Fuentes-Lara, C. & Arcila-Calderón, C. (2023). Anti-Muslim hatred hate speech on social networks. An analysis of attitudes to Islamophobia on Twitter (El discurso de odio islamófobo en las redes sociales. Un análisis de las actitudes ante la islamofobi

Country

Spain

Title

Fuentes-Lara, C. & Arcila-Calderón, C. (2023). Anti-Muslim hatred hate speech on social networks. An analysis of attitudes to Islamophobia on Twitter (El discurso de odio islamófobo en las redes sociales. Un análisis de las actitudes ante la islamofobia en Twitter), Revista Mediterránea de Comunicación.

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Year

2023

Publication type

Research evidence/report

Geographical coverage

National

Area/location of interest

Not applicable - national level

Type of Institution

Academic/research institution

Institution

Spain, Mediterranean Journal of Communication (Revista Mediterránea De Comunicación), 14(1), 225–240. https://doi.org/10.14198/MEDCOM.23044

Thematic focus

Racism & xenophobia

Groups

Muslims

Key findings

Anti-Muslim hatred exists on social networks and there is implicit hate speech. This discourse is stoked by those against immigration and intensified with anti-Muslim hatred and by the ambivalent profiles to immigration who react to terrorist attacks or portrayals of child and teenage immigrants by rejecting Muslims and joining the anti-Muslim hatred discourse. It referred to other institutions which study new migrant narratives; hate speech cannot be countered by more messages because this makes them more socially relevant. The author concluded that rather than provoking confrontation about hate speech, the narrative framework should be changed (porCausa, 2020) or the current hegemonic narrative be replaced (More in Common, 2019).

Methodology (Qualitative/Quantitative and exact type used, questionnaires etc)

Survey - quantitative and qualitative research

Sample details and representativeness

In May 2021 over 7,000 tweets were analyzed which included the key words in the research for a determined time filter (see table 1). A final sample of 2,205 tweets were chosen once repeated ones were deleted as well as those which used the key words but in other contexts and those which were not logical but whose meaning depended on a hyperlink or attached images.

DISCLAIMERThe information presented here is collected under contract by the FRA's research network FRANET. The information and views contained do not necessarily reflect the views or the official position of the FRA.