can be used to understand certain questions regarding refugee return. By leveraging these results, policymakers and program managers can augment current data sources and even supplant them in areas where contacts are not readily made or available.

To address biases, it is important to consider what populations are being reached with data collection and what populations may be left out, such as those without internet access. In addition, researchers should note the ‘public’ nature of social media, and thus choose projects for which public opinions and attitudes are salient. For example, research focusing on highly sensitive or personal issues may benefit from a different data source. Finally, just as in a survey, it is necessary to consider that an individual’s public expression of their views may differ from their personal actions and motives. Using a variety of sources can help mitigate some bias issues, as different sources have different policies on censoring violent material and thus may be used for different purposes.

Our research showcases the granularity at which social media can provide insights. For instance, as one would expect, we find that there is a clear, statistically significant difference in mentions of violence in areas with more return. This trend prevails even while looking at specific subdistricts, thus indicating the geographic granularity at which social media can provide a signal. Another interesting observation from our analysis is the role of Turkey and its mentions on social media. The high prevalence of topics related to Turkey, Kurdish politics, extremism and factions suggests that these are key issues impacting return in northern Syria. Social media data provides a window into discussions around violence and trust in these areas, including the role of foreign powers and the politics involved in the conflict.

Apart from security, discussion of governance, goods provision, and relief organizations are more prevalent in areas which people have returned to. An important difference in return and non return seems to be access to services as well as markets for goods in places of return. Thus, it is important to think of policies that stabilize the provision of such services and provide for the flow of goods. Those interested in the economic needs of Syrians can look to groups online, including online markets and job postings, for information about what goods are in high demand as well as current inflation levels. Images in these groups also indicate a clear trend that mobility is important for returnees: there is a high prevalence of bike and car sales in areas with return.

Ultimately, social media data provides a relatively comprehensive picture of the situation on the ground in Syria, especially when a variety of sources are used. By identifying groups and pages in different regions, program managers can both monitor ongoing events and witness the impact of interventions on discussion and attitudes in real time. Such monitoring is less costly than survey and other methods, and feedback is more immediate.

**Next steps** . Most of the analysis in this paper focused on descriptive analysis of trends that can be gleaned from publicly available social media data. Even though the descriptive analysis presented in this paper are valuable (Munger et al. (2021)), the value of such insights for policy makers might be even more relevant in a _predictive_ setting. This is one of the areas which future research may focus on. The clear trends in various types of analysis indicating the presence of refugee return and IDPs gives us hope that such trends can be used to predict whether future return will occur in a location just by looking at trends in social media discussion. The implication from causal mediation that information sharing on social media plays a role in return outcomes provides additional support for this goal.

Given the longitudinal nature of the data, we have the opportunity to clearly identify and quantify changes in tone of the conversation, particularly around the time of return in various locations. Many sentiment analysis tools for Arabic rely on messages from platforms such as Yelp and thus are unlikely to capture the nuances of opinion in a conflict zone. For this project we 23