Informants (KIs) interviews (IOM, 2016).<sup>16</sup> While information is not directly collected from migrants, DTM data have been collected repeatedly and cross-checked since 2017 and have been used in several official reports. These data are considered the best source of information—in terms of accuracy and coverage—available at the moment on migration flows in Libya.

Figure 1 shows the physical map of Libya and the location of the 167 FMPs used in our analysis. The FMPs are located across all of Libya. Importantly, there are FMPs in both the areas controlled by the (Tripoli-based) Government of National Accord and by the (Tobrukbased) House of Representatives (see footnote 10). There are FMPs at all the entry points to Libya, at all the important crossroads, close to the largest cities (e.g., Tripoli, Misurata, Banghazi), and in both the desert areas (South) and the coastal areas (North).

Most migrants located in the FMPs in Libya come from sub-Saharan African countries. During the period under analysis, the most common origin countries are: Niger, the Arab Republic of Egypt, Chad, Sudan, and Nigeria. The destination countries for migrants in Libya are mostly European ones. Yet, some migrants want to remain in Libya or to return home. In our data, the list of most common destination countries includes: Italy, France, Germany, Libya, and Niger.

# **4.2 News articles data**

Data on news articles are retrieved from the Global Database of Events, Language, and Tone (GDELT) ( `https://www.gdeltproject.org/` ). GDELT is an open-source news database built combining advanced natural language and data mining techniques to monitor news at the global level in real time. The project gathers meta-data from online news sources globally (every 15 minutes) and translates it into English from more than 65 languages In our analysis, we use GDELT 2.0 Global Knowledge Graph (GKG), the most comprehensive version of the GDELT dataset. GDELT identifies for each news article its source, its nationality, and its main topics.<sup>17</sup> We define a news article to be migration-related if one of its top 3 topics is one of the following: migration, refugees, borders, displacement. GDELT thus allows us to identify all the news articles related to migration for the period 2017-2020 for any of the destination countries of the migrants located in all the FMPs in Libya.

An important feature of GDELT is that it provides a numerical variable measuring the tone of each news article. The tone is calculated as the positive score minus the negative score, where the positive (negative) score is the share of all words in the article found to have a positive (negative) connotation. In practice, a news article has a positive (negative) tone score if the share of words with a positive connotation is larger (smaller) than that with a negative connotation. This tone variable has been shown to perform similarly to human-coded sentiment

> 16A Key Informant (KI) is a “person within the community who, due to his or her position, has access to specialized knowledge about the situation and context of the populations residing within. KIs can be local officials, religious, community, or tribal leaders, government administrators, local humanitarian workers, representatives of displaced groups, or others.” As an example, during Round 18 (February-March 2018), 1360 KIs interviews were conducted, in all 100 Baladiyas (i.e., districts) of Libya (IOM, 2018a).

> 17The GDELT algorithm extracts from the content of the news the major topics discussed, categorize them into topics using the taxonomy provided by the World Bank Topical Ontology, and then rank topics in order of importance, so to make it possible to identify the primary focus of each news article. For more details, see `https://blog.gdeltproject.org/mapping-the-media-a-geographic-lookup-of-gdelts-sources/`

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