CMTA : COVID -19 Misinformation Multilingual Analysis on T witter (2021)

Abstract: The internet has actually come to be an essential resource of health knowledge for individuals around the world in the present situation of the coronavirus condition pandemic(COVID-19). During pandemic situations, myths, sensationalism, rumours and misinformation, generated intentionally or unintentionally, spread rapidly through social networks. Twitter is one of these popular social networks people use to share COVID-19 related news, information, and thoughts that reflect their perception and opinion about the pandemic. Evaluation of tweets for recognizing misinformation can create beneficial understanding to review the top quality and also the readability of online information concerning the COVID-19. This paper presents a multilingual COVID-19 related tweet analysis method, CMTA, that uses BERT, a deep learning model for multilingual tweet misinformation detection and classification. CMTA extracts features from multilingual textual data, which is then categorized into specific information classes. Classification is done by a Dense-CNN model trained on tweets manually annotated into information classes (i.e., ‘false’, ‘partly false’, ‘misleading’). The paper presents an analysis of multilingual tweets from February to June, showing the distribution type of information spread across different languages. To access the performance of the CMTA multilingual model, we performed a comparative analysis of 8 monolingual model and CMTA for the misinformation detection task. The results show that our proposed CMTA model has surpassed various monolingual models which consolidated the fact that through transfer learning a multilingual framework could be developed.

## introduction since late 2019, the coronavirus disease covid-19 has spread worldwide to more than 216 countries @xcite . covid-19 has created a massive impact on multiple sectors including countries economy, government bodies, private companies, media houses and most importantly, affecting the mental and physical health of human beings by tempering their daily routine activities @xcite . covid-19 also has made us realize how well the world is interconnected through the internet. social media is a significant conduit where people share their response, thoughts, news, information related to covid-19, with one in three individuals worldwide participating in social media, with two-thirds of people utilizing it on the internet @xcite . studies have shown that many people connect to the internet and social media platforms such as twitter, facebook, whatsapp, instagram and reddit every day and utilizing it for getting information/news through them (matsa and shearer, 2018) @xcite . twitter users are known, especially, for posting and exchanging news: almost 60% of twitter users classify it as excellent or incredibly helpful for sharing preventive health information @xcite . nonetheless, social media is still full of misinformation regarding health. it is difficult to assess the authenticity of health information on the internet for people with non-medical experience. precise and reliable dissemination of correct information about the virus that causes a pandemic will help to monitor the spread of the virus and related population anxiety @xcite . social media content and misinformation may have intense implications for public opinion and behavior, positively or negatively influencing the viewpoint of those who access it @xcite . the who director-general stated at the munich security conference in february 2020, 'we are not only fighting an epidemic; we are fighting an info-demic' @xcite . it is clear that there is no way of stopping the transmission of covid-19, so it is necessary to check information on the internet in order to prevent the panic and disinformation linked to the disease. seeking accurate and valid information is the biggest challenge with internet health information @xcite . misinformation appears in several ways in the case of covid-19, such as 'covid-19 is a biological agent developed by either the us or china','covid-19 is the potential by-product of chinese cuisine, such as bat soup amongst other ingredients,'and 'breath-holding self-detection test', unconfirmed home remedies such as vitamin c, urine from animals, turmeric etc. in its worse, this type of misinformation will lead individuals to resort to unsuccessful (and actually directly harmful) remedies, either to overreact (e.g. by hoarding goods) or to underreact quite dangerously (e.g., by deliberately engaging in risky behavior and inadvertently spreading the virus). @xcite . unfortunately, the fake news spread faster than the virus @xcite ). an online social platform such as twitter provides particularly fertile ground for the spread of misinformation @xcite . twitter gives direct access to extraordinary content, which may intensify rumors and dubious information @xcite . with such a huge amount of human-generated information being exchanged every day, it has attracted natural language processing (nlp) researchers to explore, analyze, and generate valuable insights about people response to covid-19. people response is analyzed with respect to sentiments and misinformation and malicious information detection. this paper proposes cmta, a multilingual tweet analysis and information (misinformation) detection method for understanding both the negative and positive sides of social media during covid-19 pandemic. cmta uses multilingual bert, trained on 104 multiple languages to derive features from tweets and 1d convolution for finding the correlation between data of hidden states. it also uses a dense layer for linear transformation on contextual embeddings to provide inferential points. our work helps in providing better results in finding the proximity of being fake. we used manually annotated multilingual covid-19 related tweets for training deep neural network model in order to detect and identify the type of misinformation present in tweets belonging to different language groups. for experimenting with our method, we used trained models for a systematic analysis of covid-19 related tweets collected from february to june 2020. the analysis of tweets is done based on the distribution of the type of information present in tweets concerning the language used for writing a tweet. we investigated the presence of false information spread throughout tweeter by classifying the tweets in three classes: 'false', 'partly false' and 'misleading'. we have provided illustrative statistical representation of our findings and detailed discussion about the insights discovered in our survey. the motivation for designing a multilingual method lies behind the need of analyzing not just monolingual tweets but also multilingual tweets by building a single deep learning framework that would be able to understand tweets in multiple languages. that being said, we also analysed the performance of cmta multilingual bert framework with respect to 8 monolingual bert models. the performance score achieved by the multilingual model were very close to that of monolingual models which suggests that utilizing a singular multilingual model for covid-19 tweet analysis and disinformation categorization is a reliable and robust method. ## related work the covid-19 pandemic has resulted in studies investigating the various types of misinformation arising during the covid-19 crisis @xcite @xcite . studies investigate a small subset of claims @xcite or manually annotate twitter data @xcite . in (brennen et al., 2020) authors analyse different types of sources for looking for covid-19 misinformation. pennycook et al. @xcite introduced an attention-based account of misinformation and observed that people tend to believe false claims about covid-19 and share false claims when they do not think critically about the accuracy and veracity of the information. kouzy et al. @xcite annotated about 600 messages containing hashtags about covid-19, they observed that about one-fourth of messages contain some form of misinformation, and about 17% contain some unverifiable information. with such mis-information overload, any decision making procedure based on misinformation has a high likelihood of severely impacting people's health (ingraham and tignanelli, 2020). the work in @xcite examined the global spread of information related to crucial disinformation stories and "fake news" urls during the early stages of the global pandemic on twitter. their study shows that news agencies, government officials, and individual news reporters send messages that spread widely and play critical roles. tweets citing urls for "fake news" and reports of propaganda are more likely than news or government pages shared by regular users and bots. the work in @xcite focused on topic modelling and designed a dashboard to track twitter's misinformation regarding the covid-19 pandemic. the dashboard presents a summary of information derived from twitter posts, including topics, sentiment, false and misleading information shared on social media related to covid-19. cinelli et al. @xcite track (mis)information flow across 2.7m tweets and compare it with infection rates.they noticed a major spatiotemporal connection between information flow and new covid-19 instances, and while there are discussions about myths and connections to lowquality information, their influence is less prominent than other themes specific to the crisis. to find and measure causal relationships between pandemic features (e.g. the number of infections and deaths) and twitter behaviour and public sentiment, the work in (gencoglu and gruber, 2020) introduced the first example of a causal inference method. their proposed approach has shown that they can efficiently collect epidemiological domain knowledge and identify factors that influence public interest and attention. the discussion around the covid-19 pandemic and the government policies was investigated in @xcite . they used twitter data in multiple languages from various countries and found common responses to the pandemic and how they differ across time using text mining. moreover, they presented insights as to how information and misinformation were transmitted via twitter. similarly, to demonstrate the epidemiological effect of covid-19 on press publications in bogota, colombia, (saire and navarro, 2020) used text mining on twitter data. they intuitively note a strong correlation between the number of tweets and the number of infected people in the area. most of the works described above focus on analysing tweets related to single language such as english. in our work we have designed a single model leveraging multilingual bert for the analysis of tweets in multiple languages. furthermore, we used a large data set to train and analyze the tweets. our aim is to provide a system that will be restricted to any language for analysing social media data. ## data preparation this section discusses the steps involved in the collection of covid-19 related tweets. for training our misinformation detection deep learning model, we have extracted annotated misinformation data from multiple publicly available open databases. we also collected a very large number of multilingual tweets consisting of over 2 million tweets belonging to eight different languages. ## multilingual misinformation analysis in this section, we provide a detailed analysis misinformation distribution across the multilingual tweets. we used our trained multilingual model to predict and categorize the misinformation type present in tweets. we conducted our sequential misinformation analysis on a collection of over 2 million multilingual tweets. our survey studied and analyzed the distribution of covid-19 misinformation across eight major languages, (i.e. 'english', 'spanish', 'indonesian', 'french', 'japanese', 'thai', 'hindi' and 'german') for five months (i.e. february, march, april, may and june). figure 4 shows the month-wise distribution of misinformation types for each language. thai have the least of all. following more specific observation made with respect to the languages: we can also see that there was a huge increase in misleading tweets in march, tweeted in ## conclusion and future work in this paper, we presented a bert based multilingual model for analysing covid-19 related multilingual tweets. we performed a detailed systematic survey for detecting disinformation spread on the social media platform-twitter. we were able to detect misinformation distribution across eight major languages and presented a quantified magnitude of misinformation distributed across different languages in last 5 months. we also demonstrated that our single multilingual cmta framework performed significantly well as compared to the monolingual misinformation detection models. we strongly believe that our model can help in filtration of misinformation and factual data present in multiple languages during the pandemic. in future, we aim at collecting more annotated training data and performing analysis of a larger multilingual dataset to gain deeper understanding. we aim at improving our model's robustness and contextual understanding for better performance in the classification task. since analysis was done on a limited dataset the results cannot be generalised. we hope that through our work researchers could gain more deeper insights about misinformation spread across major languages and hence utilizing the information in building more reliable social media platform.