metadata: authors: 0: author: Walk,Erin Elizabeth
1: author: Garimella,Kiran
2: author: Christia,Fotini
count: Syrian Arab Republic
docna: 0: docna: Displacement and Return in the Internet Era
            : How Social Media Captures Migration Decisions in Northern Syria
docty: Policy Research Working Paper
abstracts: cdata!: Starting in 2011, the Syrian civil war
            has resulted in the displacement of over 80% of the Syrian
            population. This paper analyzes how the widespread use of
            social media has recorded migration considerations for
            Syrian refugees using social media text and image data from
            three popular platforms (Twitter, Telegram, and Facebook).
            Leveraging survey data as a source of ground truth on the
            presence of IDPs and returnees, it uses topic modeling and
            image analysis to find that areas without return have a
            higher prevalence of violence-related discourse and images
            while areas with return feature content related to services
            and the economy. Building on these findings, the paper first
            uses mixed effects models to show that these results hold
            pre- and post- return as well as when migration is
            quantified as monthly population flows. Second, it leverages
            mediation analysis to find that discussion on social media
            mediates the relationship between violence and return in
            months where there are fewer violent events. Monitoring
            refugee return in war prone areas is a complex task and
            social media may provide researchers, aid groups, and
            policymakers with tools for assessing return in areas where
            survey or other data is unavailable or difficult to obtain.
display_title: Displacement and Return in the
            Internet Era : How Social Media Captures Migration Decisions
            in Northern Syria
listing_relative_url: /research/2022/04/33802945/displacement-return-internet-era-social-media-captures-migration-decisions-northern-syria
topicv3: Forced Displacement m1326032 740,Social Media m1327099 662,Social Cohesion m1327189 738,Conflict and Violence m1327549 2433
publishtoextweb_dt: 2022-04-26T00:00:00Z
dois: 10.1596/1813-9450-10024
origu: Off of Sr VP Dev Econ/Chief Econ (DECVP)
author_keys: 0: author_key: 2000640
1: author_key: 930882
model_extractions: Policy Research Working Paper 10024 Displacement and Return in the Internet Era How Social Media Captures Migration Decisions in Northern Syria _Erin Walk Kiran Garimella Fotini Christia_ Social Sustainability and Inclusion Global Practice April 2022
Policy Research Working Paper 1024

# **Abstract**

Starting in 2011, the Syrian civil war has resulted in the displacement of over 80% of the Syrian population. This paper analyzes how the widespread use of social media has recorded migration considerations for Syrian refugees using social media text and image data from three popular platforms (Twitter, Telegram, and Facebook). Leveraging survey data as a source of ground truth on the presence of IDPs and returnees, it uses topic modeling and image analysis to find that areas without return have a higher prevalence of violence-related discourse and images while areas with return feature content related to services and the economy. Building on these findings, the paper first uses mixed effects models to show that these results hold preand post- return as well as when migration is quantified as monthly population flows. Second, it leverages mediation analysis to find that discussion on social media mediates the relationship between violence and return in months where there are fewer violent events. Monitoring refugee return in war prone areas is a complex task and social media may provide researchers, aid groups, and policymakers with tools for assessing return in areas where survey or other data is unavailable or difficult to obtain.

This paper is a product of the Social Sustainability and Inclusion Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at ewalk@mit.edu.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# Displacement and Return in the Internet Era: How Social Media Captures Migration Decisions in Northern Syria

## **JEL Classification:** F22, F52

## **Keywords:** Social media, Displacement, Refugee return, Syria, Migration, Civil War

_This paper was commissioned by the World Bank Social Sustainability and Inclusion Global Practice as part of the activity “Preventing Social Conflict a nd P romoting S ocial C ohesion i n Forced Displacement Contexts.” The activity is task managed by Audrey Sacks and Susan Wong with assistance from Stephen Winkler. This work is part of the program “Building the Evidence on Protracted Forced Displacement: A Multi-Stakeholder Partnership”. The program is funded by UK aid from the United Kingdom’s Foreign, Commonwealth and Development Office ( FCDO), i t is managed by the World Bank Group (WBG) and was established in partnership with the United Nations High Commissioner for Refugees (UNHCR). The scope of the program is to expand the global knowledge on forced displacement by funding quality research and disseminating results for the use of practitioners and policy makers. This work does not necessarily reflect t he v iews of FCDO, the WBG or UNHCR._
# **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.

3
and returnees). These results hold when enabling correlation between topics and adding time and location effects, as shown using mixed effects models. Furthermore, using more granular data on return and displacement flows we show that increased return is associated with economy topics and negatively associated with violence topics, while increased displacement is positively associated with violence topics. Finally, we use causal mediation analysis to show that in months with lower violence levels increased discussion of violent events mediates the relationship between violence and return.

In summary, our paper makes the following contributions: First, it shows the benefits of social media data, including images and messages, for fleshing out motives and discussions around displacement and return. Most research to date has been done using surveys and on the ground interviews which are costly and not always possible. By combining survey and other data with social media data, future research can leverage the precision of the former with the ease of remote access of the latter (Singh et al. (2019)). Second, we combine text analysis with image analysis to see to what degree images dovetail with the discourse and what additional information we can extract from this medium.

The rest of the paper is organized as follows: First, we discuss the context for our project including the situation in Syria, reasons for displacement and ongoing violence, and relevant literature on displacement, attitudes around return, and social media use. Next, we outline our research design and data collection, including procedures for choosing groups as well as text and image analysis methods. We then present our results and discussion, outlining our findings on return and internal displacement from topic models, images, mixed effects, and causal mediation analysis. We conclude with program and policy implications and suggestions for next steps.

# **2 The Syrian Context**

Since the start of the Syrian civil war in March 2011, Syria has seen unprecedented levels of internal displacement, movement of refugees, and civilian casualties. The war began as a protest movement in 2011 to remove President Bashar Al-Assad from power, a movement that coincided with Arab spring protests that removed the Egyptian and Tunisian presidents. Prior to the civil war there were already high levels of perceived corruption and diminished trust in public institutions (Bank (2017)). By 2012, the protests had evolved into militarized violence. As the war is in its 10th year over 400,000 Syrians have been killed and 13.2 million Syrians are refugees, asylum seekers, and internally displaced people, accounting for one sixth of the global total and 80% of the total Syrian population (UNHCR (2020)). Of these 13.2 million, 6.6 million are registered refugees, about two thirds of which reside in Turkey and five sixths of which reside in countries bordering Syria, the majority not in refugee camps (UNHCR (2021)). The war has exacted a heavy economic and social toll both on Syria and its neighboring countries (Bank (2020b), Bank (2017)).

The Syrian civil war is also complex in terms of foreign intervention. Since the early stages of the war many foreign parties have been involved in the conflict including Russia, Iran, and the Lebanese militia Hezbollah on behalf of the regime (Phillips (2016)), and states in the Gulf and Turkey on behalf of competing opposition factions. The US has intervened in support of the Kurdish Syrian Democratic Forces (SDF) and International Coalition partners against ISIS. Meanwhile, foreign fighters and mercenaries have joined the conflict primarily on behalf of jihadist groups including ISIS (Mitts (2019)). Syrians have been displaced by air strikes and conflict in their home cities, including takeover by militants and regime forces, which is made even more volatile by the constantly changing factions and areas of control. There has also been a notable decrease in economic opportunity due to destruction of cities and roads as well as sanctions aimed at weakening 4
the regime.<sup>2</sup> The coalescing of these various groups and interests makes the situation, and return prospects, in northern Syria incredibly complex. Turkey has faced a lack of stability and domestic political strife over the influx of refugees since 2011, yet as the situation draws on many seem less likely to return. In 2017 the annual Syrians Barometer poll conducted by Murat Erdoğan indicated that 17% of Syrians would not return under any conditions, and in 2019 this had increased to 52% (Erdogan (2019)). Many more indicate they would only return if the war ended and a governance structure which they favored was put into place. In order to stem the flow of refugees, and in some instances force return, Turkey has taken strong offensive actions which limit international humanitarian aid through both stifling opportunity and igniting humanitarian concerns over treatment of Kurds.<sup>3</sup> In many areas governing and aid provision are overseen by the governors of neighboring Turkish provinces (Hoffman and Makovsky (2021), Al-Hilu (2019)). In Idlib, most of the structure is provided by former Al-Qa’eda affiliated Hayat Tahrir Al Sham, with some collaboration with Turkey. The segmentation of areas of control and the closure of border crossings other than Bab al-Hawa make the work of humanitarian actors in Syria quite difficult. Many actors have channeled aid for their own purposes to control goods provision, and the burden of provisioning aid falls overwhelmingly on Syrian workers who may also be targets for attack (Hall and Todman (2021)). Though satisfaction with the impact of humanitarian aid has increased, organizations still face challenges in regards to incorporating refugees into aid processes and decision making, funding local organizations who can target aid, and coordinating aid provision (Voluntas (2019)).

Since the defeat of ISIS by the International Coalition forces in Ar-Raqqa in October 2017 the war in Syria seems to be nearing its end. However, though 75% of displaced Syrians would like to one day return to their former homes (UNHCR (2019)), ongoing violence has resulted in continued displacement (NRC (2021)). Syrians in Damascus, especially the young and educated, indicate they would be interested in emigrating if the opportunity presented itself, but they are unable to do so for monetary and other reasons (Jalabi (2021)). Furthermore, desire to return is linked to strong ties with one’s home country and hometown, ties which are likely to erode the longer the war drags on and the longer individuals are displaced (Ghosn et al. (2021)). Return to Syria in general has been infrequent and selective, though better security and service access increases returns (Bank (2019)).

Within Syria some governorates including Deir-ez-Zour, Ar-Raqqa, and Al-Hasekah have lost a large share of their population (all over 25%) whereas others such as Idlib and Rural Damascus have gained inhabitants (Bank (2019)). In the 2015-2016 Syrian Refugees and Host Communities Surveys (SRHCS) most refugees reported less than a week to prepare to leave, with the majority ending up in a neighboring country (Krishnan (2020)). Refugees in neighboring countries are less likely to be highly educated, with only 1% having completed a university degree and most working in wage jobs and construction. In Lebanon, manufacturing, construction, and agriculture are the only sectors available for refugee employment. More Syrian men are able to work in Syria, but refugees have better access to resources than IDPs and residents in Syrian governorates with a high level of conflict. Conversely, refugee children often have worse access to education. Housing conditions in Syria are poor, especially in Idlib, and many returnees lack documents (Bank (2019)). More recently

> 2Documentation of U.S. sanctions on Syria `https://home.treasury.gov/policy-issues/ financial-sanctions/sanctions-programs-and-country-information/syria-sanctions` 3 “German NGO scraps Syria project over claims it would aid Turkey’s ethnic cleansing in Afrin,” `https://www.kurdistan24.net/en/story/ 23475-German-NGO-scraps-Syria-project-over-claims-it-would-aid-Turkey% 27s-ethnic-cleansing-in-Afrin` .

5
the COVID-19 pandemic has deeply impacted refugee communities in the aforementioned locations. Refugees in host countries were more likely to be living below the poverty line pre-pandemic and refugees were often more highly impacted due to reliance on wage work (Bank (2020a)). Within Syrian, Syrians living in all regions are subject to a lack of services including electricity and water, and 60% of Syrians are food insecure (WFP (2021)).

# **3 Relevant Literature**

The situation of Syrian refugees is not unique, with over 80% of global refugees hosted by developing countries bordering the regions refugees flee from (UNHCR (2020)). These host countries struggle with service provision and maintaining the requisite bureaucracy to determine refugee legal status, while also facing backlash and anti-refugee sentiment among their own citizens (Lazarev and Sharma (2017), Alrababa’h et al. (2021), Chu et al. (2019), Braithwaite et al. (2019), Bradley (2013)). Refugees may be displaced from their home country for many reasons including economic, political, and environmental factors, and their destination depends on similar factors in potential host countries (Arias et al. (2014), Lischer (2005), Rüegger (2013), Davenport et al. (2003), Zolberg et al. (1989), Martin et al. (2019)). Kunz (1973) codes displacement as anticipatory, when the refugee is prepared to leave before the situation in their home country has degraded, or acute, when refugees are forced to leave due to government failure, violence, or environmental conditions (Kunz (1973)). The relative importance of different displacement conditions varies depending on region and political climate (Singh et al. (2020)). Given area politics, we may expect similarities in displacement between Syria and Iraq, where those topics which generated the most buzz prior to displacement were related to politics, insecurities, and infrastructure (Martin and Singh (2018)).

Barring local conditions, violence is often considered the key driver of displacement (Davenport et al. (2003), Zolberg et al. (1989)). In a 2020 CARE survey conducted through interviews with Syrian IDPs, 99% said they had been displaced due to violence or fighting (Hoffman and Makovsky (2021)). The type and prevalence of violence also matters, in that state sponsored violence and genocide are more likely to lead to refugees whereas civil wars are associated more highly with internal displacement (Steele (2019), Moore and Shellman (2006)). Individuals who witness, but do not necessarily directly experience, violence are more likely to delay leaving if they receive community support and experience post-traumatic growth (Schon (2019)).

Enabling return for the displaced requires a good understanding of how the displaced view their possibilities and options around return (Metivier et al. (2018), Camarena and Hägerdal (2020)) as well as the broader effects of such return on postwar politics (Fabbe et al. (2019)) or on security (Van Leeuwen and Van Der Haar (2016), Camarena (2016), Schwartz (2019)). Willingness to confront danger on the path to return may also be impacted by refugees’ experiences of violence prior to return and belief in their ability to adapt to circumstances on the ground (Ghosn et al. (2021)). As a result, refugees who spent more time in their home country before fleeing, and were thus more likely to have been victims of violence, are more likely to return. In the Syrian context, refugees who are single and male are more likely to return since family ties often play a large role in these decisions (Bank (2019)).

## **3.1 Return Considerations**

Knowledge of the factors which initiate displacement is key to understanding when refugees may return. Individuals are more likely to return once there are economic opportunities, services, and social networks in place to support their transition, the same features which may have prompted movement away from a region when they became unavailable (Aymerich and Zeyneloglu (2019), Hoogeveen et al. (2017), Arias et al. (2014)). In a survey study conducted on 6
3,003 displaced Syrian families in Lebanon, Alrababa’h et al. (2020) weigh the influence of “pull” factors, which draw individuals back to their former homes, and “push” factors which encourage them to leave their host communities. In doing so they find that conditions in a refugee’s home country and access to information about those conditions are a primary driver in decision making (Alrababa’h et al. (2020)). Indeed, economic factors may be even more vital to return choices than sentiment towards or connection with their home country (Camarena and Hägerdal (2020)). Refugees with strong ties to their home country may still choose to return as regular visitors rather than permanently if their new residence offers more attractive economic opportunities. Additionally, refugees are less likely to return to areas with ethic mixing when ethic tensions were a factor in initial violence and displacement (Camarena and Hägerdal (2020)).

Refugees have complex relationships with their host communities as well as with their home locations. Displacement can be part of a strategy of war, with militants manipulating refugee and IDP movement to gain access to resources or ethnically cleanse certain areas (Lischer (2008)). As part of these strategies refugees may be militarized, spreading conflict to their new locations (Lischer (2008)). More positively, in areas with low public service provision the introduction of refugees to a community may bring goods and services which benefit all the residents (Zhou et al. (2021)). Such community benefits mitigate negative sentiment towards refugee settlements.

Identifying effective ways to measure return practices is not straightforward as there are no systematic data sources on refugee return, and many current reports rely on surveys to test the underlying assumptions governing return decisions (Ghosn et al. (2021)). Given the importance of information for decisions around return (Alrababa’h et al. (2020)) and gauging living conditions in the home country, leveraging social media sources is of critical relevance because they offer an immediate assessment of returnee preferences and thoughts on return as well as a way to measure how refugees access and consume information that would be pertinent to their return.

**Relevance to the call:** Our paper contributes to work on refugee resettlement and attitudes from other researchers in this call including Bove et al. (2021), Kaplan (2021), and Parry and Aymerich (2021). Bove et al. (2021) notes that refugees are more likely to return to areas with UN peacekeeping missions, as they provide a sense of stability and help with service provision. Local governments and militias in Syria may provide similar services in certain regions (Bove et al. (2021)). Additionally, local peacekeeping agreements, if crafted with the aide of those from all sides, may facilitate safer and lasting return. Such agreements may even work in cases of high tension, such as perceived ISIL affiliation of IDPs, if the IDPs are part of the discussion or trusted leaders work as negotiators for them (Parry and Aymerich (2021)). Finally, strong religious beliefs may increase the likelihood that individuals stay in their home communities even in the presence of violence (Kaplan (2021)), as well as making them more likely to encourage others to return. Our paper augments these studies, which focus on specific factors, by considering return as a whole in terms of discussions occurring in these communities. As a result, we can determine if the factors outlined here are present in social media discourse and to what extent discussion of them differs.

# **3.2 Social Media and Information Access**

Beginning with the protests in 2011, social media has played an integral role in the Syrian civil war leading researchers to refer to it as the “most socially mediated civil conflict in history” (Lynch et al. (2014)). In the early stages of conflict platforms such as Twitter, YouTube, and Facebook were used by civilian activists to organize and share images of the protests (Freelon et al. (2015)). As the protests evolved into war, many of the key players became involved on social media. Extremist groups have used Twitter to spread their ideology (Klausen (2015), Wei et al. (2016)), as well as sectarian hate speech (Siegel and Badaan (2020), Abdo (2015)), and propaganda (Chatfield et al. (2015)), while Syrian Opposition Forces have used Facebook to communicate their 7
war narrative (Crilley (2017)). The presence of perspectives from all sides of the conflict leads Gohdes (2020) to note that “the Syrian conflict is one of the first conflicts where lines between offline and on-line conflict engagement have become blurred” (Gohdes (2020)). Outside of the Syrian context, social media data is increasingly used alongside, or even in place of, public opinion surveys, often with highly similar results (Schober et al. (2016)).

In addition to Twitter and Facebook, the role of which have been more widely studied (Khamis et al. (2012), Metzger and Siegel (2019)), we extend analysis to include data from Telegram “an encrypted platform that is harder for governments to monitor” (Mitts (2019)). Much of the research on Telegram has focused on its use by ISIS (Prucha (2016), McDowell-Smith et al. (2017), Yayla and Speckhard (2017)), though Telegram’s privacy policies also support civilians and protest movements worldwide (Urman et al. (2020)). Though much work has been dedicated to the role of social media in constructing violence narratives, it also plays a vital role in migration movement (Frouws et al. (2016), Miconi (2020), Sánchez-Querubín and Rogers (2018)). Refugees share information about routes and conditions in potential host countries to ease the journey for future displaced peoples (Frouws et al. (2016)). In interviews conducted with 44 young refugee or immigrant Syrian social media users, Miconi (2020) finds that these platforms are used for not only staying connected to war developments but also facilitating resettlement in the host country. A similar Facebook specific study notes that members of the diaspora turn to the platform to maintain social ties (Ramadan (2017)). Given the ubiquity of social media in the conflict, it follows that Syrians use social media not just for the war effort or to learn of migration routes but also to assess local conditions for return.

In acknowledgement of the convenience and importance of social media, a recent line of inquiry involves combining social media data with traditional variables to predict displacement (Singh et al. (2019), Abrishamkar et al. (2018)). Singh et al. (2019) rely on Twitter data from Iraq to assess when violent events are taking place using sentiment analysis of tweets with the hashtag “ISIS”. In combining this with traditional movement variables, they improved the accuracy of displacement predictions. To show the flexibility of such techniques, they are adapting a Spanish language model for use on the Venezuelan crisis (Singh et al. (2020)). Similarly, Abrishamkar et al. (2018) adapted a variety of techniques to get a signal of violence from news articles, and used this signal as a factor in predicting displacement (Abrishamkar et al. (2018)). By assessing topic variations in return and non-return areas and areas with and without IDPs, and noting differences in discussion of violence, governance, and services, we show that social media reveals information about return decisions and builds a foundation for adapting such techniques to consider signals which favor return, as well as bringing in new aspects such as image analysis.

# **4 Research Design and Data Collection**

Our analysis relies on novel data collected from Telegram, Twitter, and Facebook in the latest stage of the war beginning after the recapture of Ar-Raqqa from ISIS on 17 October 2017 and going through 1 December 2020. Group selection processes focus on entities which are located in Syria or primarily discussing issues related to Syria. The success of work on predicting displacement using social media conversation (Abrishamkar et al. (2018)) as well as conclusions that social media can be an effective tool for measuring public opinion (Schober et al. (2016)) indicate that users discuss salient events and opinions on social media and thus we anticipate differences in discussion in areas with and without return or IDPs. Analysis is limited to messages posted in Arabic, as messages in the native language are more predictive of displacement (Singh et al. (2020)). We augment our collection of social media data with more traditional indicators gathered from surveys and interviews by the REACH resource center (REACH (2020)), the UN Office for the Coordination 8
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# **4.1 Group Selection on Social Media**

To examine refugee displacement and return on these platforms, we created parallel processes to identify, collect data from, and analyze accounts, channels, and groups posting public messages about the Syrian conflict on Twitter, Telegram, and Facebook. While this process aimed to limit selection effects to the extent possible, the different natures of the platforms ultimately mean that our samples comprise different populations, and the differences between search functionalities meant there was no single starting point. Underscoring the replicability of our process — and the process’s resemblance to other selection processes common in the use of social media data — we present source specific models in Appendix 9 to show the selection effects inherent in the study of social media users on any singular platform.

We limited our analysis to Arabic language, the predominant language of use among all actors in the conflict.<sup>5</sup> Our final dataset contains messages in Arabic from 657 public channels and groups on Telegram, 2,106 public Twitter accounts, and 2,124 public Facebook groups and pages.

# **4.2 Identifying Location Specific Messages**

The first step of our process was attributing message sets to certain locations. Twitter, Telegram and Facebook’s functionalities make it difficult to verify the location that users post from in all but a few cases. To understand location specific discussion we searched all of the collected messages for 2004 Syrian census locations using string matching on the names in Arabic. We augmented this dataset with locations from the GeoNames API<sup>6</sup> , which includes common misspellings or dialectic spellings of locations.<sup>7</sup> The `Location Mentions` dataset, comprised of the location mentioning messages associated with the REACH data, includes messages from 770 unique locations, 54 of which are mentioned more than 10,000 times, 229 of which have returnees, and 129 of which have IDPs. A heat map showing the distribution of mentions of locations overall, locations with returnees and locations with IDPs by source is in Appendix 8.1, as are maps with locations mentioned by returnee status and year of return. The majority of locations mentioned in the `Location Mentions` dataset are in northeastern Syria in Idlib, Aleppo and northern Ar-Raqqa, an outcome of REACH data emphasis, as can be seen in Section 4 Figure 1. For 218 of these locations return first occurred during the time of data collection, the `Return Date` dataset. Of these locations, 119 have both returnees and IDPs. In the overall REACH data it is also true that most locations with IDPs also have returnees (26% of locations have IDPs and no return). Table 1 shows the size of the datasets and the percentage of messages within that dataset that are from areas with returnees and IDPs. For example, in the main `Location Mentions` dataset there are 2,360,559 messages of which 1,264,871 are from Telegram. Of the total messages, 16% (370,345 messages) are from areas with only return, 5% are from areas with only IDPs, 62% are from areas with neither and 17% are from areas with both. Of

> 5To truncate the dataset to Arabic, we first obtained the language of a message using _langid_ (Lui and Baldwin (2012)), an off the shelf tool for detecting language from text. The tool is based on pre-trained machine learning models and can detect over 90 languages. Before beginning data analysis, we performed basic cleaning on the text data using Nielsen’s stemmer (Nielsen (2017)), but prevented the stemming of proper nouns like key locations and political figures. Next, we removed stopwords using a base list of Arabic stop words and some words in Syrian dialect (my brother, where, why, how, etc.), and the words ‘channel’, ‘subscribe’, ‘Telegram’ and ‘Twitter’. The base list of Arabic stop words is from Mohatahar Arabic Stopword, _Github_ , `https://github.com/mohataher/arabic-stop-words/blob/master/list.txt`

> 6 `http://www.geonames.org/`

> 7We removed any locations which had the same names as governorates to improve granularity. A few top locations which were disproportionately represented due to false positive matches, ‘mil’, ‘san’, and ‘ada’, were removed.

10
Table 1: The fraction of messages from return and IDP areas by source for each dataset.

||`Location `|`Mention`|`s`|||`Return `|`Date`|
|---|---|---|---|---|---|---|---|
||Total|% IDP|% Returnee|% Neither|% Both|Total|%Pre|
|Total|2,360,559|5%|16%|62%|17%|769,745|53%|
|Telegram|1,264,871|5%|16%|64%|15%|391,356|57%|
|Facebook|676,580|4%|15%|60%|21%|241,964|46%|
|Twitter|419,108|8%|15%|58%|19%|136,425|56%|

the messages from locations where return occurred during the time of data collection 53% (411,694 messages) are from pre-return and 47% are from areas post-return.

We also compiled a set of groups which mention location information in the name of the group or group description (the `Researcher Coded` dataset). Such groups include @National.Defense.in.maharda on Facebook, @newsmanbij on Twitter and @saraqib2017n on Telegram. We use this smaller set of 173,906 messages as a robustness check for our results since it includes information we may be missing by using messages with location mentions. For example, in sales messages individuals might not include their location if they are already posting in a group where the location is apparent, and thus such messages would not appear in the `Location Mentions` and `Return Date` datasets but would in the `Researcher Coded` dataset. For the image analysis we used all images posted in the `Researcher Coded` dataset groups, a final image set of 24,304 images. Of these images, 921 are from areas with both IDPs and returnees, 9,001 are from areas with only returnees, and 14,382 are from areas with neither. None of the `Researcher Coded` groups are from areas with only IDPs.

To better understand displacement and return flows rather than solely IDP settlement we incorporate monthly data from OCHA which is compiled from a variety of humanitarian actors following methodology agreed to by an inter-agency IDP task force (OCHA (2021)). This data contains information on displacement out of and return to communities starting in January 2019. Though this dataset, as discussed above, is somewhat smaller than the REACH dataset– especially in terms of return– it has increased granularity regarding the size of return and displacement movements. We associated the data with location by postal code for the seeded return and displacement datasets resulting in a displacement percentage dataset of 41,434 messages from 316 locations and a return percentage dataset of 62,434 messages from 184 locations.

# **4.3 Methods**

Our primary analysis uses unsupervised tools for text and image analysis. For text analysis, we use structural topic modeling (Roberts et al. (2014)) and seeded topic models using words from word2vec (Mikolov et al. (2013)). For image analysis, we use feature extraction and clustering to obtain ‘visual topics.’ Structural topic models identify topics in text data, assigning a probability of belonging in a topic to each word. We run two unseeded models: the `Location Mentions` model with a four-way variable identifying locations as ‘IDP only,’ ‘Returnee Only,’ ‘Both,’ and ‘Neither’ as covariates for comparability and to be able to explore heterogeneity, and the `Return Date` model with a dichotomous return variable for pre- and post- return. The results below display our analysis on models with 20 topics, chosen to maximize topic coherence both mathematically and through observation of models with different topic numbers (Mimno et al. (2011)). For all structural topic model analysis, expert annotators labelled each topic by looking at the 30 most salient keywords in that topic both in terms of frequency “F” and frequency and exclusivity “FREX”. Throughout our results, we present the English translations of the topic labels.

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For the image analysis we use ResNet-50 (He et al. (2016)), a pre-trained convolutional neural network model trained on the ImageNet dataset (Deng et al. (2009)) to extract image features and then cluster them using k-means clustering. Our final analysis uses 30 clusters.

The mixed effects models include results from ‘seeded’ topic models obtained by using a subset of messages filtered for keywords about services, local governance, or displacement. The goal of these seeded models is to pull out additional insights which may be obscured in the `Location Mentions` and `Return Date` models. For seeded analysis, we worked with a Syrian researcher who provided an initial list of words focused on services, local governance, return, and displacement. We then used word2vec (Mikolov et al. (2013)) with the Continuous Bag of Words (CBOW) embeddings to find the 20 most contextually similar terms for each keyword.<sup>8</sup> The resulting list, in Appendix 9.3, was manually filtered for relevance and the expanded keyword list was used to collect all messages that included any of the words. The seeded return model includes 672 locations with 477,581 messages from areas without return and 419,090 messages from areas with return, and the seeded displacement model includes 583 locations with 55,737 messages from areas with IDPs and 125,158 messages from areas without. Both models were run with 15 topics.

# **4.4 Mixed Effects Modeling**

For the `Return Date` model as well as the seeded return model we run a mixed effects model to identify changes in topic prevalence pre- and post- return while both enabling correlation between topics and accounting for location characteristics and time shocks which occur across all messages. Similarly, for the seeded displacement and seeded return models we run mixed effects models to identify the association between population displacement or return and percentage of topic discussion. We model the outcome percent _Yij_ as a function of return in the previous month, with varying intercepts based on subdistrict, month, and topic. We model this as: In our specification we have _Xij_ as either a dichotomous variable indicating whether there is return in location _i_ in month _j_ or a continuous variable indicating the percent of the 2004 population displaced or returned to location _i_ in month _j_ . _Zij_ is a matrix including the groupspecific intercept, which takes into account the topic, the subdistrict of location _i_ , and the month _j_ , as well as the values for return, which can vary by topic.<sup>9</sup>

# **4.5 Causal Mediation Analysis**

Drawing on the importance of information access in return decisions (Alrababa’h et al. (2020)), we explore whether discussions on social media have a causal effect on return. We hypothesize that a high level of violence in a location in a given month affects whether people will return in the following month, with more robust access to information as the causal mechanism.

> 8A note on CBOW word embeddings: for a given keyword, the objective of a word embedding is to find a high dimensional representation such that similar words are placed close by in the high dimensional space. Hence, two words which are used in a similar context will be similar in the word embedding space. We also experimented with other word embedding techniques such as FastText (Bojanowski et al. (2016)), which led to less diversity within the list of relevant terms. We ran trials using stemmed and tokenized words, and ultimately used tokenized un-stemmed text with bigrams. We kept stop words for context.

> 9We chose this model based upon the hierarchical structure of our data, which naturally had time and location groups. However, the time and location effects did not end up affecting results, either alone or combined, in any of the cases, indicating that the primary variation is between topic groups as well as the relationship between topic groups and return.

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Data is grouped by month (j = 20) and subdistrict (i = 52). Not all subdistricts are represented in all months, but all are represented in at least two months.<sup>11</sup> There are 19 districts represented and the average number of months of data per subdistrict is just under 10 with a median of 9. To model the mediation effects we use the mediation package (Tingley et al. (2014)). This technique fits mediator and outcome models, then generates mediator predictors under control and treatment conditions. The outcome model is used to make predictions and calculate the quantities of interest outlined below. The outcome is a binary indicator of whether there is or is not return in a given month: Here the outcome Y is return from the OCHA data, the treatment T is violent events in the preceding month from the ACLED data, the mediator M is discussion of violence in the previous month on social media, and the covariates _Xi_ are log population, agriculture and district. In addition, we include an interaction term between the mediator and the treatment. We would expect the treatment to interact with the mediator in that a complete absence of violent events in a location would make it unlikely that there is any news about such events. The mediator is a continuous variable so we use a linear model for the mediator model with the same variables as above: In our outcome we have indirect or ‘causal mediation’ effects, ACME ( _δt_ ), the effect of changing the mediator status on the outcome given the same treatment (Imai et al. (2011)). In addition to this, we have the Average Direct Effect (ADE, _ξi_ ), the effect of changing the treatment status while maintaining the same mediator value, and the overall Average Treatment Effect (ATE).<sup>12</sup>

# **5 Results**

In this section, we present our findings on differences in content sharing in return and non-return areas and areas with and without IDPs. In Section 5.1, we assess differences in the prevalence of text topics and image clusters.<sup>13</sup> We add a time element by running a mixed effects model on topic prevalence pre- and post- return to determine if return coincides with changes in discussion in Section 5.2. In this section we also consider quantity of displacement from and return to a region and how this is associated with discourse. Finally, we consider the role which social media information may play in return decisions using mediation analysis.

Overall, we see that areas with return discuss local governance, job opportunities, and non-operational war issues more and differences are statistically significant at the 99% level accounting for Bonferroni correction for multiple hypothesis testing.<sup>14</sup> In the `Location Mentions` dataset in Figure 3, the topic related to the economy is nearly 75% more prevalent in areas with both returnees and IDPs as compared to those with neither (+0.011 points) and the one related

> 11Higher month cutoffs lead to similar results. Two was chosen as the cutoff to minimize loss of data while keeping the violent events quantile measure meaningful.

> 12When including interaction terms, the mediation package creates confidence intervals using QuasiBayesian estimation and robust standard errors (Imai et al. (2011)).

> 13 `Researcher Coded` analysis is in Appendix 9.1.

> 14The 99% significance level applies for all results discussed in the remainder of this section using Bonferroni correction for multiple hypothesis testing unless otherwise noted.

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to goods, numbers and services is slightly over 25% more prevalent (+0.008).<sup>15</sup> Those related to politics are also more prevalent, between 16 and 100 percent (+.004 to .05). War related violence topics, especially those related to the regime, are less prevalent in return areas (-.01 to .05 points). Images also support these conclusions, with images shared in accounts from areas with only returnees between 79 and 700% more likely to be goods for sale including cars, motorcycles and other items and images from areas with neither returnees nor IDPs more likely to be tanks (+.026, 113% compared to Both) or groups of militants (+.022, 49%) as seen in Figure 6.

Adding additional granularity by considering changes pre- and post- return, we use mixed effects models to find that topics which are more represented pre-return include goods and services related topics in both the `Return Date` and seeded return model (+4% and +1.3% respectively), Figure 7. In addition, violence related topics including air strike topics are less represented both post-return and when there is less return in a given location (-1.8% `Return Date` , -3.8% seeded return with return percentage), Figures 7 and 8. Increased displacement, on the other hand, is associated with violence topics including anti-ISIS campaign and air strikes (+0.33 and +0.38 respectively), Figure 8.

In general, the clearest signal from social media is on violence-related issues, suggesting that returnees and IDPs can easily look to public groups and pages for information about ongoing conflict to inform return choices. Unsurprisingly, security considerations come first. Building on this insight, we use causal mediation analysis to reveal that information sharing on social media mediates the relationship between violent events in an area and return to that area, Figure 9.

# **5.1 Content in Return and non-Return Areas**

We first consider a model run on the `Location Mentions` dataset. This model was run with a ‘four way’ covariate including information about returnees and IDPs. The covariate equaled 1 for areas with both, 2 for areas with returnees and without IDPs, 3 for areas with IDPs and without returnees, and 4 for areas with neither. For a wholistic analysis, we considered prevalence of image clusters given the same ‘four way’ covariate. Images are from the `Researcher Coded` groups, in which no locations with only IDPs are represented. The `Location Mentions` STM model is dominated by violence related topics, motivating the use of the models discussed later in this section to attain additional insights. The most common words for each topic are in Figure 4, and the Arabic versions are available in Appendix 8.2. For the image analysis, sample clusters can be seen in Figure 5.

As shown in Figure 3 discussion around organized war violence, such as “Regime military action, Air strike”, is more represented in areas without return and without IDPs, with an increase of 0.045 between neither and both (52% more prevalent). Other violence related topics such as “Air strike warning” (0.05, 29%), “Liberation army, Regime military” (0.029, 49%), and “anti-ISIS campaign” (0.01, 33%) are also more prevalent in areas without returnees or IDPs. This result supports research indicating that individuals are unlikely to want to return to areas with ongoing violence and military campaigns, especially if they were exposed to violence prior to leaving (Ghosn et al. (2021)). Image sharing aligns with the message outcomes, with the two clearly violence related topics ‘Militants, army’ (+.026, +113% compared to Both) and ‘Construction vehicles, tanks’ (+.022, 49% compared to Both) more represented in areas without returnees or IDPs as can be seen in Figure 6.

> 15For the rest of the paper percents are calculated as the percent difference between the topic percentage value of the lower covariate and the topic percentage point value of the upper covariate. i.e. a topic with mean percent 0.05 in an area without returnees and 0.1 in an area with returnees would be noted as a 100% increase in areas with returnees.

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||0<br>0.05<br>0.1|0.15<br>0.2<br>0.25|
|---|---|---|
|**Air strike warning**<br>|||
|**Regime military action, Air strike**|||
|**Foreign intervention**<br>|||
|**Children, civilians**<br>|||
|**Liberation army, Regime military**<br>|||
|**Religion, texts**|||
|**Air strikes, damage, civilians**|||
|**War news, war reporting**<br>|||
|**Description, names**<br>|||
|**Aleppo news**|||
|**ME politics, Assad, Lebanon**|||
|**Crimes, investigations**|||
|**anti−ISIS campaign**|||
|**News reports**|||
|**Goods, coronavirus, numbers**|||
|**Regime military actions**<br>|||
|**Idlib, roads, governance**<br>|||
|**Economy, weather**|||
|**Afrin, Turkish−Kurdish politics**<br>|||
|**Horoscopes, description**|||
|Return Only|||
|<br>IDPs Only|0<br>0.05<br>0.1|0.15<br>0.2<br>0.25|
|Neither<br>Both|Estimated|topic proportion|

Figure 3: Topic prevalence based on presence of returnees and IDPs, `Location Mentions` dataset.

The prevalence of violence- related discussion overall in the `Location Mentions` models, accounting for 7 of the 20 topics in Figure 4, outlines the persistent nature of violence in the daily lives of Syrians. Social media, especially public groups and channels, serve the population through announcements documenting daily violence and military movements which are ultimately useful for both those currently living in Syria and those choosing whether or not to return. Discussion in areas with only IDPs is often more similar to discussion in areas with both returnees and IDPs than to discussion in areas with neither. Though images are often used alongside, or in the place of, text to convey a message, explicitly violence related topics are less represented in the image analysis than in the text. This may be the result of filtering choices by the different platforms to remove violent or disturbing imagery.<sup>16</sup> For most violence related topics, discussion in areas with only IDPs is more similar to discussion in areas with both returnees and IDPs, whereas discussion in areas with just returnees is more similar to discussion in areas with neither returnees nor IDPs. Individuals fleeing violence often have little choice in their destination (Bank (2019)), but if fleeing violence are unlikely to remain in areas where it is prevalent. Returnees, on the other hand, are returning in part because of ties to their home town (Ghosn et al. (2021)) and thus may be willing to endure more uncertainty to be there. In fact, a UNHCR monitoring survey of voluntary returnees found that 54% returned to Syria to reunite with their families (Hoffman and Makovsky (2021)). The central role that violence plays also underscores the importance of using multiple platforms. As can be seen in Appendix 9, the majority of violence related content is over represented on Telegram, while much of the economic and job related content is over represented on Facebook. By using a variety of data sources we are getting a richer image of the situation on the ground.

> 16Twitter transparency site: `https://transparency.twitter.com/` and Facebook Community Standards: `https://transparency.fb.com/policies/community-standards/`

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|**1.**<br>**Regime military**<br>**action, Air strike**|**237,592**<br>**messages**|F: rural, Idlib, side, cannon, rural_Idlib, Rikh, shelling<br>FREX: bombardment, countryside_Idlib, Idlib, artillery shelling, shot_war, hit_cannon, shot_rikh|
|---|---|---|
|**2.**<br>**Description,**<br>**names**|**41,649**<br>**messages**|F: Muhammad, Hamad, Sheikh, Hassan, Ahmed, good, girl<br>FREX: I know, Master, I saw, Thank God, I said, praised_praised, Hadal|
|**3.**<br>**Idlib, roads,**<br>**governance**|**74,131**<br>**messages**|F: City, camp, cities, road, Idlib, Idlib, bridge<br>FREX: Boil, Kfar Takh, Kfar Takh_Rima, Mahal_City, Idlib_City, City_Sarmed, place_religion|
|**4.**<br>**anti-ISIS**<br>**campaign**|**230,185**<br>**messages**|F: Monastery, Sri, East, Dimkar, SDF, Sri_Dimkar, Raqqa<br>FREX: Brif_Deir, Ain_Issa, rural_Raqqa, rural_Hasakah, Lazar_East, Raqqa_Sri, Demqar_arrested|
|**5.**<br>**Air strike**<br>**warning**|**120,025**<br>**messages**|F: Accurate, reach, possible, accurate, possible_reach, fying, war<br>FREX: Accurate, accurate, possible_reach, fying, accurate_accurate, war_fying, sanctity|
|**6.**<br>**Aleppo news**|**24,223**<br>**messages**|F: Aleppo, countryside, west, rural Aleppo, leave, countryside of Aleppo, points<br>FREX: Rural_Aleppo, Aleppo_west, city_of_Aleppo, Aleppo_side, friend_friend, Aleppo_road, Tarnab|
|**7.**<br>**Liberation army,**<br>**Regime military**|**40,165**<br>**messages**|F: Army, Mujahid, Liberation, Erh, Idlib, walking, armed<br>FREX: Mahar_Brief, Telegram, shutter_telegram, telegram_bridge, army_controlling, walk_er, er_complex|
|**8.**<br>**News reports**|**145,970**<br>**messages**|F: Confdential, sketch, corridor, class, confdential, across report, NA<br>FREX: attachment_report, inherit, inherit_press, rent, shutter_telegram, ether, sight_attachment|
|**9.**<br>**War news,**<br>**war reporting**|**61,719**<br>**messages**|F: War, east, military, Damascus, army, Erh, side<br>FREX: Eastern, war_centered, eastern_Aust, eastern_Marj, east_bombing, east_rear, conscience_askar|
|**10.**<br>**Economy, weather**|**269,595**<br>**messages**|F: Stair, price, stoop, lower, east, side, west<br>FREX: rate, precipitation, light, gasoline, partial, rise, wet|
|**11.**<br>**Afrin, Turkish-Kurdish**<br>**politics**|**87,141**<br>**messages**|F: Afrin, occupy, hero, mercenary, debtor, witness, Reikh<br>FREX: mercenaries, hero_honor, occupied_mercenaries, Rech_martyrdom, Team_hamza, mercenary_occupation, martyrdom_hero|
|**12.**<br>**Foreign intervention**|**128,413**<br>**messages**|F: undercover, America, anchor, confdential, let, united, saddle<br>FREX: please, Washington, Jin_constitution, ok_please, Tayyib_Ardagh, Rais_Rajab, United_Rick|
|**13.**<br>**ME politics,**<br>**Assad, Lebanon**|**87,026**<br>**messages**|F: people, chief, company, Arabs, Lebanon, party, council<br>FREX: Mr. President, People's Assembly, Palestinian, Ba'ath_Arab, Netni, of the Ba'ath Party, Arab_Shter|
|**14.**<br>**Horoscopes,**<br>**description**|**93,816**<br>**messages**|F: Pregnancy, new, good, pressure, partner, horoscope, cancer<br>FREX: cancer, pregnancy_neese, your time, neez_pressure, pressure_carry, can be, NA|
|**15.**<br>**Air strikes,**<br>**damage, civilians**|**125,279**<br>**messages**|F: Bird, war, Idlib, bombing, bird_war, explosive, fun<br>FREX: explosive, explosive, merry_plane, barrel, wounded man, Idlib summary, barrel bomb|
|**16.**<br>**Goods, coronavirus,**<br>**numbers**|**167,692**<br>**messages**|F: Corn, numeral, manager, company, servant, new, reward<br>FREX: press_cren, new_log, send, new_press, send_cren, kern_rising, sterilization|
|**17.**<br>**Regime military actions**|**74,977**<br>**messages**|F: Assad, countryside, window, ower, cannon, do shield, NA<br>FREX: to fall, Fee_Shield, Pearl_Tiger, Fall_Of, Bomb_Mine, Exhaust_Trade, Land_Mine|
|**18.**<br>**Religion,**<br>**texts**|**219,306**<br>**messages**|F: same, much, all, debt, less, earth, wealth<br>FREX: I want, make, you turn, you were, dict, let down, location|
|**19.**<br>**Children,**<br>**civilians**|**79,470**<br>**messages**|F: child, civilian, bombardment, circle, hospital, witness, city<br>FREX: Ratif_Haseel, child_Mohammed, patient_hospital, city_Arab, transfer_hospital, Kafr Batn, hospital_hospital|
|**20.**<br>**Crimes,**<br>**investigations**<br>Figure 4: `Locat`|**45,349**<br>**messages**<br>`ion Men`|F: condition, kill, person, carry, poison, investigation, prison<br>FREX: betrayed, crime_murder, confess, grimm, thousand_breaths, disappear, rape<br>`tions` Topics with four way covariate. “F” indicates words that are|

Figure 4: `Location Mentions` Topics with four way covariate. “F” indicates words that are most frequent in each topic. “FREX” indicates words that are both frequent in and exclusive to each topic. Message numbers are the total number of messages from all sources combined for that topic. All results are translated from Arabic. Arabic version in Appendix 8.2 Topics in the `Location Mentions` model which are more prevalent in areas with returnees and IDPs (Figure 3) are largely related to foreign affairs, governance, politics, and the economy such as “Foreign intervention” (both +0.05, 100%), “ME politics, Assad, Lebanon” (return +0.006, 17%), “Idlib, roads, governance” (both +0.004, 16%), “Economy, weather” (both +0.011, 73%) and “Goods, coronavirus, numbers” (both +0.008, 28%). Discussion of religion is also more prevalent in areas with return, suggesting that results from Bove et al. (2021) are also upheld in Syria. For the economy and goods topics, unlike the violence topics, areas with only returnees are more similar to areas with both returnees and IDPs than areas with only IDPs are. The prevalence of economic discussion in these areas supports research from Camarena and Hägerdal (2020) on the salience of economic considerations in driving longer term return. In addition to text discussion, areas with only returnees have more images of items, which seem to be goods for sale (+0.06, +188% compared to neither), cars (+0.019, +79%), and motorcycles (+0.021, +700%) as seen in Figure 6. This is similar to the buzz driving displacement in Iraq where the key drivers are politics, insecurities and infrastructure (Singh et al. (2020)). Overall, factors for return in Syria appear to be similar to those for displacement, but in reverse. The higher prevalence of content related to local governance and goods provision in return and IDP areas, contrasted with the higher prevalence of violent content in non-return and non-IDP areas, indicates that these are important pillars for establishing a healthy community which individuals feel safe returning to. For robustness, we also note that these results are reflected in the `Researcher Coded` models in Appendix 9.1.

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<!-- Start of picture text --> Estimated Average Effect of Return<br>on Percent Discussion by Topic<br>Turkish-Kurdish politics —+'<br>Horoscope, description ———_ \<br>Air strike. Idlib —— i<br>Politics news |<br>Military control |<br>Camps, Days,displacement,weather —1.<br>‘elgion {<br>Vv ME politics, Turkey —<br>3 Medicine and health ——+<br>e Office, government i<br>Air strike warning es a<br>Regime military action —T<br>Crimes, children —-—<br>hospitals, : falib, —-—_ 1<br>‘Aleppo news 7<br>Names, martyrs —+<br>Shops, economy '<br>Air strike, civilian i<br>Insurgency, anti-ISISwitness,campaign casualty 11 _————<br>-0.04 -0.02 0.00 0.02 0.04 0.06<br>Percent Discussion<br><!-- End of picture text -->

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<!-- Start of picture text --> Estimated Average Effect of Return Percent on<br>Percent Discussion by Topic, Seeded Return Model<br>7<br>Military, camps, prison<br>Schools, shops, services 1+<br>R lit ti t<br>‘egimeForeignmilitaryintervention,action i1<br>Turkish-Kurdish politics \<br>Description, Health T<br>1<br>Air strike,Names,civilianmartyrsinjuiyay ——tT1<br>xv2a Airi strike, Idlib, Assad ——_——_!1{<br>Weather :<br>Liberation army, i]<br>campaigns i<br>Government, regime 1<br>anti-ISIS campaign i<br>Religion, land ownership 1T<br>Prices, goods, horoscope '1 rs<br>Crimes, investigations, i<br>hospital 1<br>0.0 0.1<br>Percent Discussion<br><!-- End of picture text --> <!-- Start of picture text --> Estimated Average Effect of Displacement<br>Percentage on Percent Discussion by Topic,<br>Seeded Displacement Model<br>'<br>ME politics +'<br>Names, martyrs 1<br>anti-ISIS campaign,camps 11 —$<—+____.<br>Hospitals, Afrin !<br>1<br>Religion, poverty 7<br>2oO3 Government,Aleppo news,WeatherofficesIEDs 1vit'i<br>F | . |<br>Liberation army, campaigns 1<br>Air strike, civilian casualty i<br>Cities, roads — '<br>Air strike, Idlib . o—_———____<br>United Nations, the regime 1t<br>Crimes, prison '<br>Air strike, |<br>regime military actions 1<br>-0.004 0.000 0.004<br>Percent Discussion<br><!-- End of picture text -->
<!-- Start of picture text --> Effect of Mediator on Violence - Return Relationship<br>wo<br>2<br>oO<br>So<br>: ¢<br>a®—2w9° ’<br>=<br>Ww<br>se<br>ai 9<br>2 * Control<br>? ¢ Treatment<br>© Overall<br>oa<br>N<br>°<br>ACME ADE ATE<br>Effect Type<br><!-- End of picture text -->
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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
ran exploratory analysis with polyglot, which categorizes each word as positive or negative, but averaging the results of such word counts can lead to in-cohesive results since the sentiment of a sentence may not be captured by the positive or negative nature of its individual words as seen in Appendix 9.4 (Chen and Skiena (2014)). Future research may train such a tool on conflict related social media messages using expert annotators to verify tone. This could be used to study potential conflicts or differences in opinions between returnees and the population already living in an area in order to understand the assimilation of returnees or IDPs in a location.

Finally, researchers may wish to explore the sentiment towards refugees in neighboring countries such as Turkey and Lebanon and how social media discussion responds to increased migration. Such work could be augmented with satellite data and other mobility related data from google maps to identify differences in locations where returnees settle and where settlements already exist.

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