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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