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

被引:0
|
作者
Iranzo-Cabrera, Maria [1 ]
Castro-Bleda, Maria Jose [2 ]
Simon-Astudillo, Iris [3 ]
Hurtado, Lluis-F. [2 ]
机构
[1] Univ Valencia, Valencia, Spain
[2] Univ Politecn Valencia, Valencia, Spain
[3] Univ Valladolid, Valladolid, Spain
关键词
hate speech; improper language; polarization; gender; twitter; social media; journalism; political communication; natural language processing; TWITTER; POLARIZATION; DELIBERATION; EXPRESSION; OPINION; TWEET;
D O I
10.1177/08944393241269417
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
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.
引用
收藏
页数:28
相关论文
共 14 条
  • [1] Towards countering hate speech against journalists on social media
    Charitidis, Polychronis
    Doropoulos, Stavros
    Vologiannidis, Stavros
    Papastergiou, Ioannis
    Karakeva, Sophia
    [J]. Online Social Networks and Media, 2020, 17
  • [2] Detecting hate speech against politicians in Arabic community on social media
    Guellil, Imane
    Adeel, Ahsan
    Azouaou, Faical
    Chennoufi, Sara
    Maafi, Hanene
    Hamitouche, Thinhinane
    [J]. INTERNATIONAL JOURNAL OF WEB INFORMATION SYSTEMS, 2020, 16 (03) : 295 - 313
  • [3] Uncovering Cybercrimes in Social Media through Natural Language Processing
    Ramírez Sánchez J.
    Campo-Archbold A.
    Zapata Rozo A.
    Díaz-López D.
    Pastor-Galindo J.
    Gómez Mármol F.
    Aponte Díaz J.
    [J]. Complexity, 2021, 2021
  • [4] Surveying the Use of Social Media Data and Natural Language Processing Techniques to Investigate Natural Disasters
    Ma, Zihui
    Li, Lingyao
    Mao, Yujie
    Wang, Yu
    Patsy, Olivia Grace
    Bensi, Michelle T.
    Hemphill, Libby
    Baecher, Gregory B.
    [J]. NATURAL HAZARDS REVIEW, 2024, 25 (04)
  • [5] Using Natural Language Processing and Data Mining for Forecasting Consumer Spending Through Social Media
    Mostafa, Noha
    Abdelazim, Kholoud
    Grida, Mohamed
    [J]. INTELLIGENT SYSTEMS AND APPLICATIONS, VOL 4, INTELLISYS 2023, 2024, 825 : 882 - 901
  • [6] Natural Language Processing through BERT for Identifying Gender-Based Violence Messages on Social Media
    Soldevilla, Ivonne
    Flores, Nahum
    [J]. 2021 IEEE INTERNATIONAL CONFERENCE ON INFORMATION COMMUNICATION AND SOFTWARE ENGINEERING (ICICSE 2021), 2021, : 204 - 208
  • [7] Cryptocurrency ecosystems and social media environments: An empirical analysis through Hawkes’ models and natural language processing
    Ortu, Marco
    Vacca, Stefano
    Destefanis, Giuseppe
    Conversano, Claudio
    [J]. Machine Learning with Applications, 2022, 7
  • [8] Cryptocurrency ecosystems and social media environments: An empirical analysis through Hawkes' models and natural language processing
    Ortu, Marco
    Vacca, Stefano
    Destefanis, Giuseppe
    Conversano, Claudio
    [J]. MACHINE LEARNING WITH APPLICATIONS, 2022, 7
  • [9] Analysis of depression in social media texts through the Patient Health Questionnaire-9 and natural language processing
    Kim, Nam Hyeok
    Kim, Ji Min
    Park, Da Mi
    Ji, Su Ryeon
    Kim, Jong Woo
    [J]. DIGITAL HEALTH, 2022, 8
  • [10] Monitoring COVID-19 pandemic through the lens of social media using natural language processing and machine learning
    Liu, Yang
    Whitfield, Christopher
    Zhang, Tianyang
    Hauser, Amanda
    Reynolds, Taeyonn
    Anwar, Mohd
    [J]. HEALTH INFORMATION SCIENCE AND SYSTEMS, 2021, 9 (01)