Journalists’ Ethical Responsibility: Tackling Hate Speech Against Women Politicians in Social Media Through Natural Language Processing Techniques

Social media has led to a redefinition of the journalist’s role. Specifically on Twitter, these professionals assume an influential position and their discourse is dominated by personal opinions. Taking into consideration that this platform has proven to be a breeding ground for polarization, digital harassment and hate speech, notably against women politicians, this research aims to analyze journalists’ involvement in this complex scenario. The investigation aims to determine whether, immersed in online and gender defamation campaigns, journalists enhance the quality of public debate or, on the contrary, they reinforce the visibility of this hostile content. To this end, we examined a sample of 63,926 tweets published from 23 to 25 November 2022 related to a campaign of political violence against the Spanish Minister of Equality using Natural Language Processing tools and qualitative content analysis. Results show that during those three days, at least half of the tweets contained hate speech and improper language. In this climate of hostility, journalists participating in the debate not only have an ability to attract likes and retweets but also exhibit polarization and use hate speech. Each ideological position—for and against the Minister—is also reflected in their own uncivil strategies. Under the umbrella of free speech and regardless of argumentative discourses, those journalists who lean towards ideological progressivism tend to insult their opponents, and those on the political right use divisive constructions, stereotyping and irony as attack techniques.

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Publication Details

Journal
Social Science Computer Review
Published
2024-08-05
DOI
https://doi.org/10.1177/08944393241269417
Citations
7
Primary Topic
Hate Speech and Cyberbullying Detection
Type
article
Field-Weighted Citation Impact
2.43

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article

Journalists’ Ethical Responsibility: Tackling Hate Speech Against Women Politicians in Social Media Through Natural Language Processing Techniques

Iris Simón-Astudillo, Lluís-F. Hurtado, María José Castro-Bleda, María Iranzo-Cabrera
7 citations
Social Science Computer Review
Hate Speech and Cyberbullying Detection
2.43
article

Journalists’ Ethical Responsibility: Tackling Hate Speech Against Women Politicians in Social Media Through Natural Language Processing Techniques

Iris Simón-Astudillo, Lluís-F. Hurtado, María José Castro-Bleda, María Iranzo-Cabrera
article en
7 citations

Abstract

Social media has led to a redefinition of the journalist’s role. Specifically on Twitter, these professionals assume an influential position and their discourse is dominated by personal opinions. Taking into consideration that this platform has proven to be a breeding ground for polarization, digital harassment and hate speech, notably against women politicians, this research aims to analyze journalists’ involvement in this complex scenario. The investigation aims to determine whether, immersed in online and gender defamation campaigns, journalists enhance the quality of public debate or, on the contrary, they reinforce the visibility of this hostile content. To this end, we examined a sample of 63,926 tweets published from 23 to 25 November 2022 related to a campaign of political violence against the Spanish Minister of Equality using Natural Language Processing tools and qualitative content analysis. Results show that during those three days, at least half of the tweets contained hate speech and improper language. In this climate of hostility, journalists participating in the debate not only have an ability to attract likes and retweets but also exhibit polarization and use hate speech. Each ideological position—for and against the Minister—is also reflected in their own uncivil strategies. Under the umbrella of free speech and regardless of argumentative discourses, those journalists who lean towards ideological progressivism tend to insult their opponents, and those on the political right use divisive constructions, stereotyping and irony as attack techniques.

Social Science Computer ReviewVol. 43(3)
Universidad de Valladolid (ES), Universitat de València (ES), Universitat Politècnica de València (ES)
Banco Santander, Ministerio de Ciencia e Innovación, Universidad de Valladolid, Agencia Estatal de Investigación
Gender equality
Openalex Percentile: Top 10%
Hate Speech and Cyberbullying Detection
2.43
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