El discurso de odio en Twitter durante la crisis migratoria de Ceuta en mayo de 2021

ICONO 14, Revista de comunicación y tecnologías emergentes, 20(2), 2022. ISSN: 1697-8293 https://doi.org/10.7195/ri14.v20i2.1858 ICONO 14 es editada por: Francisco García (Universidad Complutense de Madrid, España) y Manuel Gertrudix (Universidad Rey Juan Carlos (España) bajo una licencia de licencia de Creative Commons Reconocimiento 4.0 Internacional. Monográficos Hate speech on Twitter during the Ceuta migration crisis in May 2021 El discurso de odio en Twitter durante la crisis migratoria de Ceuta en mayo de 2021 Discurso de ódio no Twitter durante a crise migratória de Ceuta, em Maio de 2021 Aránzazu Román-San-Miguel1* Francisco J. Olivares-García1** Salud María Jiménez-Zafra2*** 1 Department of Journalism II, Faculty of Communication, Universidad de Sevilla, Spain 2 Department of Computer Science, Universidad de Jaén, Spain * Associate Professor (Department of Journalism II). Faculty of Communication, Universidad de Sevilla, Spain ** Associate Professor (Department of Journalism II). Faculty of Communication, Universidad de Sevilla, Spain *** Professor (Department of Computer Science). Universidad de Jaén, Spain Received: 23/02/2022; Revised: 19/03/2022; Accepted: 29/06/2022; Published: 28/07/2022 Translation to English: ALTALINGUA, S.L. To cite this article: Román-San-Miguel, Aránzazu; Olivares-García, Francisco J.; & Jiménez-Zafra, Salud María. (2022). Hate speech on Twitter during the Ceuta migration crisis in May 2021 ICONO 14. Scientific Journal of Communication and Emerging Technologies, 20(2). https://doi.org/10.7195/ri14.v20i2.1858 Abstract 2Icono14 |Julio-Diciembre 2022 Volumen 20 N° 2 | ISSN: 1697-8293 | DOI: https://doi.org/10.7195/ri14.v20i2.1858 This paper analyses hate speech within messages posted on Twitter from 17-25 May 2021 during the crisis caused by thousands of immigrants entering on the Tarajal border in Ceuta. The aim of the research was to group messages that included hate speech into themes. To do this, a mixed methodology was used, and resulted in six themes being identified. Of these, four focused on political issues, accounting for 80% of the messages, and only 20% referred to racism and immigration. Up to five campaigns of unknown origin have also been identified. This paper concludes that hate speech focuses more on political issues than on the problem of immigration itself, its causes, consequences and possible solutions. Keywords: Hate speech; Twitter; Ceuta; social media; online campaigns; immigration Resumen Este trabajo analiza el discurso de odio en los mensajes publicados en Twitter desde el 17 al 25 de mayo de 2021 durante la crisis producida por la entrada de miles de inmigrantes en la frontera del Tarajal en Ceuta. El objetivo de la investigación es realizar una clasificación temática de los mensajes que incluyen discurso de odio. Para ello, se ha empleado una metodología mixta y como resultado se han podido diferenciar seis temas, de los cuales cuatro se centran en temas políticos, suponiendo el 80% de los mensajes, y solo el 20% de ellos se refieren a racismo e inmigración. Además, se han detectado hasta cinco campañas de origen desconocido. Este trabajo concluye que los discursos de odio se centran más en temas políticos que en la propia problemática de la inmigración, sus causas, sus consecuencias y las posibles soluciones. Palabras clave: Discurso de odio; Twitter; Ceuta; redes sociales; campañas online; inmigración Resumo Este documento analisa o discurso do ódio nas mensagens publicadas no Twitter de 17 a 25 de Maio de 2021 durante a crise causada pela entrada de milhares de imigrantes na fronteira de Tarajal em Ceuta. O objectivo da investigação é levar a cabo uma classificação temática das mensagens que incluem o discurso do ódio. Para este fim, foi utilizada uma metodologia mista, e como resultado, foram identificados seis temas, quatro dos quais se concentram em questões políticas, representando 80% das mensagens, e apenas 20% das quais se referem ao racismo e à imigração. Além disso, foram detectadas até cinco campanhas de origem desconhecida. Este estudo conclui que o discurso do ódio se concentra mais em questões políticas do que no problema da imigração em si, nas suas causas, consequências e possíveis soluções. Palavras-chave: discurso do ódio; Twitter; Ceuta; redes sociais; campanhas em linha; imigração 1. Introduction Social media have allowed the use of freedom of expression to degenerate into abuse, creating the need for limits. So much so, that in recent years the media debate has focused on whether freedom of expression is at risk when certain speech - especially hate speech - is being fought in the courts. As Burgos-García (2019) argues, "If people behave anonymously Hate speech on Twitter during the Ceuta migration crisis in May 2021 Icono14 |Julio-Diciembre 2022 Volumen 20 N° 2 | ISSN: 1697-8293 | DOI: https://doi.org/10.7195/ri14.v20i2.1858 3 and fail to adhere to critical, constructive thinking, the limits on freedom of expression are exceeded to the detriment of the rights of others" (p. 138). However, a distinction should be made between different forms of expression that may be rude, and hate crimes themselves, which lead the legal system to place limits on freedom of expression (p. 149). The birth of social networks can be traced back to the beginning of the 21st century, although they did not become popular until years later, when the number of users and the functionalities of these platforms increased. Only a decade later, researchers such as Falxa (2014) began to study hate speech on social media, claiming that "the fight against hate speech on social media comes up against numerous obstacles" (p. 104). Although steps in this direction were already being taken in Europe, such as the decision taken on 24 January 2013 by the Judge of First Instance of Paris's Tribunal de Grande Instance, requiring US- based Twitter "to set up an alert mechanism allowing French users to report any abuse or hate messages for possible removal, and to provide the French authorities with details of any users who have posted illegal tweets" (p. 104). On 31 May 2016, the European Union required Facebook, Google, Microsoft and Twitter to agree to a code of conduct that would require them to review and remove within 24 hours any illegal hate speech posted on their services (Bisht et al., 2020, p. 244). The scientific literature approaches hate speech from different areas of study - especially law (Howard, 2019; Rollnert-Liern, 2019; Bautista-Ortuño, 2017; Falxa, 2014). However, it is significant that studies are carried out as part of communication sciences - especially when there are those who consider social networks to be media (Hütt-Herrera, 2012; Pantoja- Chaves, 2011). This is a point of view that can be disputed. However, what is not disputed is that messages posted on social media reach millions of people every day and it is important to know whether, as channels of communication, they spread hate crimes. On this, a recent study by Arcila-Calderón et al. (2022, p. 32) found that, "in European regions where there is a higher proportion of immigrants, they receive greater public support" and, therefore, "regions in which support was greater saw a lower level of hate speech on Twitter". However, as this paper shows, there is still hate crime on social networks such as Twitter which we should work to eradicate. Some studies about social media that have been published have focused on hate crimes in regard to gender or sexual orientation - such as Arcila-Calderón et al. (2021a). The authors also present "the first prototype for automatically detecting hate speech on Twitter in Spanish, specifically were it is motivated by gender and sexual orientation". This research is based on events in Spain on 17-18 May 2021, when thousands of people entered the country through the border with Morocco at El Tarajal (Ceuta). This prompted Spain’s Prime Minister, Pedro Sánchez, to visit the area on 18 May, and on the same day Reuters provided the media with an image of a Spanish Red Cross volunteer comforting a sub-Saharan immigrant, which generated numerous negative tweets. 4Icono14 |Julio-Diciembre 2022 Volumen 20 N° 2 | ISSN: 1697-8293 | DOI: https://doi.org/10.7195/ri14.v20i2.1858 The trigger for Morocco allowing thousands of people to enter Spain was the Polisario Front leader, Brahim Ghali, being admitted to a Spanish hospital on an Algerian passport, without prior notice being given to the Moroccan authorities, at a time of great tension, as in November 2020 heavy clashes had erupted between Morocco and the Polisario Front over control of Western Sahara. All of this provoked major reaction on social networks, both in Spain and Morocco. Some users took advantage of the occasion to post xenophobic, violent, racist or hate speech messages. Immigration has always been a focus for fomenting speech around rejection and hatred. Accordingly, we can cite studies on the hatred extolled by nationalism in the United States, with migrants being positioned as a threat (Morgenfeld, 2016); and Germany, where the Alternative for Germany (AfD) party - which calls itself anti-refugee and anti-immigration - became the third strongest party in the German parliament in 2017 (Valdez-Apolo et al., 2019, p. 365). The latter study concludes that the predominant tone on Twitter is negative, which the authors attribute to the fact that immigration is frequently related to "hostile events or circumstances of a bellicose, economic, political, demographic and/or climatic nature" (p. 379). However, it has the limitation that "the study was done by manually analysing content" (p. 385). As we will see below, this article has used a mixed method for the analysis. Another focus of hateful and violent publications is Islamophobia (Awan, 2014, Zamora et al., 2021). 1.1. Freedom of expression and social media The fact that freedom of expression is a right does not mean that it is without a number of limitations that have been legally put in place over the years. These limitations are unrelated to the expression of ideas that are perceived as negative - as, if this were the case, pluralism, tolerance and broadmindedness in a democratic society would disappear (González-Herrera, 2018, p. 3). On this, the media are particularly protected as informers and opinion formers, even where this right has not always been exercised in an ethical manner. By way of example, González-Herrera cites the false information published in the UK during the campaign leading up to the referendum to leave the European Union and Donald Trump's 2016 presidential election. According to some studies, the US president had shown himself to be 'an inveterate liar, immune and defiant to factchecking' (Echevarría, 2016, p. 10). It was not the media, but social networks that spread the most fake news during the US Republican candidate's campaign, which was accentuated during the Covid-19 pandemic - fake news that was then passed on to the media that used social networks as sources of information (Fernández-Muñoz et al., 2022; Gutiérrez-Vidrio, 2020; Pérez-Curiel and Limón- Naharro, 2019). Indeed, social media and especially Twitter had to intervene to curb Donald Hate speech on Twitter during the Ceuta migration crisis in May 2021 Icono14 |Julio-Diciembre 2022 Volumen 20 N° 2 | ISSN: 1697-8293 | DOI: https://doi.org/10.7195/ri14.v20i2.1858 5 Trump's misuse of the platform, especially after the assault on the US Capitol on 6 January 2021 and his false messages, if not inciting hatred and violence. On 8 January, Twitter shut down Donald Trump's account and later Reddit shut down the account of Trump supporters for promoting hateful behaviour, followed by Twitch shutting down Trump's account. On Twitter's help website there is an entire section dedicated to the company's policies where hate speech is detected in posted content (Twitter, 2021). During the course of this investigation, several accounts were found to have been deleted for posting hate speech content on a number of occasions. 1.2. Hate speech on Twitter Hate is deemed to be reprehensible conduct in the moral, religious, social and legal spheres, as it can harm rights such as a person or group's honour or dignity (Moretón- Toquero, 2012, p. 4). As early as 2012, an attempt was made to give a name to hatemotivated behaviour on the internet, which Moretón-Toquero (2012) called "cyberhate" - even though, in this study, the internet is referred to in a very generic way, without studying the impact of social media. What it highlighted was the difficulty of legislating in such an open and international domain like the internet, but also the fear that trivialising this issue in a medium accessed by young people as could be aroused by a more influential public (p. 15). To define which material is racist and xenophobic, we referred to the Additional Protocol to the Convention on Cybercrime, concerning the criminalisation of racist and xenophobic acts committed through computer systems, which was agreed in Strasbourg on 28 January 2003 and published in Spain's Official Government Gazette Number 26, Section 1 on 30 January 2015: "Racist and xenophobic material" means any written material, images or any other representation of ideas or theories that advocates, promotes or incites hatred, discrimination or violence, against any person or group of persons, on the grounds of race, colour, descent or national or ethnic origin, as well as religion insofar as religion is used as a pretext for any of these factors. (p. 7.216) The Hate Crime Survey Report for the period December 2020 to March 2021, by the Secretary of State for Security of Spain's Home Office (López-Gutiérrez et al., 2021), classifies hate crime as: racism/xenophobia, sexual orientation/ LGBTphobia, religion, ideology and disability/illness. All these manifestations of hate could be found among the messages downloaded from Twitter for this project. According to a report published by Barcelona City Council, as early as 2014 there were around 10,000 tweets a day that included racist insults in English - equivalent to one in every 6Icono14 |Julio-Diciembre 2022 Volumen 20 N° 2 | ISSN: 1697-8293 | DOI: https://doi.org/10.7195/ri14.v20i2.1858 15,000 tweets (Cabo-Isasi and García-Juanatey 2017, p. 5). Years later, the media would echo Twitter's decision to delete more than a thousand tweets in which racist abuse was detected and it was stated that the British government was working on laws in this area (Cano, 2021). We found some recent studies about hate speech on Twitter, such as that of Valdez- Apolo et al. (2019), who claim that the prevailing sentiment in messages about immigrants is negative, and more so than about refugees. Those authors consider that this "negative tone" may have its origin in the fact that "the migration phenomenon" in the media is "negative in nature", as it is frequently "related to hostile events or circumstances of a bellicose, financial, political, demographic and/or climatic nature" (p. 379). They also address the stigmatisation of refugees through hate messages on social media Frías and Seoane (2019), and Arcila-Calderón et al. (2021b). This is something that can be seen in a latent form in the study at hand, as the massive entry of immigrants into a country - and encouraged by a neighbouring country's authorities' laxity - could be seen by the receiving population as a threat to its integrity as a country. For, although online hate speech might initially be thought to cause no harm due to its virtual nature, studies by Williams et al. (2020) have shown a link between hate speech on social media and actual crime - especially against black and Muslim communities. Other studies have approached the refugee issue by disaggregating the study by gender, such as Amores et al. (2020), who conclude that the image of refugee women is as passive agents in the images published by the media. Political discourse has also been related in different studies to hate messages via Twitter (Arcila-Calderón et al., 2021c; Arcila-Calderón et al., 2020; Casero-Ripollés et al., 2020; Paz, 2021). 2. Material and methods A mixed methodology was used for this project - very similar to that seen in other projects related to hate speech and Twitter. For example, in Miró-Linares (2016), in which both quantitative analysis with data mining techniques and qualitative analysis of content are used. Accordingly, techniques that automatically filter Twitter content based on the use of specific lexicons have also been used - as can be seen in Jiménez-Zafra et al. (2021). First, all messages published in Spanish between 17-25 May 2021 containing the words Ceuta and/or Morocco were downloaded using Twitter's API V2. An additional download was also done based on a series of hashtags, as shown in Table 1. The list of tags was obtained using the Trendinalia, Account Analysis and Google Trends websites, based on Hate speech on Twitter during the Ceuta migration crisis in May 2021 Icono14 |Julio-Diciembre 2022 Volumen 20 N° 2 | ISSN: 1697-8293 | DOI: https://doi.org/10.7195/ri14.v20i2.1858 7 the topics that most interested users and the tags used in the messages. This was then all converted into a spreadsheet. A total of 310,907 tweets were downloaded. Many of them were retweeted posts - that is, original posts that other people simply copied and pasted as they were via their own accounts, without contributing any content. Thus, these messages needed to be eliminated, leaving 38,901 posts at the end of this process. Table 1 shows the number of messages retrieved according to keyword and tag classification: Table I. Complete list of downloaded messages organised by keyword and tag Source: Author's own A second filtering was then performed to remove duplicate entries, as a tweet can include several keywords and tags at the same time. After the new filtering process, the number of publications was reduced to 36,887. In total, 18,441 original messages were identified, the rest being those that were quoted (2,932) or replied to (15,514). For the quantitative analysis, four specific lexicons were used that can be helpful in detecting hate speech. These lexicons have been satisfactorily evaluated in the study by Plaza-del-Arco et al. (2020). The number of words contained in each lexicon along with a couple of significant examples are shown below: 8Icono14 |Julio-Diciembre 2022 Volumen 20 N° 2 | ISSN: 1697-8293 | DOI: https://doi.org/10.7195/ri14.v20i2.1858 Xenophobia: 44 words (e.g. Moor/Muslim), nigger) Immigration: 250 words (e.g. deport him, refugee) Misogyny: 183 words (e.g. bitch, slut) Insults: 279 (e.g. bastard, faggot) To facilitate the analysis, all information was organised in a spreadsheet consisting of 18 fields. The first ten fields were directly related to the information extracted via the Twitter API; the other eight were created based on the content provided by the four lexicons. Each of these lexicons generated two fields in the spreadsheet: one indicating the presence (or not) of words included within them and the other containing the terms that were found: Id: Unique identifier for the tweet. Text: Message written in the tweet. Topic: Field automatically generated based on use of the lexicon filter, which classifies messages according to whether they deal with immigration, xenophobia, insults or misogyny. The result of this classification can be seen in Table 2. Type: Original, reply or quote, depending on whether the message is an original post or whether it is a comment on or quoted from another. Sensitive content: Field provided automatically by Twitter, showing either true or false. Retweet count: Number of times an original post has been retweeted. Comment count: Total number of comments received for each tweet. Quoted count: Number of times a user has quoted a message. Likes count: Total number of users who have liked a message. These four fields have been used to assess the popularity of tweets. Source: The origin of the publication, depending on whether it was tweeted from a mobile or desktop application. Total number of words on immigration: Number of words in the tweet that are included in the immigration lexicon. Number of words on immigration: Number of words from the immigration lexicon that are used in the tweet. Total number of insults: Number of lexicon insults used in the tweet. Insults: Words from the lexicon related to insults and used in the tweet. Total number of misogynistic words: Number of words from the misogyny lexicon that are used in the tweet. Misogynistic words: Words from the misogyny lexicon that are used in the tweet. Total number of xenophobic words: Number of xenophobic words used in the tweet. Xenophobic words: Words from the xenophobia lexicon that are used in the tweet. Hate speech on Twitter during the Ceuta migration crisis in May 2021 Icono14 |Julio-Diciembre 2022 Volumen 20 N° 2 | ISSN: 1697-8293 | DOI: https://doi.org/10.7195/ri14.v20i2.1858 9 Table II. Themed classification based on the lexicon filters Source: Author's own Following this automated organisation, the content was analysed manually by reading the messages marked by the lexicon theme filter, along with all the messages with hashtags. Thus, of the 36,887 tweets downloaded without duplicates, a total of 7000 tweets suspected of containing hate speech were manually reviewed. Of these, 4964 include one of the words in the lexicons, while 1817 relate to the sum of all messages that included one of the hashtags. The fact that a post has a word from a lexicon does not imply that the tweet is violent, just as there are messages that are not marked by lexicons that include hateful content. To find the largest number of these messages, an additional search for the words war, invasion, border, army, humiliation, death and expulsion was done. This added 219 messages to those analysed, bringing the total to 7000. These messages were manually reviewed and 1000 tweets were found that actually included hate speech. Among the rest, although words were used that could indicate hatred, they were not in the context used. The exact number was 1005, but was reduced to 1000 to simplify the calculations. 3. Findings and discussion Based on the analysis of the content of the messages, they were classified into six themed areas. This classification does not take account of messages that form part of organised campaigns, which are discussed in section 3.2. 16 Icono14 |Julio-Diciembre 2022 Volumen 20 N° 2 | ISSN: 1697-8293 | DOI: https://doi.org/10.7195/ri14.v20i2.1858 References Amores, Javier J., Arcila-Calderón, Carlos, & González-de-Garay, Beatriz (2020). The gendered representation of refugees using visual frames in the main western european media. Gender Issues, 37(4), 291-314. https://doi.org/10.1007/s12147-020-09248-1 Arcila-Calderón, Carlos, Amores, Javier J., Sánchez-Holgado, Patricia, & Blanco-Herrero, David (2021a). Using shallow and deep learning to automatically detect hate motivated by gender and sexual orientation on twitter in spanish. Multimodal Technologies and Interaction, 5(10) ht tps://doi.org/10.3390/mti5100063 Arcila-Calderón, Carlos, Blanco-Herrero, David, Frías-Vázquez, Maximiliano, & Seoane-Pérez, Francisco (2021b). Refugees welcome? online hate speech and sentiments in twitter in spain during the reception of the boat aquarius. Sustainability (Switzerland), 13(5), 1-17. https://doi. org/10.3390/su13052728 Arcila-Calderón, Carlos, Blanco-Herrero, David, Matsiola, María, Oller-Alonso, Martín, Saridou, Theodora, Splendore, Sergio, & Veglis, Andreas (2021c). Framing migration in southern european media: Perceptions of spanish, italian, and greek specialized journalists. Journalism Practice, https://doi.org/10.1080/17512786.2021.2014347 Arcila-Calderón, Carlos, de la Vega, Gonzalo, & Blanco Herrero, David (2020). Topic modeling and characterization of hate speech against immigrants on twitter around the emergence of a farright party in Spain. Social Sciences, 9(11), 1-19. https://doi.org/10.3390/socsci9110188 Arcila-Calderón, Carlos, Sánchez-Holgado, Patricia, Quintana-Moreno, Cristina, Amores, Javier J., & Blanco-Herrero, David (2022). Hate speech and social acceptance of migrants in europe: Analysis of tweets with geolocation. [Discurso de odio y aceptación social hacia migrantes en Europa: Análisis de tuits con geolocalización] Comunicar, 30(71), 1-13. https://doi.org/10.3916 /C71-2022-02 Awan, Imran (2014). Islamophobia and Twitter: A typology of online hate against Muslims on social media. Policy & Internet, 6(2), 133-150. https://doi.org/ggb54f Bautista-Ortuño, Rebeca (2017). ¿Eres un ciberhater? Predictores de la comunicación violenta y el discurso del odio en Internet. International e-Journal of Criminal Science, 11, 1-28. https://bit.l y/3kiYLfL Bisht, Akanksha, Singh, Annapurna, Bhadauria, H. S., Virmani, Jitendra, & Kriti (2020). Detection of Hate Speech and Offensive Language in Twitter Data Using LSTM Model en Jain, Shruti, & Paul, Sudip (coord.), Recent Trends in Image and Signal Processing in Computer Vision (pp. 243-264). Springer. https://doi.org/hft4 Burgos-García, Olga (2019). Los límites a la libertad de expresión: el discurso del odio en Internet en Marín-Conejo, Sergio (ed.). El mundo a través de las palabras. Lenguaje, género y comunicación (pp. 138-150). Dykinson, S.L. https://doi.org/gwbs Cabo-Isasi, Alex, & García-Juanatey, Ana (2017). El discurso de odio en las redes sociales: un estado de la cuestión. Ayuntamiento de Barcelona. https://bit.ly/3Bt92vb Cano, Manuela (12 de julio de 2021). Investigan ataques racistas a jugadores ingleses tras la final de la Eurocopa. France 24. https://bit.ly/2WrV3Hq Casero-Ripollés, Andreu, Micó-Sanz, Joseph-Lluís., & Díez-Bosch, Míriam (2020). Digital public sphere and geography: The influence of physical location on twitter’s political conversation. Media and Communication, 8(4), 96-106. https://doi.org/10.17645/mac.v8i4.3145 Echevarría, Borja (2016). Más ‘fact-checking’ contra la posverdad. Cuadernos de periodistas: Revista de la Asociación de la Prensa de Madrid, 33, 9-16. https://bit.ly/3gyBgwL Hate speech on Twitter during the Ceuta migration crisis in May 2021 Icono14 |Julio-Diciembre 2022 Volumen 20 N° 2 | ISSN: 1697-8293 | DOI: https://doi.org/10.7195/ri14.v20i2.1858 17 BOE (30 de enero de 2015). Instrumento de Ratificación del Protocolo adicional al Convenio sobre la Ciberdelincuencia relativo a la penalización de actos de índole racista y xenófoba cometidos por medio de sistemas informáticos, hecho en Estrasburgo el 28 de enero de 2003. BOE, n. 26, sec. I, 7214-7224. https://bit.ly/3mByWsQ Falxa, Joana (2014). Redes sociales y discursos de odio: un enfoque europeo en Pérez Álvarez, Fernando (coord.). Moderno discurso penal y nuevas tecnologías (pp. 89-106). Ediciones Universidad de Salamanca. Fernández-Muñoz, Cristóbal, Rubio-Moraga, Ángel Luis, & Álvarez-Rivas, David (2022). The Multiplier Effect on the Dissemination of False Speeches on Social Networks: Experiment during the Silly Season in Spain. In: Lahby, Mohamed, Pathan, Al-Sakib Khan, Maleh, Yassine, & Yafooz, Wael Mohamed Shaher. (eds) Combating Fake News with Computational Intelligence Techniques. Studies in Computational Intelligence, vol 1001. Springer, Cham. https://doi.org/10.1007/978 -3-030-90087-8_12 Frías Vázquez, Maximiliano, & Seoane Pérez, Francisco (2019). Hate Speech in Spain Against Aquarius Refugees 2018 in Twitter. Paper presented at the ACM International Conference Proceeding Series, 906-910. https://doi.org/10.1145/3362789.3362849 González-Herrera, Daniel (2018). Libertad de expresión y discurso de odio en Europa: protegiendo a las minorías en tiempos de posverdad en Rodríguez-García, Nicolás, Carrizo-González-Castell, Adán, & Leturia-Infante, Francisco J. (coord.) Justicia Penal Pública y Medios de Comunicación (pp. 549-573). Tirant lo Blanch. Gutiérrez-Vidrio, Silvia (2020). El discurso político en la era digital. Donald Trump y su uso de Twitter. Estudios del discurso, 6(1), 56-81. https://bit.ly/3lByCIm Howard, Jeffrey W. (2019). Free Speech and Hate Speech. Annual Review of Political Science, 22, 93-109. https://doi.org/ghrr2v Hütt-Herrera, Harold (2012). Las redes sociales: Una nueva herramienta de difusión. Reflexiones, 91(2), 121-128. https://bit.ly/3EyaONC López-Gutiérrez, Javier, Fernández-Villazala, Tomás, Máñez-Cortinas, Carlos, San-Abelardo-Anta, María Yamir, Gómez-Esteban, Jesús, Sánchez-Jiménez, Francisco, Herrera-Sánchez, David, Martínez-Moreno, Francisco, Rubio-García, Marcos, Gil-Pérez, Victoria, Santiago-Orozco, Ana María, & Gómez-Martín, Miguel Ángel (2021). Informe de la encuesta sobre delitos de odio. Secretaría General Técnica del Ministerio del Interior, NIPO: 126 21 071 6. https://bit.ly/3nM bMyS Jiménez-Zafra, Salud María, Sáez-Castillo, Antonio José, Conde-Sánchez, Antonio, & Martín-Valdivia, María Teresa (2021). How do sentiments affect virality on Twitter? Royal Society Open Science, 8(4), 1-11. https://doi.org/gwdf Maqueda, Adrián (19 de mayo de 2021). Ni es en Turquía ni es antigua: la imagen del guardia civil en Ceuta rescatando a un bebé es real. Newtral. https://bit.ly/2XoGqoG Miró-Linares, Fernando (2016). Taxonomía de la comunicación violenta y el discurso del odio en Internet. Revista d’Internet, Dret i Política, 22, 93-118. https://doi.org/g4b6 Moretón-Toquero, María Aránzazu (2012). El ciberodio, la nueva cara del mensaje de odio: entre la cibercriminalidad y la libertad de expresión. Revista jurídica de Castilla y León, 27, 1-18. htt ps://bit.ly/3DwlLQb Morgenfeld, Leandro (2016). Estados Unidos: Trump y la reacción xenófoba contra la inmigración hispana. Conflicto Social, 9(16), 15-33. https://bit.ly/3pXix3B Pantoja-Chaves, Aantonio (2011). Los nuevos medios de comunicación social: las redes sociales. Tejuelo, 12, 218-226. https://bit.ly/2ZAa6QD 18 Icono14 |Julio-Diciembre 2022 Volumen 20 N° 2 | ISSN: 1697-8293 | DOI: https://doi.org/10.7195/ri14.v20i2.1858 Paz, María Antonia, Mayagoitia-Soria, Ana, & González-Aguilar, Juan Manuel (2021). From polarization to hate: Portrait of the spanish political meme. Social Media and Society, 7(4) http s://doi.org/10.1177/20563051211062920 Pérez-Curiel, Concha, & Limón-Naharro, Pilar (2019). Political influencers. A Study of Donald Trump’s personal brand on Twitter and its impact on the media and users. Communication & Society, 32(1), 57-75. https://doi.org/gwdg Plaza-del-Arco, Flor Miriam, Molina-González, M. Dolores, Ureña-López, L. Alfonso, & Martín- Valdivia, M. Teresa (2020). Detecting Misogyny and Xenophobia in Spanish Tweets Using Language Technologies. ACM Transactions on Internet Technology (TOIT), 20(2), 1-19. https://do i.org/gwhv Rollnert-Liern, Göran (2019). El discurso del odio: una lectura crítica de la regulación internacional. Revista Española de Derecho Constitucional, 115, 81-109. https://doi.org/gwdh Twitter (2021). Política relativa a las conductas de incitación al odio. Twitter. https://bit.ly/3w03uY3 Valdez-Apolo, María Belén, Arcila-Calderón, Carlos, & Jiménez-Amores, Javier (2019). El discurso del odio hacia migrantes y refugiados a través del tono y los marcos de los mensajes en Twitter. RAEIC, Revista de la Asociación Española de Investigación de la Comunicación, 6(12), 361-384. ht tps://doi.org/dxj2 Williams, Matthew L., Burnap, Pete, Javed, Amir, Liu, H.an,& Ozalp, Sefa (2020). Hate in the Machine: Anti-Black and Anti-Muslim Social Media Posts as Predictors of Offline Racially and Religiously Aggravated Crime. The British Journal of Criminology, 60(1), 93-117. https://doi.org/c9qh Zamora Medina, Rocío, Garrido Clemente, Pilar & Sánchez Martínez, Jorge (2021). Analysis of hate speech involving islamophobia on twitter and its social repercussion in the case of the campaign “Remove the labels from the veil”. Anàlisi: Quaderns de Comunicació i Cultura, 65, 1-19. https://doi.org/10.5565/REV/ANALISI.3383