Racial Disinformation, Populism and Associated Stereotypes across Three European Countries during the COVID-19 Pandemic
Abstract
:1. Introduction
1.1. Racial Hoaxes and Their Associated Stereotypes
1.2. Fake News and Racial Disinformation during the COVID-19 Pandemic
1.3. Racial Hoaxes and Populism in France, Italy and Spain
1.4. Current Research
Aim and Hypothesis
2. Materials and Methods
2.1. Corpus and Coding Process
2.2. Data Analysis Procedure
- -
- Normalisation: file transformation to ensure proper recognition of graphic forms by resolving different ambiguities (e.g., removing excessive spaces or reducing capital letters);
- -
- Stopword removal: elimination of terms that did not convey specific and/or relevant contents (indefinite adjectives, articles, adverbs, exclamations, interjections, prepositions, pronouns, auxiliary verbs and modal verbs);
- -
- Multiword verification: identification of sequences of two or more words that constitute a lexical unit, including subsets such as compound nouns and idiomatic expressions;
- -
- Text segmentation into elementary contexts, with each context corresponding to a transcribed transmission;
- -
- -
- Specificity analysis differentiated in ‘typical lexical units’: comparison of the relative frequencies of the lemmas in a portion of the corpus with the expected theoretical frequencies (Giuliano and La Rocca 2012) and ‘exclusive lexical units’ (i.e., the lemmas present only in a specific portion of the corpus), performed and reported for each country; and
- -
- Correspondence analysis: qualitative–quantitative technique of factorial analysis applied to categorical data based on the co-occurrence matrix. It allows for qualitative interpretation of the factorial plane based on the proximity or distance and/or similarity or dissimilarity between lexical units. The closer the lexical units are, the more frequently they co-occur in the same lexical context, and the more distant they are, the less frequently they co-occur (Benzécri and Bellier 1980). This analysis was carried out and reported in the first case, considering only the country-of-origin variable, and in the second case, considering the stereotype content and pandemic period variables.
3. Results
3.1. Specificity Analysis
- (1)
- National cues, where words related to the French people, politicians and locations were found. In our corpus, these cues were intended to emphasise the economic threat posed by immigrants, the individuation of France as a landing country for migrants without any control, the national identity as downgraded and contaminated (as in the example ‘A third of marriages are with foreigners who become French’) and the contextualisation of specific reported events.
- (2)
- The economic domain, where the included lemmas and their sentences emphasise the favourable conditions for migrants, the complementary feelings of unfairness when thinking of the French’s conditions and the denouncement of the welfare spending for migrants (e.g., ‘Pierre, a retired farmer, lives on 284 euros per month. Unfortunately for him, Pierre is not a migrant. Asylum seekers can receive a monthly allowance of 330 euros from the state’.).
- (3)
- The religious lexicon, the main objective of which was to stress the perceived threat in both temporary situations (e.g., risks related to the COVID-19 pandemic) and more general sociocultural traditions (e.g., ‘In Clichy, a number of rather shocking things are happening: Muslims praying in the streets every day and elected officials who can not take it anymore…’) An additional function of the religious domain is to mock migrants’ habits (e.g., ‘On this first day of Ramadan, the photo of a cow-shaped box laughing with an Arabic design is being shared on social networks as a special Ramadan promotion’).
- (4)
- The migratory domain, whose lexicon is related to accentuating the criminal rate (e.g., ‘Chloë, 9 years old, rapt, killed and abused in Calais by a migrant’).
- (5)
- Both specific and wider threats or dangers concerning the national identity, the extent of the migratory flows, and more general socio-political and economic problems, as in the following example: ‘We still have statistical evidence of the link between mass immigration and a staggering increase in insecurity’.
- (1)
- The COVID-19 pandemic emergency. This topic that first emerged had a wide range of subthemes, where migrants are presented both as potential threats (e.g., ‘If the right attention is not paid to ghettos and buildings occupied by foreigners, there is a risk of witnessing a new explosion of COVID-19.’) and as a real danger (e.g., ‘Migrant with COVID repatriated. And now 100 agents are in quarantine.’). In addition, migrants are pointed out as benefiting from unfair aid and as objects of more general public and social debates (e.g., ‘The problem with COVID-19 is not the infection itself: that is surmountable. We have the tools, we have safety measures, and we know that the mortality rate from COVID-19 is comparable to that of any other influenza’.).
- (2)
- National cues, where a special focus on political personalisation was found. As a matter of fact, wide opposition between the two central national leaders is proposed. On the one hand, Salvini (representing closed-port measures concerning migrants) is depicted as a purposeful leader who takes on important positions and is invoked as a supervisory authority and a victim of partiality and bias; on the other hand, Conte (a leader trying to mitigate too restrictive measures against migrants) is defined as a weak-willed leader and a target of complaints and critics. Beyond these political issues, the national referred lexicon also includes Italian people as victims of extremely violent acts (e.g., ‘Italian boy beaten up by 3 immigrants’) and symbolic victims of unfair measures taking advantage of migrants, who have a dismissive attitude towards Italy.
- (3)
- Landing and hosting of migrants, where the lexicon includes the several steps and demands concerning these problems, which are connected to several risks (crime and health problems) and to favouritism from political management.
- (4)
- Current affairs and crime, where objects, actions and culprits give life to a wide range of criminal acts.
- (5)
- The socioeconomic domain. For example, the world of gambling is cited to emphasise the contradiction between immigrants’ (presumed) hardship and (real) careless behaviours. This domain is specifically marked when it relates to (1), thus connecting the socioeconomic lexicon to the COVID-19 emergency and blaming migrants’ COVID-19 infractions (e.g., ‘Migrants, they don’t care about COVID and DPCM: they crowd Snai betting halls. No rules for them’.).
- (1)
- National and geographic cues related to general references and contextualised events.
- (2)
- The ethnic and religious domains highlighted (a) the contradictions between the native population and migrants, either beneficiary or pretentious, arrogant and dismissive (e.g., ‘If you are a Spanish worker, you pay 1500 euros for [a] truck licence. And if you are an immigrant, the Andalusian government pays for it’). In addition, a too high rate of the migratory phenomenon is denounced.
- (3)
- Feelings of threat mostly related to crime rates as well as to the paradoxical condition of the Spanish population, which is both economically deprived and at risk of losing traditions. News events are accurately told, and details are usually proposed as aggravating conditions (e.g., ‘14 North Africans affected by scabies arrested for assaulting a girl who was looking for her boyfriend; and when the boyfriend appeared, they stabbed him in the face’). In addition, the condition of clandestinity as a beneficiary of governmental benefits and costs is proposed.
- (4)
- The economic side of the problem mainly reflects the migrants’ parasitic condition (e.g., ‘The Municipality of Adra once again launches aid to support the unemployed gypsy and Muslim population’).
3.2. Lexical Correspondence Analysis
4. Discussion
- (1)
- Religious and ethnic references: In France and Spain, there is a stronger emphasis on religious and ethnic dimensions, which include concerns about specific religious practices (such as Muslim prayers in public spaces) and the portrayal of ethnic or religious minorities as potential threats to the local culture and traditions. France’s hoaxes are specifically crafted to mock migrants’ religious habits.
- (2)
- Specific geographical references: While the high rate of migratory phenomena is discussed in all three hoaxes, Italy focuses on specific locations. Its concern is for ports and other specific areas to become landings for migrants without any control. This reflects the Italian political landscape, where issues related to migrants and asylum seekers have been highly visible and criticised. France also seems bothered by the high rate of uncontrolled migratory flows into its ports.
- (3)
- Unique political figures: Each country has its unique political figures at the centre of its hoax narratives. For example, French hoaxes depict LePen as a strong leader against migratory phenomena, fighting to protect and restore the national identity (his slogan is ‘The French first’).
- (4)
- Socioeconomic concerns: In Italy, socioeconomic issues related to gambling are mentioned as irrefutable proofs of migrants’ careless attitude towards Italy’s safety measures against COVID-19 and as demonstrations that they are not truly innocent victims facing hardships. This theme is not as prominent in the hoaxes from France and Spain, which tend instead to criticise the management of resources and welfare between the ‘parasitic’ migrants and the local population.
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Stereotypes Content | COVID-19 Pandemic | |||||||
---|---|---|---|---|---|---|---|---|
B | P | DD | DU | W | C | Pre | Post | |
Italy | 35 | 15 | 24 | 23 | 0 | 0 | 48 | 49 |
36.1% | 15.5% | 24.7% | 23.7% | 0% | 0% | 49.5% | 50.5% | |
France | 10 | 1 | 28 | 31 | 0 | 0 | 54 | 16 |
14.3% | 1.4% | 40% | 44.3% | 0% | 0% | 77.1% | 22.9% | |
Spain | 21 | 6 | 16 | 29 | 0 | 0 | 32 | 40 |
30.9 | 8.3% | 22.2% | 40.3% | 0% | 0% | 44.4% | 55.6% |
Typical Lexical Units | |||||
---|---|---|---|---|---|
LEMMA | SUB | TOT | CHI2 | (p) | |
National cues | FRANCE | 21 | 29 | 107.59 | 0.000 |
FRENCH | 12 | 13 | 84.58 | 0.000 | |
NATIONAL | 5 | 11 | 12.68 | 0.000 | |
Exclusive lexical units: MARINE (7), PEN (7), CALAIS (3), ROUBAIX (3) | |||||
Economic domain | EURO | 15 | 36 | 32.92 | 0.000 |
RECEIVE | 5 | 8 | 20.75 | 0.000 | |
PENSIONER | 3 | 4 | 16.06 | 0.000 | |
TAXPAYERS | 3 | 4 | 16.06 | 0.000 | |
JOB | 4 | 8 | 11.84 | 0.000 | |
ACCOMMODATION | 3 | 5 | 11.73 | 0.000 | |
ALLOWANCE | 2 | 3 | 9.10 | 0.002 | |
PUBLIC HOUSING | 2 | 4 | 5.92 | 0.014 | |
PENSION | 3 | 8 | 5.42 | 0.019 | |
Exclusive lexical units: CREDIT CARD (6), CREDITED (3) | |||||
Religious lexicon | RAMADAN | 5 | 6 | 30.83 | 0.000 |
WEAR | 3 | 4 | 16.06 | 0.000 | |
MUSLIM | 7 | 21 | 10.07 | 0.001 | |
PRAYER | 3 | 8 | 5.42 | 0.019 | |
CROSS | 2 | 5 | 4.06 | 0.043 | |
Exclusive lexical units: SERMONS (3), HEADSCARF (3) | |||||
Migratory domain | IMMIGRATION | 11 | 21 | 35.11 | 0.000 |
IMMIGRANT | 57 | 272 | 25.81 | 0.000 | |
REFUGEE | 4 | 6 | 18.20 | 0.000 | |
REUNIFICATION | 3 | 4 | 16.06 | 0.000 | |
ASYLUM | 7 | 19 | 12.25 | 0.000 | |
APPLICANT | 4 | 8 | 11.84 | 0.000 | |
ARAB | 3 | 5 | 11.73 | 0.000 | |
Threat/danger | CONTAINMENT | 2 | 3 | 9.10 | 0.002 |
INVASION | 2 | 3 | 9.10 | 0.002 | |
STEAL | 3 | 6 | 8.88 | 0.002 | |
EXPOSURE | 2 | 4 | 5.92 | 0.014 | |
Exclusive lexical units: INSECURITY (3), INVADE (3) |
Typical Lexical Units | |||||
---|---|---|---|---|---|
LEMMA | SUB | TOT | CHI2 | (p) | |
COVID-19 pandemic emergency | COVID | 74 | 77 | 11.19 | 0.000 |
CENTRES | 65 | 68 | 9.19 | 0.002 | |
POSITIVE | 27 | 28 | 4.22 | 0.040 | |
Exclusive lexical units: QUARANTINE (34); SANITARY (21); CONTAGION (17); CASE (12); COVID-19 swab test (16); OUTBREAK (12); VIRUS (10); GATHERINGS (8). | |||||
National cues | ITALY | 38 | 39 | 6.70 | 0.009 |
Exclusive lexical units: ITALIAN (55), SALVINI (23), CONTE (21), MATTEO (16), ALPINE (13), MILAN (12), ROME (12), SICILY (12), MINISTER (8), NAPLES (8), PALERMO (8), ALASSIO (7), LAMORGESE (7), VENTIMIGLIA (7) | |||||
Landing and hosting | Exclusive lexical units: SITUATION (20), BOARD (19), GUEST (17), CASE (12), EPISODE (11), REPATRIATE (8), CLIMB (8), LAND (8), UMPTEENTH (7), LANDED (7), TRANSFER (6), SEA (6), HOTSPOT (5), INTEGRATION (5) | ||||
Current affairs and crime | Exclusive lexical units: POLICE (10), ESCAPE (10), CARABINIERE (9), BARRACKS (9), KILLED (9), KILL (8), WEAPONS (7), REJECT (6), KNIFE (5), DENOUNCED (5), INSULT (5) | ||||
Socio-economic domain | Exclusive lexical units: BET (10), TWITTER (6), ZUCKERBERG (6), ECONOMIC (5), FACEBOOK (5) |
Typical Lexical Units | |||||
---|---|---|---|---|---|
LEMMA | SUB | TOT | CHI2 | (p) | |
National and geographic cues | SPAIN | 9 | 10 | 101.56 | 0.000 |
SPANISH | 9 | 11 | 90.73 | 0.000 | |
Exclusive lexical units: CANARIES (6), MASPALOMAS (5), PODEMOS (4) | |||||
Ethnic and religious domain | MUSLIM | 13 | 21 | 93.17 | 0.000 |
MOROCCAN | 7 | 20 | 22.85 | 0.000 | |
GYPSY | 3 | 5 | 20.61 | 0.000 | |
ISLAM | 3 | 6 | 16.24 | 0.000 | |
CROSS | 2 | 5 | 7.94 | 0.004 | |
ISLAMIC | 3 | 10 | 7.66 | 0.005 | |
MAN | 4 | 20 | 4.81 | 0.028 | |
WOMAN | 4 | 22 | 3.89 | 0.048 | |
Exclusive lexical units: ROMANIAN (5), NATIVE (3) | |||||
threat, crimes | ATTACKED | 4 | 6 | 31.39 | 0.000 |
OFFEND | 2 | 3 | 15.69 | 0.000 | |
RAPE | 2 | 3 | 15.69 | 0.000 | |
ILLEGALLY | 2 | 3 | 15.69 | 0.000 | |
DEAD | 2 | 5 | 7.94 | 0.004 | |
CLANDESTINE | 9 | 52 | 7.81 | 0.005 | |
ARRESTED | 2 | 7 | 4.71 | 0.030 | |
economic side of the problem | AIDS | 4 | 5 | 39.22 | 0.000 |
PAY | 5 | 12 | 21.07 | 0.000 | |
EURO | 9 | 36 | 16.85 | 0.000 | |
EAT | 2 | 3 | 15.69 | 0.000 | |
FOOD | 2 | 3 | 15.69 | 0.000 | |
TOURISM | 2 | 3 | 15.69 | 0.000 | |
TOURISTIC | 2 | 3 | 15.69 | 0.000 |
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© 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
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D’Errico, F.; Cicirelli, P.G.; Lops, A.; Scardigno, R. Racial Disinformation, Populism and Associated Stereotypes across Three European Countries during the COVID-19 Pandemic. Soc. Sci. 2024, 13, 465. https://doi.org/10.3390/socsci13090465
D’Errico F, Cicirelli PG, Lops A, Scardigno R. Racial Disinformation, Populism and Associated Stereotypes across Three European Countries during the COVID-19 Pandemic. Social Sciences. 2024; 13(9):465. https://doi.org/10.3390/socsci13090465
Chicago/Turabian StyleD’Errico, Francesca, Paolo Giovanni Cicirelli, Angelica Lops, and Rosa Scardigno. 2024. "Racial Disinformation, Populism and Associated Stereotypes across Three European Countries during the COVID-19 Pandemic" Social Sciences 13, no. 9: 465. https://doi.org/10.3390/socsci13090465
APA StyleD’Errico, F., Cicirelli, P. G., Lops, A., & Scardigno, R. (2024). Racial Disinformation, Populism and Associated Stereotypes across Three European Countries during the COVID-19 Pandemic. Social Sciences, 13(9), 465. https://doi.org/10.3390/socsci13090465