# Mining the Discussion of Monkeypox Misinformation on Twitter Using RoBERTa

Or Elroy, Dmitry Erokhin, Nadejda Komendantova, Abraham Yosipof

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Or Elroy, Dmitry Erokhin, Nadejda Komendantova, Abraham Yosipof. Mining the Discussion of Monkeypox Misinformation on Twitter Using RoBERTa. 19th IFIP International Conference on Artificial Intelligence Applications and Innovations (AIAI), Jun 2023, León, Spain. pp.429-438, 10.1007/978-3-031-34111-3_36. hal-05449805

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# Mining the Discussion of Monkeypox Misinformation on Twitter Using RoBERTa

Or Elroy<fnref n="1" /> ID, Dmitry Erokhin<fnref n="2" /> ID, Nadejda Komendantova<fnref n="2" /> ID,
and Abraham Yosipof<fnref n="1" />(✉) ID

**Abstract.** The monkeypox outbreak in 2022 raised uncertainty leading to misinformation and conspiracy narratives in social media. The belief in misinformation leads to poor judgment, decision making, and even to unnecessary loss of life. The ability of misinformation to spread through social media may worsen the harms of different emergencies, and fighting it is therefore critical.

In this work, we analyzed the discussion of misinformation related to monkeypox on Twitter by training different classifiers that differentiate between tweets that spread and tweets that counter misinformation.

We collected over 1.4M tweets related to the discussion of monkeypox on Twitter from over 500K users and calculated word and sentence embeddings using Natural Language Processing (NLP) methods. We trained multiple machine learning classification models and fine-tuned a Robustly Optimized BERT Pre-training Approach (RoBERTa) model on a set of 3K hand-labeled tweets. We found that the fine-tuned RoBERTa model provided superior results and used it to classify the complete dataset into three categories, namely misinformation, counter misinformation and neutral.

We analyzed the behavioral patterns and domains that were used in misinformation and counter misinformation tweets. The findings provide insights into the scale of misinformation within the discussion on monkeypox and the behavior of tweets and users that spread and counter misinformation over time. In addition, the findings allow us to derive policy recommendations to address misinformation in social media.

**Keywords:** Misinformation · Monkeypox · Classification · RoBERTa · NLP

## 1 Introduction

The worldwide outbreak of the monkeypox virus in 2022 was declared a global health emergency by the World Health Organization (WHO) [1]. Social media platforms are a primary source of information at times of emergency. The outbreak of the monkeypox virus is accompanied by the spread of misinformation and conspiracy theories on social

<footnote n="1">¹ Faculty of Information Systems and Computer Science, College of Law and Business, Ramat-Gan, Israel
aviyo@clb.ac.il</footnote>

<footnote n="2">² International Institute for Applied Systems Analysis, Laxenburg, Austria</footnote>
media platforms, similarly to the COVID-19 pandemic. Twitter is one of the largest and most prominent social networks today and is therefore a fertile ground for the growth and distribution of misinformation and conspiracy theories.

Misinformation is false or inaccurate information according to the best factual evidence that is available at a given point in time, regardless of an intention to mislead or deceive [2]. Misinformation and conspiracy theories offer people with explanations and a sense of control over a situation of uncertainty [3]. A conspiracy theory is misinformation according to which a group of people are secretly trying to cause harm or achieve something, when other explanations are more probable. A conspiracy theory typically opposes the consensus among qualified professionals.

Misinformation is spread by way of rumors, conspiracy theories, fake news, and fear mongering, and can significantly affect what people perceive as a risk, and how they believe they should react to it [4]. The ability of misinformation to spread globally and easily through social media raises the risk of worsening the harms of different types of emergency situations [5–8].

The belief in conspiracy theories is motivated by a humane need to rationalize events. The lack of authoritative sources with reliable information regarding the outbreak of the virus, combined with circumstantial evidence, fosters uncertainty and thus misinformation and conspiracy narratives [9, 10]. Believing in misinformation and conspiracy theories led to the unnecessary loss of life during the COVID-19 pandemic. Hence, fighting the spread of misinformation and conspiracy theories, and countering it with reliable information, is critical.

In attempt to fight misinformation, the WHO advised for the monitoring of the public's sentiment and the timely addressing of misinformation using health information that avoids stigmatization of certain groups [11]. Twitter adopted strict policy towards misinformation during the COVID-19 pandemic, though the number of tweets and accounts removed, as reported by Twitter, is negligible.

Analyzing the discussion on monkeypox during the recent outbreak supports the effort to control and manage the spread of misinformation and conspiracy theories on social media. However, such an analysis presents several challenges, including the collection of enough relevant data, and the development of a classifier for automated detection of tweets that spread and counter misinformation or conspiracy theories. Finally, an analysis of the classified tweets is needed to gain knowledge and insights on how to better fight misinformation and conspiracy narratives.

The objective of this study is to analyze the discussion related to monkeypox on Twitter and differentiate between tweets that spread and counter misinformation. Understanding the evolvement and behavior of misinformation on social media enables a better and faster reaction to misinformation and conspiracy theories in the future. This study achieves a better understanding of the life cycle of misinformation and conspiracy theories using Natural Language Processing (NLP) and inspection of behavioral patterns.

We address these challenges and make the following contributions. We collected 1,440,475 tweets that are relevant to the discussion on monkeypox from 505,163 users
on Twitter. We manually labeled 3,218 tweets into three categories, namely misinformation, counter misinformation, and neutral. We fine-tuned a Robustly Optimized Bidirectional Encoder Representations from Transformers Pretraining Approach (RoBERTa) model for the classification task and compared its performance to several other machine learning classifiers. We analyzed the classified dataset to find and compare behavioral patterns in the data. Finally, we offer policy suggestions to reduce unwanted behavior of misinformation and conspiracy spreading, and support wanted behavior that counters misinformation and conspiratory narratives.

This study is structured as follows. The second section presents previous related works that used NLP to analyze misinformation and conspiracy theories using different methodologies and approaches. The third section presents the dataset that was collected and preprocessed. The fourth section discusses the classification process, the models that were compared and their performance. The fifth section presents the analysis results. The last section discusses the conclusion of this study and future work.

## 2 Related Work

Analyses of misinformation and conspiracy theories have compared between different conspiracy theories [4] and analyzed the behavior of different groups within the same conspiracy [3]. Erokhin et al. [4] analyzed COVID-19 conspiracy theories to identify patterns and categorize them into groups. Batzdorfer et al. [3] investigated the dynamics of tweets that discuss COVID-19 conspiracy theories, by comparing tweets from a group of users that talked about conspiracy theories and a group of users that participated in the general discussion on the virus.

A key task in analyzing tweets related to conspiracy theories or misinformation is to label and classify the tweets. A set of embeddings and labels are needed for the training of a classifier.

BERT, a Bidirectional Encoder Representations from Transformers [12], provides superior results for different NLP tasks including word embedding [13, 14]. Multiple variations of BERT with different strengths and weaknesses are available for a variety of tasks. RoBERTa was pretrained using different design decisions than BERT that improve the performance and state of the art results on different datasets [15]. Previous studies evaluated the performance of RoBERTa and found that it provides better results than BERT [16–18]. Certain models were pretrained on domain specific data to provide better embeddings [19].

Micallef et al. [20] used BERT embeddings to investigate and counter misinformation in tweets related to COVID-19 over a period of five months. Elroy and Yosipof [21] transformed BERT word embeddings to sentence embedding using Sentence-BERT [22] to train a classifier and classify a dataset of over 300K COVID-19 5G conspiracy theory related tweets.

The metadata and characteristics of tweets and their authors are useful to achieve a better understanding of behavioral patterns. Beskow and Carley [23] used the number of users that follow the author and the number of users the author follows as an indication of whether the author is a robot or not. O’Donovan et al. [24] and Gupta et al. [25] found that metadata of tweets, such as URLs, mentions, retweets and tweet length may serve
as indicators for credibility. Elroy and Yosipof [21] found that certain features such as the presence of a URL and the sentiment score of a tweet, can help identify conspirative tweets.

## 3 Dataset

We collected a dataset of tweets using Twitter's academic research API, which enables access to the full archive of Twitter. The search query includes all tweets in English that contain the term "monkeypox" between May 1, 2022 and August 24, 2022, and excludes retweets. The query is simple yet very effective in filtering tweets related to the discussion on monkeypox, and the very wide search query enables us to collect a large amount of data without much noise.

We applied a preprocessing methodology similar to [26]. For performance optimization, we limited the dataset to tweets that were 350 characters or shorter after preprocessing. After preprocessing, the final dataset consists of 1,440,475 tweets related to discussion on monkeypox that were posted by 505,163 users.

In order to train a classifier to classify the full dataset into three categories, namely misinformation, counter-misinformation, and neutral, we hand-labeled 3,218 tweets based on and according to facts provided by the World Health Organization [1]. Table 1 presents the number and examples of tweets in each category.

<table><caption>**Table 1.** Number and examples of tweets in each category.</caption><thead><tr><td>Category</td><td># of Tweets</td><td>Example Tweets</td></tr></thead><tbody><tr><td>Misinformation</td><td>1,090</td><td>"The monkeypox travel with Pfizer vaccines. It can cover big distances quickly. Stop the vaccine traveling and that will stop the "monkeypox" virus also..."</td></tr><tr><td>Counter misinformation</td><td>739</td><td>"Monkeypox is a potentially serious disease caused by infection with the monkeypox virus. Anyone can get monkeypox and it's important for everyone to take precautions to stop the spread."</td></tr><tr><td>Neutral</td><td>1,389</td><td>"#India confirms Asia's first #monkeypox death."</td></tr></tbody></table>

## 4 Classification

To train the machine learning classifiers, we calculated the word embeddings and transformed to sentence embeddings using RoBERTa and Sentence-BERT, respectively, resulting in 768 features for each tweet. We tested the performance of several machine learning classifiers. We trained a Random Forest model (RF) with 1,000 trees, a k-Nearest Neighbors (k-NN) model with 9 nearest neighbors, a Support Vector Machine (SVM) model with a linear kernel and 5,000 max iterations, a Naïve Bayes model, an XGBoost model, and a Voting Ensemble combining all five models. We evaluated the models using stratified 5-fold cross-validation.
We further fine-tuned a RoBERTa model with a classification layer. We evaluated the model with the same stratified 5-fold cross-validation, except that each 20% test was further split to 10% test and 10% validation for the fine-tuning. We fine-tuned the model for 10 epochs with a dropout of 0.2, weight decay of 0.01, learning rate of 2e-5, and batch size of 16. The model converged at epoch 5 with an average validation loss of 0.631 and an average F1 score of 0.77 on the test sets.

Table 2 presents the average F1, precision, and recall scores of each model on the test sets in all five folds. The performance results shows that the fine-tuned RoBERTa model achieves the best results with an average F1 score of 0.767, average precision of 0.774, and an average recall of 0.774. We therefore used the fine-tuned RoBERTa model to classify the unlabeled dataset.

<table><caption>**Table 2.** Comparison of performance with other models.</caption><thead><tr><th>Methods</th><th>F1</th><th>Precision</th><th>Recall</th></tr></thead><tbody><tr><td>Random Forest</td><td>0.674 ± 0.018</td><td>0.694 ± 0.020</td><td>0.678 ± 0.018</td></tr><tr><td>k-NN</td><td>0.607 ± 0.008</td><td>0.622 ± 0.009</td><td>0.611 ± 0.009</td></tr><tr><td>SVM</td><td>0.717 ± 0.014</td><td>0.718 ± 0.015</td><td>0.717 ± 0.014</td></tr><tr><td>Naïve Bayes</td><td>0.616 ± 0.009</td><td>0.631 ± 0.006</td><td>0.620 ± 0.009</td></tr><tr><td>XGBoost</td><td>0.693 ± 0.022</td><td>0.696 ± 0.022</td><td>0.694 ± 0.022</td></tr><tr><td>Voting Ensemble</td><td>0.712 ± 0.012</td><td>0.713 ± 0.012</td><td>0.712 ± 0.012</td></tr><tr><td><strong>RoBERTa</strong></td><td><strong>0.767 ± 0.021</strong></td><td><strong>0.774 ± 0.015</strong></td><td><strong>0.774 ± 0.015</strong></td></tr></tbody></table>

# 5 Results Analysis and Discussion

We assigned the unlabeled dataset to the RoBERTa model. The results of the classifier can be seen in Table 3. The results show that 180,259 of the tweets spread or support misinformation related to the monkeypox virus, and 152,522 of the tweets counter misinformation related to the monkeypox virus.

The results indicate that 1,107,694, most of tweets, are neutral or irrelevant to the discussion on misinformation. This methodology allows us to quantify the scale of the discussion on misinformation in relation to the general discussion on monkeypox. The results show that only 30% of the tweets discuss misinformation related to the monkeypox virus.

Analyses of the misinformation and counter-misinformation categories indicate that for each tweet that spreads misinformation there are only 0.85 tweets that counter it. For each user that spreads misinformation there are 0.84 users that counter them. However, users that counter the conspiracy theories tweet slightly more per user than users that spread misinformation.

We examined the behavioral patterns in and between the misinformation and counter-misinformation categories. Figure 1 presents the weekly frequency of tweets in each group.
<table><caption>**Table 3.** The number of tweets and users in each category.</caption><thead><tr><td>Category</td><td># of Tweets</td><td># of Users</td><td>Tweets/User</td></tr></thead><tbody><tr><td>Spread misinformation</td><td>180,259</td><td>112,765</td><td>1.599</td></tr><tr><td>Counter misinformation</td><td>152,522</td><td>94,748</td><td>1.610</td></tr><tr><td>Neutral</td><td>1,107,694</td><td>401,458</td><td>2.759</td></tr></tbody></table>

Two major peaks are observed, and are likely related to the epidemiological evolution of the virus. The first peak is during May 2022, when according to data from the WHO, the number of new cases reported globally started growing exponentially [27]. The second peak of July and August 2022 took place during a peak of new confirmed cases.

Tweets that spread misinformation have dominated the conversation since the beginning of the outbreak. However, a shift in dominance took place at the beginning of the second peak, possibly marking the last cycle of misinformation [21].

<figure><img src="image_4.png" /><figcaption>**Fig. 1.** Weekly frequency time series of tweets spreading misinformation and tweets countering misinformation. The blue line represents the spreading tweets and the red line represents the countering tweets.</figcaption></figure>

We analyzed the users that participated in the conversation over time. Figure 2 presents the number of unique users that participated in the discussion each week, as well as the number of new unique users who participated in the discussion for the first time. The results show that the discussion is mostly driven by users that participate in the discussion for the first time. This behavior indicates that the interest in the discussion on misinformation related to the monkeypox virus is authentic and not artificially created by a small number of users.

To investigate patterns in the behavior of users, we analyzed the domains that were linked in the misinformation and counter-misinformation categories. Domains that do not provide any value to the analysis, such as social media websites and URL-shortening services were ignored.

Both the spreading and countering categories started by referencing news agencies based in the United Kingdom when the outbreak started. The reason for this behavior
<figure><img src="image_5.png" /><figcaption>**Fig. 2.** The weekly number of unique participating users and new unique users.</figcaption></figure>

is likely because the first cases of the outbreak were reported in the United Kingdom and Ireland [27]. The categories have quickly diverged and started referencing different websites from that point onwards.

Figure 3 and Fig. 4 show the top 10 most referenced domains in each category and their percentage in the respective category. The most referenced domains in the category that counters misinformation are of authorities such as the U.S. Centers of Disease Control and Prevention (CDC), globally acknowledged entities such as the WHO, and established news agencies such as the NBC and the New York Times (see Fig. 3).

<figure><img src="image_6.png" /><figcaption>**Fig. 3.** Top 10 most referenced domains in the counter-misinformation category and their percentage in the category.</figcaption></figure>

On the other hand, the domains referenced in the category that spreads misinformation are mostly of websites that allow users to upload and publish their own content. YouTube leads the list, followed by other platforms that are often associated with extreme free speech and conspiracy theories (see Fig. 4).

These findings are in line with the findings of Micallef et al. [20] that investigated misinformation on COVID-19 and found that YouTube is the most frequently referenced website in both misinformation and counter-misinformation.
<figure><img src="image_7.png" /><figcaption>**Fig. 4.** Top 10 most referenced domains in the misinformation category and their percentage in the category.</figcaption></figure>

# 6 Conclusion

In this work, we investigated the discussion on misinformation related to monkeypox virus on Twitter. We trained multiple machine learning classification models and fine-tuned a RoBERTa model with a classification layer to classify the tweets as misinformation, counter-misinformation and neutral to the discussion. We found that the RoBERTa fine-tuned model outperformed the other methods and used it to classify the complete dataset.

We analyzed the classified dataset and found that only a third of the tweets related to monkeypox discuss misinformation, whereas two thirds of the tweets are neutral. Analysis of the users that participated in the discussion revealed that the interest is driven by users that participate in the discussion for the first time.

We also found that tweets countering misinformation referenced authoritative sources such as the CDC and WHO more often. As such, it may be recommended to provide more frequent updates on authoritative websites with reliable information for use by the users countering misinformation and indirectly support the fight in misinformation.

On the other hand, tweets that spread misinformation referenced mostly websites that host user content, such as YouTube. Encouraging these platforms to monitor content and adopt stricter community guidelines, would likely reduce the amount of misinformation to be shared. Additionally, it may be recommended to notifying users of social media platforms about the sources of the content and their credibility, to minimize unaware echoing of misinformation.

The data used in this work is limited to the discussion related to monkeypox on Twitter, over a relatively short period, during which the level of uncertainty is unclear and the discussion is still on-going. Future works may make use of data available from other social media platforms and analyze the discussion over a longer period.
**Acknowledgements.** This research has received funding from the European Union's Horizon 2020 research and innovation program under grant agreement No. 101021746, CORE (science and human factor for resilient society).

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