# **6. Concluding remarks**

This study investigates whether Twitter data can be used to infer attitudes towards COVID vaccination with an application to the Arabic speaking world. Our objectives are three-fold. First, to estimate the prevalence of anti-vaccine attitudes using Twitter data. Second, to identify which messengers and what arguments are found to be most effective. Third, to assess how Twitterbased estimates of vaccine hesitancy compare to estimates obtained from survey data.

The following findings stand out. At first glance, anti-vaccine sentiment estimated from Twitter data is surprisingly low in comparison to estimates obtained from survey data. Only about 3 percent of Twitter accounts in our database are identified as anti-COVID-vaccination (compared to 20 to 30 percent of survey respondents). The most effective messages on the anti-vaccine side highlight claims that the vaccine causes serious life-threatening side effects. In the pro-vaccine camp, tweets containing content showing public figures receiving the vaccine are found to have the largest reach by far. This observation sits well with the earlier study by Alatas et al. (2019), which finds that Twitter endorsements of vaccination by celebrities are most effective. The study furthermore finds that celebrity endorsements can influence beliefs about vaccination. With respect to messengers in our application to the MENA region, we find that state-news accounts receive the overwhelming majority of retweets in our database.

To explain why our estimates of anti-vaccine attitudes are visibly lower than survey-based estimates, which are found to range approximately between 20 to 30 percent, we consider three candidate sources of bias. First, Twitter accounts include both individuals and organizations, whereas survey respondents consist of individuals only. The organizations in our sample are primarily news organizations, NGOs, private companies, law firms, government entities, and international bodies – all of which are more likely to express overtly positive attitudes towards vaccination. Individuals, on the other hand, are more likely to hold anti-vaccine attitudes and express them explicitly online. Second, Twitter users are a selective sub-sample of the population biased towards higher levels of education, higher income and increased political engagement. Third, the vaccine rollout during the period in which the data for our sample was collected was slow and uneven. Therefore, users may not have developed strong views on vaccination during this period, nor did they have the incentive to express these views on Twitter.

Most of the difference between the Twitter-based and survey-based estimates of COVID vaccine hesitancy can be accounted for by restricting Twitter accounts to those belonging to individuals. Using the survey data, we furthermore find that vaccine hesitancy declines with the respondent’s level of education. Restricting the survey samples to individuals who completed a secondary degree or higher we obtain survey-based estimates of vaccine hesitancy that are of the same magnitude as Twitter-based estimates of hesitancy (among accounts belonging to individuals).

Two possible directions for future work include: (a) extending this analysis to other regions of the world, starting with the Twitter discourse on COVID vaccination in English (which will be a magnitude larger in volume), (b) extending the analysis to other social media platforms, 17