=Paper=
{{Paper
|id=Vol-3199/paper10
|storemode=property
|title=UofA-Truth at Factify 2022 : A Simple Approach to Multi-Modal Fact-Checking
|pdfUrl=https://ceur-ws.org/Vol-3199/paper10.pdf
|volume=Vol-3199
|authors=Abhishek Dhankar,Osmar Zaiane,Francois Bolduc
|dblpUrl=https://dblp.org/rec/conf/aaai/DhankarZB22
}}
==UofA-Truth at Factify 2022 : A Simple Approach to Multi-Modal Fact-Checking==
https://ceur-ws.org/Vol-3199/paper10.pdf
UofA-Truth at Factify 2022 : A Simple Approach to
Multi-Modal Fact-Checking
Abhishek Dhankar1 , Osmar R. Zaïane1 and Francois Bolduc1
1
University of Alberta, Edmonton, Alberta, T6G 2E8, Canada
Abstract
Identifying fake news is a very difficult task, especially when considering the multiple modes of conveying
information through text, image, video and/or audio. We attempted to tackle the problem of automated
misinformation/disinformation detection in multi-modal news sources (including text and images)
through our simple, yet effective, approach in the FACTIFY shared task at De-Factify@AAAI2022. Our
model produced an F1-weighted score of 74.807%, which was the fourth best out of all the submissions.
In this paper we will explain our approach to undertake the shared task.
Keywords
fake news, multi-modal, De-Factify@AAAI2022, FACTIFY shared task
1. Introduction
Humankind has dealt with misinformation since time immemorial [1]. However, never in
human history have people had access to the amount of information that they have today. The
Internet is the primary reason for easy access to this information. It has given people the ability
to access information from all over the world and from innumerable sources. However, this
deluge of information has brought with it the problem of misinformation/disinformation/fake
news. Never before have we had more efficacious means to disseminate deceptive fallacies and
falsehoods that are unfortunately believed and are wrongfully, and sometimes dangerously
impacting people.
While there are many definitions of Fake News, for this paper Fake News can be defined as a
news piece, social media post, etc., which contains claim(s) that can be refuted by information
put out by “reputable organizations”. Such organizations may include, but are not limited to,
government bodies, news outlets that score high on Media Bias/Fact Check’s Factual Reporting
scale [2] or professional fact-checking organizations which are verified signatories of the
International Fact-Checking Network (IFCN) code of conduct [3] [4]. This definition of fake news
off-loads the responsibility of determining what exactly fake news is, on to expert fact-checkers
or domain experts, and allows Artificial Intelligence (AI) to deal with the more manageable
problem of determining whether claim(s) made in a news piece is entailed, not entailed or
De-Factify: Workshop on Multimodal Fact Checking and Hate Speech Detection, co-located with AAAI 2022. 2022
Vancouver, Canada
$ dhankar@ualberta.ca (A. Dhankar); zaiane@ualberta.ca (O. R. Zaïane); fbolduc@ualberta.ca (F. Bolduc)
https://www.linkedin.com/in/abhishek-dhankar-928610140/ (A. Dhankar); https://www.cs.ualberta.ca/~zaiane/
(O. R. Zaïane); http://www.bolduclab.com/page9.html (F. Bolduc)
© 2021 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR
Workshop
Proceedings
http://ceur-ws.org
ISSN 1613-0073 CEUR Workshop Proceedings (CEUR-WS.org)
refuted by a corresponding news piece from a reputable source.
Fake news can cause real world harm as is being seen during the COVID-19 pandemic:
misinformation has led to vaccine hesitancy, which is directly tied to increased chances of
mortality due to COVID-19 [5]. Fact-checking or determining whether a news piece contains
fake claims is the first step in countering such fake news. Furthermore, it is not only important
to detect and counteract fake news, but to do so in a timely manner. Given the large amount of
information generated on social media sites every-day and the time constraints that online fact-
checking operates under, it is imperative that automated methods of misinformation detection
are developed to aid in the manual fact-checking of fake news.
In general, the information generated and distributed on the Internet is multi-modal, i.e., con-
sisting of text, images, audio-visual, etc. Often times information is conveyed via a combination
of two or more modes, for instance, memes, pieces of information rapidly spread among users,
are often a combination of text and image/short video, where text is overlaid on the image or
short video (also called a gif). Thus, an automated method should be able to take advantage of
all the modes of information available to fact-check a claim.
The shared task FACTIFY, in conjunction with the AAAI conference, attempts to aid in the
development of automated multi-modal fact-checking by introducing a dataset that consists of
multi-modal claims and corresponding supplementary information or documents, using which
said claims need to be fact-checked [6] [7]. Each multi-modal “claim” consists of a short sentence
or phrase and an associated image, which may or may not have overlaid text. The corresponding
supplementary information or “document” consists of a sentence or sentences from a reputable
source with an accompanying image. The task is to determine whether the claim text and claim
image are individually entailed, not entailed, or refuted by the corresponding document text
and image pair. Depending on that, and defined by the shared task organizers, there are five
possible labels that each claim text and image pair can have:
• Support_Multimodal: Both claim text and image are entailed
• Support_text: Text is entailed, but the image is not
• Insufficient_Multimodal: Claim text is not entailed, but claim image is
• Insufficient_Text: Neither claim text nor claim image is entailed
• Refute: Both claim text and image are refuted
Our team “UofA-Truth” participated in the shared task and secured the 4𝑡ℎ position with a
weighted F1-score of 74.807%, just ≈ 2 F1 points behind the top submission. In this paper, we
shall describe our simple yet effective automated fact-checking model.
2. Related Works
The dataset used to train and test our model was released under the shared task FACTIFY, which
is a part of the workshop De-Factify at the AAAI 2022 conference [6]. The dataset consists of a
total of 50, 000 claim and document pairs, which are divided into train, validation and test sets
of sizes 35, 000(70%), 7, 500(15%) and 7, 500(15%) respectively.
The entailment aspect of the shared task is similar to “Stance Detection”, which can be defined
as the classification of the stance of the producer of a news piece concerning an unverified
claim [8]. In the context of the shared task, the unverified claim is the claim text and image
pair, and the news piece is the document text and image pair.
Stance Detection is an important part of Fake News detection and was notably used in the
Fake News Challenge - 1 (FNC-1) [9]. This challenge was similar to the FACTIFY shared task,
except FNC-1 only dealt with text entailment or stance detection, unlike FACTIFY which deals
with multi-modal entailment. FNC-1 introduced a dataset that consisted of a headline and a
body of text, which may be from the same article or different articles. Depending on the stance
of the body of text concerning the headline, the text-headline pairs were to be classified into
any of the following classes:
• Agrees: The body of text agrees with the claim(s) made in the headline
• Disagrees: The body of text disagrees with the claim(s) made in the headline
• Discusses: The body of text and headline are referring to the same subject, but the body
does not take any stance or position on the claim(s) made in the headline
• Unrelated: The body of text is not related to the claim(s) being made in the headline
FACTIFY’s not-entail class can be considered similar to a combination of Unrelated and
Discusses classes of FNC-1, while entails and refutes classes can be considered similar to
FNC-1’s Agrees and Disagrees classes respectively.
This similarity between the two tasks led us to draw inspiration from the UCL Machine
Reading team’s submission to the FNC-1’s challenge, which performed 3𝑟𝑑 best among the 50
submissions to the challenge [10]. In their submission the UCL team, Riedel et al., describe their
approach as a “simple but tough-to-beat baseline” for stance detection. As explained above,
there are two inputs for this task - a headline and a body of text. Riedel et al. calculated the
Term Frequency (TF) vectors and Term Frequency - Inverse Document Frequency (TF-IDF) for
both the headline and the body of text-based on the 5, 000 most frequent words. The TF vectors
of the two inputs are concatenated with the result of the cosine similarity between the TF-IDF
vectors of the headline and body, as shown in Figure 1. The resultant vector of length 10, 001 is
then fed as input into a shallow Multi Layer Perceptron (MLP) network, which has a softmax
output of length four, one for each class in the FNC-1 task.
Given the similarity of the tasks being solved in FNC-1 and FACTIFY, we adopted the manner
of the concatenation of the cosine similarity and vector representations of the header and
body as explained in [10]. Instead of using TF for vector representations and TF-IDF for cosine
similarity calculation, we used Sentence-BERT in lieu of both TF and TF-IDF vectors to determine
entailment between the claim text and document text [11]. Sentence BERT is a transformer
based model which has been specifically trained to represent sentences and paragraphs and
therefore may be better for sentence representation than other methods which usually compute
the average across vector representations of individual words to obtain sentence or paragraph
representations. To determine entailment between claim and document images, we used a
pre-trained instantiation of the Xception architecture [12] available in Keras [13], which had
been trained on JFT-300M dataset [14].
The FNC-1 challenge had two other submissions which performed better than [10], however,
we concluded that those were more complicated architectures and might hamper the scalability
and time complexity of our model. For instance, Pan et al., who submitted the winning
Figure 1: Concatenated Vector Representation. Adapted from “A simple but tough-to-beat baseline for
the Fake News Challenge stance detection task”, by Riedel et al.
model [15], had an ensemble model which consisted of a deep learning model and a tree based
ensemble model as implemented in Xgboost [16]. The outputs of the two models were weighted
equally to produce the final predictions. Hanselowski et al. also implemented an ensemble model
which consisted of five Neural Network models. The final prediction was made through majority
voting. Despite the increased complexity, Team UCL Machine Reading’s model performance
was within ≈ 1 point of the top two submissions.
3. Methodology
The FACTIFY shared task’s classes (Support_Multimodal, Support_text, Insufficient_Multimodal,
Insufficient_Text, Refute) are composed of a combination of text and image entailment classes.
For instance, if text entailment, non-entailment, and refutation are represented by 𝒯 _0, 𝒯 _1,
𝒯 _2, and image entailment, non-entailment and refutation are represented by ℐ_0, ℐ_1, ℐ_2
respectively; then the shared task’s classes can be reformulated as a combination of text and
image entailment labels as shown in Table 1. It is important to note here that all combinations
of text entailment labels and image entailment labels are not present in Table 1. For instance,
combinations such as 𝒯 _0 & ℐ_2 do not exist. The lacking combinations are treated when
consolidating the labels after classification. This is explained in Section 3.4.
It can be clearly seen that the shared task can now be broken down into two sub-tasks;
namely, text entailment and image entailment, where text entailment consists of classes 𝒯 _0,
𝒯 _1 and 𝒯 _2, and image entailment consists of classes ℐ_0, ℐ_1, ℐ_2. These new classes
are the combination of the original class labels as shown in Table 2 and Table 3 for the text
entailment and image entailment tasks respectively. Once the dataset is rearranged according
to the sub-task labels, we end up with one dataset for each sub-task.
We now define Text Entailment as a task of predicting the document text’s stance towards the
Table 1
FACTIFY Task Labels & Corresponding Text and Image Entailment Labels
FACTIFY Task Label Text Entailment Label Image Entailment Label
Support_Multimodal 𝒯 _0 ℐ_0
Support_text 𝒯 _0 ℐ_1
Insufficient_Multimodal 𝒯 _1 ℐ_0
Insufficient_Text 𝒯 _1 ℐ_1
Refute 𝒯 _2 ℐ_2
Table 2
Text Entailment Task Labels in Terms of Original FACTIFY Task Labels
Text Entailment Label FACTIFY Labels
𝒯 _0 Support_Multimodal & Support_text
𝒯 _1 Insufficient_Multimodal & Insufficient_Text
𝒯 _2 Refute
Table 3
Image Entailment Task Labels in Terms of Original FACTIFY Task Labels
Image Entailment Label FACTIFY Labels
ℐ_0 Support_Multimodal & Insufficient_Multimodal
ℐ_1 Support_text & Insufficient_Text
ℐ_2 Refute
claim text, and Image Entailment as a task of predicting the document image’s stance towards
the claim image.
3.1. Preprocessing
Image preprocessing is done by resizing all the images to (256, 256, 3) size with bilinear interpo-
lation as implemented in image_dataset_from_directory in Keras [13]. Thereafter, all the pixel
values are scaled to a range of 0 to 1.
Text preprocessing involves removing URLs from all claim and document texts with the help
of the Preprocessor library [18].
3.2. Vector Representations
The preprocessed inputs (text and images) need to be converted into vector representations so
that they can be presented as input for a classifier.
The preprocessed images are converted into vectors of size 2048 each, by using the pre-trained
Xception model in Keras [13]. This can be achieved by setting include_top attribute to False
and pooling attribute to ‘avg’. Setting include_top to False removes the fully connected layer at
the end of the model and exposes the output of the second to the last layer. Setting pooling to
Figure 2: Image Entailment Classifier Architecture
‘avg’ ensures that a global pooling average is applied to the 3D output of the second last layer
of Xception, to convert it into a 1D output. Since the Xception model has been trained on a
massive dataset for a general image classification task, it can be reasonably assumed that the
output of the second to the last layer captures information that may be useful for downstream
tasks such as image entailment.
The preprocessed texts are converted into vectors of length 384 each by using the pre-trained
Sentence-BERT model [11].
The cosine similarity of the vector representations of claim and corresponding document
images is calculated and concatenated in the manner shown in Figure 2. This creates a concate-
nated representation for each claim and corresponding document image of size 4097. Similarly,
the concatenated representation of claim and corresponding document text of size 769 is created
through the same procedure of cosine similarity calculation and subsequent concatenation as
shown in Figure 3.
3.3. Classifiers
The vector representations are now used for training the classifiers for the image and text
entailment tasks. Different classifiers are used for the image and text entailment tasks.
As shown in Figure 2 the image entailment classifier consists of a single fully connected
hidden MLP layer of 5000 units, ReLU activation with a dropout probability of 0.5. The output
of this layer then feeds into a fully connected output layer of 3 units, one for each class label
(entailment, non-entailment and refute), and a sigmoid activation function.
On the other hand, as shown in Figure 3 the text entailment classifier consists of two fully
connected layers of 450 units each, ReLU activation functions, 𝑙2 activity regularizers, and a
Figure 3: Text Entailment Classifier Architecture
dropout probability of 0.55 for the first layer and 0.4 for the second layer. The output of the two
hidden layers then feeds into the fully connected output layer 3 units with sigmoid activation.
The Cross-Entropy loss is calculated after performing the softmax operation on the outputs
of both the classifiers.
3.4. Label Consolidation
The output of the image classifier classifies every pair of claims and document image into one
of the three labels ℐ_0, ℐ_1 and ℐ_2. Similarly, for claim and document text pairs.
The pairs of image and text entailment labels belonging to the same data-point are com-
bined and then converted into the original FACTIFY task labels (namely, Support_Multimodal,
Support_text, Insufficient_Multimodal, Insufficient_Text, Refute) according to Table 1.
However, it is possible that the combination procedure may produce pairs of entailment
labels that do not have any corresponding FACTIFY task label. For instance, (𝒯 _0, ℐ_2), (𝒯 _1,
ℐ_2), (𝒯 _2, ℐ_0), (𝒯 _2, ℐ_1), are four such invalid pairs of labels. We thus have to change
such label pairs into valid label pairs. We do so by using a heuristic as described in Table 4(A).
If one of either claim text or claim image is entailed, i.e., 𝒯 _0 or ℐ_0, it is unlikely that the
other claim mode will be refuted by the document, hence, the latter’s label needs to be changed
to not-entailed, i.e., 𝒯 _1 or ℐ_1. If however, one of the claim text or image is refuted by the
corresponding document then it is unlikely that the other claim mode will have uncertain
entailment, hence the latter’s label should be converted to refuted as well, i.e., 𝒯 _2 or ℐ_2.
Thereafter, we can calculate the final weighted F1 accuracy on the Test set.
Table 4
Heuristics for invalid label conversion
Invalid Label Pair Valid Label Pair Invalid Label Pair Valid Label Pair
(𝒯 _0, ℐ_2) (𝒯 _0, ℐ_1) (𝒯 _0, ℐ_2) (𝒯 _0, ℐ_0)
(𝒯 _1, ℐ_2) (𝒯 _2, ℐ_2) (𝒯 _1, ℐ_2) (𝒯 _1, ℐ_1)
(𝒯 _2, ℐ_0) (𝒯 _1, ℐ_0) (𝒯 _2, ℐ_0) (𝒯 _2, ℐ_2)
(𝒯 _2, ℐ_1) (𝒯 _2, ℐ_2) (𝒯 _2, ℐ_1) (𝒯 _2, ℐ_2)
A: Invalid to Valid Label Pair Conversion B: New Invalid to Valid Label Pair Conversion
4. Results & Discussion
Our team, UofA-Truth, secured the 4𝑡ℎ position on the leaderboard, with an F1-score of 74.807%
on the final evaluation. However, the confusion matrix, shown in Figure 4, reveals more
fine-grained details about our model’s performance on the test set.
The model performed worst on the Insufficient_Multimodal category. It can be clearly
seen from the matrix that a large number (304) of data points with ground truth Insuffi-
cient_Multimodal were incorrectly classified as Support_Multimodal. Since the only difference
between the two classes is text entailment, it is possible that the model maybe unable to dif-
ferentiate between text entailment and non-entailment. This may be because the claim and
document texts might have common words or might even talk about tangential or similar topics,
but do not reach the threshold of text entailment.
Furthermore, the model did not perform well on the Support_text class. A significant number
(202) of data-points belonging to Support_Text were misclassified as Support_Multimodal.
Again, given that the only difference between the two classes is in image entailment, it follows
that a model would find it hard to differentiate between the two. Similarly, for the Support_Text
and Insufficient_Text.
The model performs best on the Refute class despite the fact that the said class had the fewest
data points in the training set. Very few of its data points are misclassified as other classes, and
vice-versa. This may be because a significant number of the data points belonging to the Refute
class have been taken from fact-checking websites. This fact may set such data points apart
from other claim-document pairs. For instance, document images and corresponding claim
images of the Refute class tend to be identical because fact-checking websites almost always
provide a screenshot of the fake news/social media posts they debunk in their articles. They
may even overlay images of news pieces they fact-check with a digital stamp, indicating their
logo or whether the news piece was true or fake. They usually clearly state the gist of the fake
news they debunk, at the beginning of every article, often quoting said fake news verbatim.
Such peculiarities may make data points belonging to the Refute class easy to discern.
Heuristics mentioned in Section 3.4 can be changed to improve the weighted F1-score on
the test set. It is possible that the image entailment model merely learns to determine the
similarity between claim and document image pairs. Thus, it may be better to have a heuristic
that changes the invalid label pairs into valid label pairs by changing the image entailment label
Figure 4: Confusion Matrix for Original Heuristic
Figure 5: Confusion Matrix for Modified Heuristic
to be the same as the text entailment label. Therefore, after the competition results, we changed
the heuristic for invalid label pair to valid label pair conversion as per the new heuristics shown
in Table 4(B). These modified heuristics improve the final F1-score from 74.807% to 75.183%.
As can be seen by the confusion matrix in Figure 5, the new heuristic reduces the classification
accuracies of the Insufficient_Multimodal and Support_Text classes, for the benefit of the other
classes. Other than that, the overall dynamics remain the same as in Figure 4. It could be possible
to continue adjusting these heuristics to obtain even better results but have not experimented
further.
5. Conclusion
In this paper, we introduced a simple, yet effective method of multi-modal fake news detection.
We divided the main task into two sub-tasks; namely, text entailment and image entailment.
Thereafter, we used pre-trained Xception network and Sentence-BERT to get vector repre-
sentations of images and text respectively. We then used these vector representations for
classifications tasks of image and text entailment by adapting the approach introduced by
Riedel et al. in their submission to the FNC-1 task. Finally, we consolidated the prediction of
the two sub-tasks of image and text entailment to get the final predictions. We used the model
thus created to make predictions on the test set, and our team’s submission achieved the 4𝑡ℎ
position on the leader board with a 74.807% weighted F1-score.
Acknowledgments
This work was partially funded by a Collaborative Health Research Project grant from the
Canadian Institutes of Health Research (CIHR) and the Natural Sciences and Engineering
Research Council of Canada (NSERC). Osmar Zaiane, a Canada CIFAR AI Chair, is also funded
by the Canadian Institute for Advanced Research (CIFAR).
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