual features from text and do not consider its structure or syntactic information.
To this end, we mainly consider deep transformer Bidirectional Encoder
Representations from Transformers (BERT) to extract contextual embeddings and use
them along with image embeddings. Taking inspiration from recent work by Cao
et al. [
To carry out this study3, we experiment with four Twitter datasets4 on
binary classi cation tasks, two of which are from the recent CLEF-CheckThat!
2020 [
ne-tuning experiments using ViLBERT to see if a large transformer-based multimodal model can learn meaningful representation and perform better on small-sized datasets.
The remainder of the paper is organized as follows. Section 2 brie y discusses related work on fake news detection and the sub-problems of claim and conspiracy detection. Section 3 presents details of image, text, and multimodal features as well as the
ne-tuned and applied models. Section 4 describes the experimental setup, results and summarizes our ndings. Section 5 concludes the paper with future research directions. 2
There is a wide body of work on fake news detection that goes well beyond this paper's scope. Therefore, we restrict this section to multimodal fake news, claim detection, and conspiracy detection. 2.1
The earliest claim detection works go back a decade. Rosenthal et al. [
Wikipedia discussion forums. They
classi ed them via logistic regression using the sentiment, syntactic and lexical
features like POS (Part-of-Speech) tags and n-grams, and other statistical features
over text. Since then, researchers have proposed context dependent [
For claim detection on social media in particular, recently CLEF-CheckThat!
2020 [
For multimodal fake news in general, several benchmark datasets have been
proposed in the last few years, generating interest in developing multimodal
visual and textual models. In one of the relatively early works, Jin et al. [
Object Features (Io) In order to encode objects and the overall image
content, we extract features from a pre-trained ResNet [
Place and Scene Features (Ip) In order to encode the scene information in
an image, we extract features from a pre-trained ResNet [
Places365 [
Hybrid Object and Scene Features (Ih) We also experiment with a hybrid model trained on both ImageNet and Places365 datasets that encodes object and scene information in a single model. To extract these features, we again use a ResNet -101 model and follow the same encoding process.
Image Sentiment (Is) To encode the image sentiment, we use a pre-trained
model [
Since context and semantics of the sentence is shown [
To reduce the domain gap for our Twitter datasets in English, we experiment
with two BERT models. The rst variant is called BERTweet [
For English text, with vanilla BERT-base model, we pre-process the text
by following the steps mentioned in Cheema et. al. [
ViLBERT (Vision-and-Language BERT) We use ViLBERT [
For the image branch, ViLBERT uses state-of-the-art object detection model
Mask R-CNN [
Feature Extraction In our xed embedding experiments with a SVM, we experiment with the output of pooling and last layers of image and text branches. With pooling layers, we directly concatenate (MpCoAolT ) the image and text outputs. With last layer outputs we average the image region embeddings and word embeddings to get one single embedding per modality and then concatenate them (MaCvAgT ). From pooling layers, each modality's embedding size is a 1024dimensional vector, and the last layer average of embeddings gives 1024 and 768-dimensional vectors for image and text, respectively. For ne-tuning, we follow ViLBERT 's downstream task approach, where the pooling layer outputs are either added (MpAoDolD) or multiplied (MpMooUl L) and passed to a classi er. For Arabic text, we use Google Translate to convert the text into English because all ViLBERT models are trained on English text.
ViLBERT is ne-tuned on several downstream tasks which can be relevant
for encapsulating image-text relationship for our claim detection problem.
Therefore, we experiment with four di erent pre-trained models, namely, conceptual
captions , image retrieval (Image-Ret ), grounding referring expressions (localize
an image region given a natural language reference) (RefCOCO ), and a
multitask model [
For our xed embedding experiments, we train SVM models with each type of image and text embeddings for binary classi cation of tweets as shown in Figure 2 (a). For ne-tuning textual models (Figure 2 (b)), given that we have relatively small-sized datasets, we only experiment with ne-tuning the last two and four layers of transformer models for each dataset. We concatenate the image and text features for multimodal xed embedding experiments and train an SVM model over them for classi cation.
In the case of ViLBERT (Figure 2 (c)), we again train SVM over the extracted pooled image and text outputs for classi cation. For ne-tuning, we x the individual transformer branches and experiment with ne-tuning the last two and four co-attention layers to activate the interaction between modalities. It enables us to see the e ect of only the attention mechanism that can show the bene t of an image and text in claim detection. We use a simple classi er on top of ViLBERT outputs as recommended by the authors of ViLBERT, which includes a linear layer for down projecting outputs to 128 dimensions, followed by ReLU (Recti ed Linear Unit) non-linear activation function, a normalization layer and nally a binary classi cation layer. Dropout is used to avoid overtting, and the ne-tuning is performed by minimizing the cross-entropy loss. 4
In this section, we describe all the datasets and their statistics, training details and hyper-parameters, model details, experimental results, and discuss them as obtained by di erent models mentioned in Section 3. 4.1
We selected the following four publicly available Twitter datasets with
highquality annotations (which excludes [
CLEF-Ar [
LESA [
We applied 5-fold cross-validation to overcome the issue of low number of samples in each dataset. We used the ratio of around 72:10:18 for training, validation, and testing in each data split. Next, we report the experimental results for di erent model con gurations. The reported results are averaged across ve splits of each dataset. We report accuracy and weighted-F1 measure to account for label imbalance in all the datasets. 4.2
SVM hyper-parameters: we perform grid search over PCA energy (%) conservation, regularization parameter C and RBF kernel's gamma. The parameter range for PCA varies from 100% (original features) to 95% with decrements of 1. The parameter range for C and gamma vary between 1 to 1 on a log-scale with 15 steps. For experiments only on the CLEF-En dataset, we use the range between 2 to 0 for C and gamma, as the number of samples are very low and needs aggressive regularization. We normalize the nal embedding so that l2 norm of the vector is 1.
Fine-tuning BERT and VilBERT: we use a batch size of 4 for CLEF-En and 16 for the other datasets. We train all the models for 6 epochs with a starting learning rate of 5e 5 and a linear decay. A dropout with ratio 0:2 is applied after the rst linear layer in the classi er for regularization during ne-tuning. 4.3
Table 1 and Table 2 show the unimodal and multimodal models' performance for all the four datasets based on type of features and feature combinations respectively.
Unimodal Results - In Table 1, it can be seen that all the visual features perform poorly in comparison to textual features. This is expected as visual information on its own cannot indicate whether a social media post makes a claim unless it has text or it's a video. Among the four types of visual models, Object (Io) and Hybrid (Ih) features are slightly better, probably because the place or scene information (lowest F1 for all datasets) on its own is not a useful indicator in images for claim detection. With textual features, BERT models that are further pre-trained on tweets (TBT ; TBy T ) and COVID -related data (TCT ; TCy T ) perform better in comparison to vanilla BERT (TBCBlean; T CBlBeany) in at-least three datasets. It suggests that the tweets' structure and the domain gap are better captured and reduced respectively in Twitter corpus pre-trained models. Further, normalizing (T Clean) the tweet text delivers better performance
BB than using the raw text (TBRBaw). In SVM training, we observed the sum of the last four layers of BERT to compute the embeddings performs better than the other pooling combinations. It indicates that downstream tasks can bene t from the diverse information in di erent layers of BERT. Similarly, ne-tuning the last four layers instead of two (marked with2) gives better performance across all the datasets with BERT-base (TBCBleany), COVID-Twitter-BERT (TCT y) and AraBERT (TABy).
Multimodal Results - In Table 2, we can see the e ect of combining visual features with textual features by using a simple concatenation in SVM and also with multimodal co-attention transformer ViLBERT. Although we do not see any bene t of using the image sentiment embeddings (Ise) in unimodal models, here instead, we use the image sentiment predictions (Isp) that perform better or equivalent in comparison to other visual features. For instance, in case of CLEF-Ar, sentiment predictions Isp with AraBERT (TABy) gives the best xed embedding performance. Similarly, combining hybrid features (Ih) with BERTbase (TBCBleany) and object features with COVID-Twitter-BERT (TCT y) in case of LESA and MediaEval improves the metrics by 1% over textual SVM models.
With ViLBERT, it is interesting to see that with xed visual and textual branches, it can capture some information from image and text with co-attention to boost performance in case of LESA and MediaEval. It is worth mentioning that the best unimodal textual models for English and Arabic are pre-trained models further trained on Twitter and language-speci c data corpus. In the case of ViLBERT, there is a wider domain gap, and for Arabic, the translation process loses quite a bit of information that results in a drop in performance. Di erent pooling operations applied for pre-trained ViLBERT models show more di erence in xed-embedding SVM experiments where the average pooling (MaCvAgT ) yields a considerable performance, which we also observed in unimodal SVM experiments. We observed that pre-training tasks (best two reported in Table 2) also matter, where image retrieval (Image-Ret) and language reference grounding (RefCOCO) features perform much better for all the datasets. It is explainable since both tasks require capturing complex relationships and linking text to speci c image regions in the image, enabling them to perform better for our tasks. We can summarize the ndings of our experiments as follows: 1) Domainspeci c languages models should be preferred for downstream tasks such as claim detection or fake news, where underlying meaning and context of certain words (like COVID) is essential, 2) Multimodality certainly helps as seen with multimodal transformer models, where activating interaction through co-attention layers between xed unimodal embeddings improves the performance in two datasets, 3) To further understand underlying multimodal dynamics it might be better to explicitly model multimodal relationships, for instance, importance of image or correlation between image-text in addition to claim detection, 4) Certain pre-training tasks in ViLBERT are better suited for downstream tasks and need further introspection on larger datasets, and lastly, 5) Visual models need to be better adapted to social media images, for instance, the models used here are not su cient for
diagrams or images with large text, which constitute around 30-40% of LESA and MediaEval datasets.
In this paper, we have investigated the role of images and tweet text for two problems related to fake news, claim, and conspiracy detection. For this purpose, we combined several state-of-the-art CNN features for images with BERT features for text. We observed the performance improvement over unimodal models in two out of four Twitter datasets over two languages. We also experimented with the recently proposed multimodal co-attention transformer ViLBERT and observed a promising performance using both image and text even with relatively small-sized datasets. In future work, we will look into other ways to include external knowledge in domain-independent claim detection models without relying on di erent domain-speci c language models. Second, we plan to investigate multimodal transformers in more detail and analyze if the performance does scale with more data in similar tasks. Finally, to address the limitation of visual models, we will consider models that can deal with text and graphs in images and extract suitable features.
This work was funded by European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement no 812997.