# A Retrospective Analysis of the COVID-19 Infodemic in Saudi Arabia
## 2 Background and Related Work
### Tools to Understand, Measure, and Control the COVID-19 Infodemic  
While an infodemic cannot be eliminated, it can be managed with the right precautions. The World Health Organization has led the efforts in this field to guide relevant research and effective practices across the globe and define public health research needs in order to advance this field during the COVID-19 pandemic. The first WHO Infodemiology Conference held on 29 June 2020, and several conferences followed, which had a threefold contribution: (1) Understanding the multidisciplinary nature of infodemic management; (2) identifying current examples and tools to understand, measure, and control infodemics; and (3) building a public health research agenda to direct focus and investment in this emerging scientific field with the overall aim of establishing a community of practice and research. A framework for managing infodemics was proposed, which requires a transdisciplinary approach to address the problem's complexity, including several disciplines such as mathematics, digital health, data science, and social and behavioral sciences [25].  
Similarly, UNESCO published policy briefs focusing on the increasing threat related to the spread of COVID-19 misinformation. The briefs analyze the types of misinformation, investigate how individuals, governments, and media platforms respond to this phenomenon, review actions to combat it, and assess risks associated with applied measures to limit its spread. Finally, they provide recommendations on how to respond to the crisis, taking into consideration human rights concerns such as freedom of expression and privacy [26]. One method used by the agency is to promote facts about the COVID-19 disease from credible sources and motivate people to be more critical towards the information they see  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Dataset** & **Timeframe** & **Tweets** \\ \hline ArCov-19 [21] & January–June 2020 & 785,000 \\ ArCorona [22] & 21 February–31 March 2020 & 1,000,000 \\ Adawood’s Dataset [23] & January–April 2020 & 3,800,000 \\ Alam [24] & January–April 2020 & 218 \\ Alsudais Dataset [9] & December 2019–April 2020 & 1,048,575 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Public Annotated COVID-19 Datasets in Arabic language.  
online using hashtags campaigns such as ThinkBeforeClicking, ThinkBeforeSharing, and ShareKnowledge [27; 28].  
Furthermore, numerous recent studies examined the impact of Twitter and other social media platforms on COVID-19 and infodemics. [PERSON] et al. [29] studied the information and misinformation landscape over a year-long period of Twitter to characterize the spread on social media. They used clustering and topic modeling techniques to identify the major narratives, including health misinformation and conspiracies. They found that the echo chamber effect contributes to misinformation spread as users who share questionable content are clustered more closely in the network than others. [PERSON] et al. [30] explored the use of network analysis techniques to detect disinformation campaigns and generate features for distinguishing them from legitimate activities. They trained a binary classifier based on statistical features extracted from both networks. The results showed that coordination patterns could be helpful for providing evidence of disinformation activity.