# **1 Introduction**

The Syrian civil war, raging for almost a decade, has led to some of the largest population displacement of our times. Host nations, even those that had followed an open-border policy (such as Turkey, Jordan and Lebanon), have seen their citizens become wary if not outright hostile toward refugees (Kirişci (2014), Nielsen (2016), Aktas et al. (2018), Getmansky et al. (2018), Yahya et al. (2018)). The European Union is militantly protecting its borders against new migrant flows and Turkey has announced the projected return of over one million Syrian refugees in a 20-mile-deep safe zone in northern Syria that includes the areas of Idlib, Tel Abyad, and Azaz (Hoffman and Makovsky (2021), Gall (2019)). The COVID-19 pandemic-induced border closures and the spread of the disease in crowded refugee camps have further increased calls for refugee return. Despite these hopes, in 2020 only 467,000 Syrian refugees returned home, while 1.8 million were newly displaced (NRC (2021)). Though UNHCR intention surveys indicate that most refugees would like to return at some point, many are unwilling to do so under the current conditions, which are further impacted by the large number of IDPs, and report that they have given up hope of being able to return in the next 5 to 10 years (NRC (2021)). Furthermore, many young and educated Syrians in the capital city of Damascus indicate that they would choose to emigrate if given the opportunity (Jalabi (2021)).

This paper analyzes refugee return and internal displacement in the internet era with a focus on understanding how the widespread use of social media captures considerations on return for Syrian refugees. In doing so, it also considers displacement in Syria as a lens through which to view the difficulties returning refugees may face. Though many have analyzed attitudes around return with surveys (Ghosn et al. (2021), Alrababa’h et al. (2020), Krishnan (2020)), this paper is, to our knowledge, the first exploration using observational social media text and image data on the prospects for refugee return in northern Syria. Northern Syria is a territory still militarily contested by the Syrian Opposition with the support of Turkey; the Assad regime with the support of Russia; and the Kurdish Autonomous Authority with the support of the US. Neighboring Turkey has also been vocal in plans for re-settling refugees in Turkish-controlled areas of Syria (Hoffman and Makovsky (2021)). The paper draws on original social media data from the three most popular social media platforms in Syria: Telegram, Facebook, and Twitter. Our focus is on attitudes, behaviors, and information dissemination around topics of internal displacement and refugee return.

To identify discussions and conditions that reflect internal displacement and return we use unsupervised machine learning (ML) methods to cluster topics of interest from the text and image data. We also run ‘seeded’ topic models, models run on subsets of the data filtered for keywords related to displacement, local governance, service provision, and return. Using insights from the topic models we run two sets of analysis. First, we use mixed effects models to identify changes in topic discussion post-return. Second, we use mediation analysis to understand the role that social media plays in the relationship between local violence and return.

We find that discussions of violence are more prevalent in areas with neither returnees nor IDPs, with regime military action, air strike warnings and the anti-ISIS campaign 29–52% more prevalent,<sup>1</sup> whereas discussions of services and the economy are more prevalent in areas with returnees and IDPs (an increase between 16% and 73%). Images posted in return groups are more likely to be goods for sale including cars, motorcycles, and other miscellaneous items (indicating active trade and commerce, between 79% to 700% more prevalent in areas with only returnees than areas with neither IDPs nor returnees). In non-return areas, images are more likely to be violence-related including militants and tanks (up to 113% more prevalent than in areas with IDPs 1These percentages reflect the percent difference in mean topic proportion between areas without returnees and IDPs and those with.

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