Content analysis of P ersian/ F arsi Tweets during COVID -19 pandemic in I ran using NLP (2020)

Abstract: Iran, along with China, South Korea, and Italy was among the countries that were hit hard in the first wave of the COVID-19 spread. Twitter is one of the widely-used online platforms by Iranians inside and abroad for sharing their opinion, thoughts, and feelings about a wide range of issues. In this study, using more than 530,000 original tweets in Persian/Farsi on COVID-19, we analyzed the topics discussed among users, who are mainly Iranians, to gauge and track the response to the pandemic and how it evolved over time. We applied a combination of manual annotation of a random sample of tweets and topic modeling tools to classify the contents and frequency of each category of topics. We identified the top 25 topics among which living experience under home quarantine emerged as a major talking point. We additionally categorized the broader content of tweets that shows satire, followed by news, is the dominant tweet type among Iranian users. While this framework and methodology can be used to track public response to ongoing developments related to COVID-19, a generalization of this framework can become a useful framework to gauge Iranian public reaction to ongoing policy measures or events locally and internationally.

## introduction as covid-19 has rapidly and widely spread in the united states and globally, this now pandemic is shaking up all aspects of daily life in all countries affected in an unforeseen manner. economic activities have been disrupted globally on an unprecedented scale @xcite and governments are resorting to a number of policies and measures trying to manage primary and subsequent health, economic and financial aspects of this crisis. while each country will have its unique experience of dealing with this pandemic, there are shared aspects in terms of how different societies deal with and react to the spread of the virus. these commonalities stem from the nature of the virus itself and the biological and psychological similarities of humankind regardless of geographical boundaries. additionally, there is the commonality in terms of policies that have been devised for mitigation and control of the virus across borders. iran, along with china, south korea, and italy has been among the countries that have been hit hard in the first wave of the viral spread the cause of which is to be yet fully explained. iranians have been using social media outlets such as telegram, whatsapp, twitter, instagram, and facebook to both receive a large portion of their daily news, in addition to spreading the information to one another and expressing their opinion about various developments in the country such as social unrest or in this case events and issues related to the covid-19 spread. leveraging machine learning and natural language processing (nlp) techniques we are conducting an ongoing analysis of the reaction of the persian/farsi speaking users foot_0 on social media starting with twitter. in this work, we applied topic modeling to find the themes of tweets posted in persian/farsi about covid-19, followed by manual annotation of a random subset of tweets to assess the distribution of various content types among tweets. we believe our framework can be valuable in monitoring public reaction to ongoing developments locally and internationally related to covid-19 pandemic, but additionally, a tool and platform to be used for future major economical, political, or health-related events among persian/farsi users. this study is organized as the following. in section 2, we briefly outline the data collection and preprocessing steps on tweets. in section 3, we outline the methods and experiments that were applied to raw data to obtain the results and insights. lastly, we report the insights and results of our data collection efforts and analysis in section 4. we conclude the paper in section 5 by summarizing the results and discussing future directions and next steps. ## data collection we used the social feed manager (sfm) (libraries, 2016) platform to collect tweets foot_1 . sfm is a software developed at the george washington university that uses twitter developer api to help researchers with collecting tweets. we listed a group of hashtags that are associated with covid-19 related persian tweets on twitter. hashtags and their corresponding english translation are shown in figure 1. we chose these hashtags based on the trends on twitter since we started the data collection. we also added new hashtags associated with persian covid-19 on twitter which we will use in our future analysis. we started tweets collection process since march 12 th , 2020 and we're still collecting tweets in real-time so that we can do future processing on tweets and update our results. the results in our experiment section are using tweets from march 13 th to the end of april 19, 2020. ## experiments 4 and methods in this round of analysis, we use topic modeling, latent dirichlet allocation (lda), to analyze the topics of original tweets. the main goal here for using lda is to find the topics that are being discussed in persian tweets. we discuss this analysis in section 3.1. we also annotated a random sample of tweets from two days to find out what type of content tweets are mostly about. we discuss the details of annotation in section 3.2. ## discussion and results in this round of analysis, we report our findings and insights from analyzing tweets on three areas. we first share our insights about level of activity of the users who tweeted in persian around covid-19 over time. next, we look at the correlation between different types of covid-related tweets with the official number of confirmed cases in iran. lastly, we outline the result of our analysis on the content of the tweets to show what topics were mostly talked about among the users around covid-19 during this ongoing crisis. related to the topic and content, we additionally broke down the tweets in terms of the type of the language users used when responding to covid-19. ## conclusion and next steps in this paper, we collected more than 530,000 original tweets in persian/farsi related to covid-19 pandemic over time and analyzed the content in terms of major topics of discussion and the broader category of tweets. we applied a combination of manual annotation of a random sample of tweets and topic modeling tools to classify the contents and frequency of each category of topics. we identified the top 25 topics among which living experience under home quarantine emerged as a major talking point. we additionally categorized broader contents of tweets that show satire, followed by news, is the dominant tweet type among the iranian users. there are a few next steps and directions that we are actively working on. 1) we are interested to understand how covid-19 related conversations among iranian users shifted and evolved over time by tracking discussion topics within specific time windows, 2) continue to conduct manual annotation of tweets for our topic analysis, as a continuation of content analysis described in section 3.2, for the newly added data points we collect. we expect these annotations to help us to better understand and measure public's reaction to the pandemic and to the specific events that unfolded over time, 3) we are interested to conduct a deeper analysis on the factuality of news and information shared on twitter and categorizing the different types of information in terms of validity and accuracy. for instance, we have observed examples of wrong information and claims about vaccines or treatments for corona-virus in the early days creating a black market for sales of these products. we are running more analysis to initially find the themes of such false information and additionally identify different strategies that are being used for spreading it.