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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