Table 1
Frequency ranking of the 10 most frequent migr-lexicon items in the FR-MIGR-TWIT corpus.
| MIGR-LEXICON | ENGLISH TRANSLATION | NUMBER OF OCCURRENCES | FREQUENCY (%) |
|---|---|---|---|
| immigration | immigration | 7,954 | 39.81 |
| migrant | migrant | 6,642 | 33.25 |
| migratoire | migratory | 2,857 | 14.30 |
| immigré | immigrant (noun or adjective) | 676 | 3.38 |
| migration | migration | 608 | 3.04 |
| #pjlasileimmigration | #pjtheasylumimmigration | 201 | 1.01 |
| #loiasileimmigration | #lawasylumimmigration | 138 | .69 |
| #débatimmigration | #debateimmigration | 81 | .41 |
| immigrationniste | immigrationnist | 78 | .39 |
| anti-migrant | anti-migrant | 74 | .37 |
| TOTAL | 19,309 | 96.65 |
Table 2
Twitter/X accounts included in the FR-R-MIGR-TWIT subcorpus.
| POLITICAL ACTOR OR PARTY | TWITTER/X ACCOUNT | REPRESENTATIVE TYPE | PERCENTAGE OF (RE)POSTS CONTAINING MIGR-LEXICON |
|---|---|---|---|
| Christian Estrosi | @cestrosi | Individual | 3.27 |
| Emmanuel Macron | @EmmanuelMacron | Individual | .71 |
| Éric Ciotti | @Eciotti | Individual | .40 |
| Éric Zemmour | @ZemmourEric | Individual | 4.06 |
| Florian Philippot | @f_philippot | Individual | 2.04 |
| Jordan Bardella | @J_Bardella | Individual | 6.17 |
| Marine Le Pen | @MLP_officiel | Individual | 6.27 |
| Marion Maréchal | @MarionMarechal | Individual | 5.76 |
| Michel Barnier | @MichelBarnier | Individual | 1.68 |
| Nicolas Bay | @NicolasBay_ | Individual | 10.68 |
| Nicolas Dupont-Aignan | @dupontaignan | Individual | 2.73 |
| Philippe Meunier | @Meunier_Ph | Individual | 2.59 |
| Rassemblement National | @RNational_off | Organization | 8.13 |
| Valérie Boyer | @valerieboyer13 | Individual | 1.84 |
| Valérie Pécresse | @vpecresse | Individual | 1.12 |
| Xavier Bertrand | @xavierbertrand | Individual | .79 |
Table 3
Twitter/X accounts included in the FR-L-MIGR-TWIT subcorpus.
| POLITICAL ACTOR OR PARTY | TWITTER/X ACCOUNT | REPRESENTATIVE TYPE | PERCENTAGE OF (RE)POSTS CONTAINING MIGR-LEXICON |
|---|---|---|---|
| Adrien Quatennens | @Aquatennens | Individual | 2.25 |
| Alexis Corbière | @alexiscorbiere | Individual | 1.23 |
| Anne Hidalgo | @Anne_Hidalgo | Individual | 2.45 |
| Arnaud Montebourg | @montebourg | Individual | .14 |
| Benoît Hamon | @benoithamon | Individual | 1.85 |
| Christiane Taubira | @ChTaubira | Individual | .32 |
| Clémentine Autain | @Clem_Autain | Individual | 1.53 |
| Danièle Obono | @Deputee_Obono | Individual | 4.86 |
| Esther Benbassa | @EstherBenbassa | Individual | 5.81 |
| Europe Ecologie-Les Verts | @EELV | Organization | 5.05 |
| François Hollande | @fhollande | Individual | .41 |
| François Ruffin | @francois_ruffin | Individual | .33 |
| Gauche Républicaine et Socialiste | @gauche_rs | Organization | .52 |
| Génération.s | @generationsmvt | Organization | 5.95 |
| Jean-Luc Mélenchon | @jlmelenchon | Individual | .69 |
| La France Insoumise | @franceinsoumise | Organization | 2.13 |
| Manon Aubry | @manonaubryfr | Individual | 1.27 |
| Nathalie Arthaud | @n_arthaud | Organization | 5.78 |
| Parti Radical de Gauche | @partiradicalg | Organization | .38 |
| Parti Socialiste | @partisocialiste | Organization | .79 |
| Philippe Poutou | @philippepoutou | Individual | 1.43 |
| Raphael Glucksmann | @rglucks1 | Individual | 2.45 |
| Yannick Jadot | @yjadot | Individual | 2.28 |
Table 4
Annotation dimensions, analytical purposes, and labels.
| ANNOTATION DIMENSION | ANALYTICAL PURPOSE |
|---|---|
| Lexical identification | Identifies the surface form of the annotated migr- occurrence |
| Lemmatization | Normalizes inflected or surface forms under a shared lexical entry |
| Syntactic function | Captures the syntactic function of the annotated occurrence. The inventory includes core syntactic labels such as adjunct (AD), dependent (DEP), direct object (OBJ), oblique object (OBL), predicate (PRED), root (ROOT), syntactic subject (SUB) and verb (VERB), as well as platform-specific labels such as tweet-final and tweet-initial hashtags or mentions (tw.final.hashtag; tw.final.mention; tw.initial.hashtag; tw.initial.mention) and non-hyperlinked tweet-initial positions (tw.initial.nonhypertext). |
| Semantic role | Captures the semantic role assigned to the annotated occurrence. The inventory includes traditional labels such as Agent, Content, Experiencer, Force, Goal, Instrument, Result, Source, and Theme, as well as adapted labels including Beneficiary (Theme.Beneficiary.neg) and Maleficiary (Theme.Beneficiary.pos). It also includes genre-specific labels corresponding to discursive and semiotic functions specific to political tweets, such as Content, Speaker, and discursive topic (Topic), and Place (including subtypes such as Place.Media and Place.PoliticalEvent). |
| Modification | Captures whether the annotated item modifies another noun or is itself modified, and records the lexical identity of the modifier or non-migr- head noun |
| List and parallelism structures | Captures whether the annotated item appears in list or parallelism structures, and records their extent through the number of conjuncts to the left and/or right of the migr- occurrence |
| Tweet metadata | Links each annotation to contextual tweet-level information (date, username, retweet origin) |
Table 5
Query terms used for migr-tweet extraction.
| KEYWORDS | ENGLISH TRANSLATION |
|---|---|
| immigration, immigrations | immigration, immigrations |
| migrant, migrants | migrant, migrants |
| immigré, immigrés, immigrée, immigrées, immigrant, immigrants, immigrante, immigrantes | immigrant |
| migr | migr- string (broad retrieval key) |
| migration, migrations | migration, migrations |
| migratoire, migratoires | migratory |
Table 6
Metadata settings for tweet extraction.
| TWEETS.FIELDS |
|---|
| author_id; context_annotations; conversation_id; created_at; entities; geo; id; in_reply_to_user_id; lang; possibly_sensitive; public_metrics; referenced_tweets; reply_settings; source; text; withheld |
Table 7
Annotation dimensions implemented in ANALEC: field names of the annotation unit MIGR-LEXICON.
| ANALYTICAL DIMENSION | ANNOTATION FIELD NAME(S) ASSOCIATED WITH THE ANNOTATION UNIT MIGR-LEXICON IN ANALEC |
|---|---|
| Lexical identification | #forme# |
| Lemmatization | lemma |
| Syntactic function | func_syn |
| Semantic role | role_sem |
| Modification | modification, lemma_modif_l1…/lemma_noun-1 |
| List and parallelism structures | list_par, rel-list_par, length-1, #forme#_migr-list_par |
| Tweet metadata | rt, username, date |
Figure 1
Annotation structures in ANALEC.
Figure 2
Annotation unit MIGR-LEXICON-9075 in the ANALEC interface.
Figure 3
XML encoding of annotation unit MIGR-LEXICON-9075.
Table 8
Inter-annotator agreement results: exact agreement and Cohen’s kappa.
| EXACT AGREEMENT | KAPPA | |
|---|---|---|
| LEMMA | 1.0 | 1.0 |
| #forme# | .932 | .927 |
| ROLE_SEM | .729 | .606 |
| FUNC_SYN | .822 | .671 |
| LIST/PAR | .904 | .788 |
| modification | .957 | .925 |
| LEMMA_MODIF_L1 | .965 | .656 |
| LEMMA_MODIF_R1 | .951 | .846 |
| LEMMA_MODIF_R2 | .986 | .716 |
| LEMMA_MODIF-R3 | 1.0 | |
| LEMMA_MODIF-R4 | 1.0 | |
| LEMMA_MODIF-R5 | 1.0 | |
| LEMMA_NOUN-1 | .938 | .819 |
Table 9
Classification of lexical items associated with migr-lexicon items in lists and parallelisms.
| CATEGORY CODE | LEMMA_CONJUNCT | ENGLISH TRANSLATION |
|---|---|---|
| Security_judicial | insécurité, terrorisme, terroriste, islamisme, criminel, criminalité, délinquant, délinquance, clandestin, trafic, trafiquant, justice, police, violence | insecurity, terrorism, terrorist, Islamism, criminal, criminality, delinquent, delinquency, clandestine migrant(s), trafficking, trafficker, justice, police, violence |
| Economic_institutional | csg, retraite, impôt, impot, fiscalité, fraude, fraude sociale, pouvoir d’achat, concurrence, concurrence déloyale, traité de libre-échange, schengen, union européenne, europe, bruxelles, état, institution | CSG (general social contribution), retirement/pension, tax, taxation, fraud, welfare fraud/social fraud, purchasing power, competition, unfair competition, free trade agreement, Schengen, European Union, Europe, Brussels, state, institution |
| Identity | communautarisme, laïcité, identité, priorité nationale, nation, culture | secularism, identity, national priority, nation, culture |
| Humanitarian_asylum | réfugié, asile, demandeur d’asile, exilé, intégration, accueil, solidarité | refugee, asylum, asylum seeker, exile/exiled person, integration, reception/welcome, solidarity |
| Social_vulnerability | femme, lgbtqi, pauvre, sans-abri, rom, enfant | woman, LGBTQI, poor person, homeless person, Roma person, child |
| Geography | calais, ceuta, aquarius, paris, nice, italie, france | Calais, Ceuta, Aquarius, Paris, Nice, Italy, France |
Figure 4
Annual evolution of the thematic distribution of conjuncts associated with immigration in the FR-L and FR-R subcorpora.
Figure 5
Annual evolution of the thematic distribution of conjuncts associated with migrant in the FR-L and FR-R subcorpora.