Media headlines shape our initial interpretation of news, framing narratives that influence societal engagement with political and social issues. Yet, they often rely on sensationalism and bias to capture readers’ attention. In this paper, we aim to uncover distinct patterns in Italian headline composition, examining how language and framing vary across political leanings. We analyze a dataset of daily Italian newspaper articles from two outlets with opposing political perspectives, anonymized as Newspaper A and Newspaper B. Our study encompasses the entire set of news and a subset of topics (n = 8) likely to contain stereotypes or clickbait headlines identified using a Large Language Model. Our methodology combines (1) a lexicometric analysis to identify characteristic words of each newspaper, and (2) the training of an accurate deep learning classifier (F1 = 0.84) to learn specific patterns for categorizing headlines into these two perspectives and leveraging explainability techniques to extract and interpret these patterns. Our analysis reveals distinct tonal differences between the two newspapers: Newspaper A generally adopts a more balanced and nuanced approach, while Newspaper B often favors a more direct and sometimes provocative style, especially regarding topics like immigration and social justice. Additionally, Newspaper B’s headlines tend to be brief and punchy, in contrast to the longer, more detailed ones from Newspaper A. Despite these tonal differences, both outlets exhibit similar stereotypical patterns in their coverage, such as consistently emphasizing nationality and group distinctions in ways that can reinforce social stereotypes. This shared tendency suggests that, although their narrative strategies differ, both outlets could contribute to a broader pattern of stereotype reinforcement.

Decoding Narratives: Towards a Classification Analysis for Stereotypical Patterns in Italian News Headlines / Berta, Matteo; Greco, Salvatore; Tipaldo, Giuseppe; Cerquitelli, Tania. - (2024), pp. 5253-5262. (Intervento presentato al convegno 2024 IEEE International Conference on Big Data (BigData) tenutosi a Washington DC (USA) nel 15-18 December 2024) [10.1109/BigData62323.2024.10825258].

Decoding Narratives: Towards a Classification Analysis for Stereotypical Patterns in Italian News Headlines

Berta, Matteo;Greco, Salvatore;Tipaldo, Giuseppe;Cerquitelli, Tania
2024

Abstract

Media headlines shape our initial interpretation of news, framing narratives that influence societal engagement with political and social issues. Yet, they often rely on sensationalism and bias to capture readers’ attention. In this paper, we aim to uncover distinct patterns in Italian headline composition, examining how language and framing vary across political leanings. We analyze a dataset of daily Italian newspaper articles from two outlets with opposing political perspectives, anonymized as Newspaper A and Newspaper B. Our study encompasses the entire set of news and a subset of topics (n = 8) likely to contain stereotypes or clickbait headlines identified using a Large Language Model. Our methodology combines (1) a lexicometric analysis to identify characteristic words of each newspaper, and (2) the training of an accurate deep learning classifier (F1 = 0.84) to learn specific patterns for categorizing headlines into these two perspectives and leveraging explainability techniques to extract and interpret these patterns. Our analysis reveals distinct tonal differences between the two newspapers: Newspaper A generally adopts a more balanced and nuanced approach, while Newspaper B often favors a more direct and sometimes provocative style, especially regarding topics like immigration and social justice. Additionally, Newspaper B’s headlines tend to be brief and punchy, in contrast to the longer, more detailed ones from Newspaper A. Despite these tonal differences, both outlets exhibit similar stereotypical patterns in their coverage, such as consistently emphasizing nationality and group distinctions in ways that can reinforce social stereotypes. This shared tendency suggests that, although their narrative strategies differ, both outlets could contribute to a broader pattern of stereotype reinforcement.
2024
979-8-3503-6248-0
File in questo prodotto:
File Dimensione Formato  
Decoding Narratives- Towards a Classification Analysis for Stereotypical Patterns in Italian News Headlines.pdf

accesso aperto

Tipologia: 2. Post-print / Author's Accepted Manuscript
Licenza: Pubblico - Tutti i diritti riservati
Dimensione 1.01 MB
Formato Adobe PDF
1.01 MB Adobe PDF Visualizza/Apri
Decoding_Narratives_Towards_a_Classification_Analysis_for_Stereotypical_Patterns_in_Italian_News_Headlines.pdf

accesso riservato

Tipologia: 2a Post-print versione editoriale / Version of Record
Licenza: Non Pubblico - Accesso privato/ristretto
Dimensione 1.08 MB
Formato Adobe PDF
1.08 MB Adobe PDF   Visualizza/Apri   Richiedi una copia
Pubblicazioni consigliate

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11583/2996189