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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