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ABSTRACT: Corpus linguistics offers scholars the possibility to elaborate and compute linguistic data on a large scale. Data-driven approaches allow bottom-up analyses of pragmatic, lexical, semiotic, societal and cultural phenomena ([1]). The investigation of gender representation in language is one of the most recent application of computational and corpus-based tools (cf. [2]; [3]; [4]). In light of this recent body of work, this study aims to explore the pragmatic, semiotic and cultural implications of Italian media communicative strategies and representation of gender violence. The topic proposed here is of particular interest in our contemporary society. In fact, according to the European Institute for Gender Equality (EIGE), no European-level statistics about "gender violence" were available until 2015, due to lack of reliable data. More accurately gathered information on gender-based violence can further increase awareness and counteract this phenomenon. As a matter of fact, corpora may indeed represent precious resources. However, to the best of our knowledge, studies on the representation of women and gender violence in corpora of media are still scarce ([5]). In an attempt to fill this gap, here we are focusing on the representation of “women-asvictims” in Italian media. Our purpose is to untangle the complexity of linguistic representations and communicative intentions regarding gender-induced violence, by adopting an integrated methodology: a data-driven approach, consisting of both quantitative and qualitative investigations, and a “cross-stylistic”/ “cross-modal” approach. For such purpose, we built a multi-media and multi-modal corpus, composed of two sub-corpora. Specifically: - a 300,000 words corpus consisting in a nine-months collection of crime section articles (WItNECS- Women in Italian News Crime sections). To guarantee the representativeness and the balancedness of the data collected, we surveyed 4 national newspapers: Corriere della Sera, La Stampa, Il Fatto Quotidiano and La Repubblica (national + regional editions of Milan, Florence, Naples, and Palermo). - A video-database of the Italian Amore Criminale (AC) docu-fiction series (season 2015/2016), built with a layered/tiered methodology. On these data, we performed different types of analyses to explore and compare journalistic and television expressive and narrative strategies: 1. Computational analyses: a) After having pre-processed the raw text in WItNECS, a structural topic modelling analysis was performed (STM, [6]). STM represents an innovative method exploiting complex algorithms and Bayesian statistics to automatically retrieve thematic information from texts; although this approach is fairly common in social sciences, it has only recently been employed in Linguistics, and rarely to Italian ([7]). b) Using the toolchain of the Open Polarity Enhanced Name Entity Recognition project (OpeNER, [8]), we detected named entities ([9]) in WItNECS. 2. Lexical analyses: a) For AC television language, we performed a video-to-text alignment on ELAN [10] with speech and gesture tagging following the coding norms proposed in [11]. Accordingly, we identified: Iconic gestures (modeling the shape of an object or the motion of an action); Metaphoric gesture (conveying abstract/symbolic contents); Deictic gestures (such as pointing objects in conversational space); Emblematic gestures (with standard properties and language-like features). The same coding was applied for tagging speech acts. b) For WItNECS, corpus-based frequentist analyses were performed on SketchEngine ([12]) (i.e. N-grams, collocations, keywords-in-context, association measurements). 3. Pragmatic analysis: Along with lexical-based analyses, an investigation of metaphorical language in WItNECS and AC was performed, based on Conceptual Metaphor Theory (CMT, [13]), using MetaNet’s online repository ([1]) as resource. In fact, recent corpus-based works have shown the importance of metaphors in highlighting implicatures and underlying ideologies in societal and cultural discourse (cf [14]; [15]). Some interesting patterns emerge from these multi-level investigations. - Text mining and STM provided a corpus-level representation of the major themes – and indirectly the journalistic communicative intentions and narrative strategies – regarding gender violence news. It seems that among the ten topics detected, those delineating the crime are estimated to represent high proportions in the text. Here we are presenting examples from four topics that together contribute to over 60% of the “message” conveyed in WItNECS: Topic A: maltrattamenti, minacce, pugni, botte, calci, percosse; Topic B: stazione, treno, turista, telecamere; Topic C: donna, carabinieri, omicidio, delitto, uccisa, trovata; Topic D: ergastolo, imputato, assise, aula. Other topics that emerged regarded sexism, social media, and prostitution. - The automatically classified named entities perfectly reflect the journalistic genre – detailed deictic information and scarce descriptions – and the contemporaneity and technicality of gender violence phenomena (e.g.: carabinieri [Org.government_agency], omicidio [Event.crime], moglie [Person.relationship], Facebook [Org.business]). Interestingly, besides the typical entities associated to crime news (i.e. people and organizations involved, types of weapons, etc.), social media entities emerge as well, presumably suggesting the intertwining of gender violence and new technologies. - The coding procedure through ELAN revealed that, out of 103 observations, 49% of communicative acts (both speech and gestures) were tagged as metaphoric: within AC, metaphors were purposely used to describe both women’s and men’s socio-psychological behaviour. On the other hand, iconic acts (26%) were employed to describe and/or mime motions of violence. - Differences in communicative strategies and conceptual representation emerge from our study: for example, while newspapers mostly report physical violence, AC also investigates psychological violence; television offers a more complex perspective on the episodes of violence narrated, which nevertheless often results in spectacularizing the crime. However, some striking similarities transpire from the cross-modal and corpus-based frequentist analyses. For instance, both WItNECS and AC tend to portray women stereotypically, as “in-relation-to” a man (wives, mothers) and not as individuals. Moreover, the wide range of metaphors retrieved was further subdivided into conventional (i.e. present on MetaNet) and novel (i.e. absent from MetaNet). Several interesting observations can be made on metaphor networks in the corpus. Notably, both media use (almost) the same types of metaphors. In WItNECS, metaphors are conveyed especially by verbs ([1]), while in AC they are expressed by speech and gestures, and they are pivots for representing the experience of abuse. The rich presence of metaphorical images affects the reader/viewer and solicits a heightened emotional response ([16]). In conclusion, our study employs up-to-date empirical approaches that combine top-down, bottom-up, quantitative, and qualitative analyses to reveal pragmatic, communicative, and psychological aspects of the representation of gender violence in Italian media. REFERECES: [1] Petruck, M. R. L. (Ed.) (2018). MetaNet. Amsterdam/Philadelphia: John Benjamins. [2] Baker, P. (2014). Using Corpora to Analyze Gender. London: Bloomsbury. [3] Fragaki, G. & Goutsos, D. (2015). Women and Men Talking About Men and Women in Greek. In J. Romero-Trillo (ed), Yearbook of Corpus Linguistics and Pragmatics 2015. Current approaches to Discourse and Translation Studies, New York: Springer. [4] Busso, L. & Vignozzi, G. (2017). Gender Stereotypes in Film Language: a Corpusassisted Analysis. In R. Basili, M. Nissim, G. Satta (eds), Proceedings of the Fourth Italian Conference on Computational Linguistics (CLiC-it 2017), Rome, Italy, December 11-13, 2017. CEUR Workshop Proceedings 2006, CEUR-WS.org 2017. [5] Abis, S. & Orrù, P. (2015). Il femminicidio nella stampa italiana: un’indagine linguistica. gender/sexuality/italy, 3, 18-33. [6] Roberts, M.E., Stewart, B.M., Tingley, D., Airoldi, E. M. (2013). The Structural Topic Model and Applied Social Science. Advances in Neural Information Processing Systems Workshop on Topic Models: Computation, Application, and Evaluation. 2013. [7] Brookes, G., & McEnery, T. (2019). The utility of topic modelling for discourse studies: A critical evaluation. Discourse Studies, 21(1), 3– 21. https://doi.org/10.1177/1461445618814032. [8] Agerri, R., Cuadros, M., Gaines, S., & Rigau, G. (2013). OpeNER: Open Polarity Enhanced Named Entity Recognition. Procesamiento del Lenguaje Natural, 51, 215-218. [9] Yadav, V. & Bethard, S. (2018). A Survey on Recent Advances in Named Entity Recognition from Deep Learning models. Proceedings of the 27th International Conference on Computational Linguistics, Santa Fe, New Mexico, August 20-26, 2018, 2145–2158. [10] ELAN (Version 4.9.4) [Computer software] (19 May 2016). Nijmegen: Max Planck Institute for Psycholinguistics. Retrieved from https://tla.mpi.nl/tools/tla-tools/elan. [11] Kong, A. P., Law, S., Kwan, C. C., Lai, C. & Lam, V. (2015). A Coding System with Independent Annotations of Gesture Forms and Functions during Verbal Communication: Development of a Database of Speech and GEsture (DoSaGE). Journal of Nonverbal Behaviour, 39(1), 93-111. [12] Kilgarriff et al. (2014). The Sketch Engine: ten years on. In Lexicography, volume 1, issue 1, pp. 7-36, New York: Springer. Available online at: https://www.sketchengine.co.uk. [13] Lakoff G. (2014). Mapping the brain's metaphor circuitry: metaphorical thought in everyday reason. Frontiers in human neuroscience, 8, 958. [14] Dodge, E. (2016). A deep semantic corpus-based approach to metaphor analysis: A case study of metaphoric conceptualizations of poverty. Constructions and Frames, 8(2), 256-294. [15] David, O. & Lakoff, G. & Stickles, E. (2016). Cascades in metaphor and grammar: A case study of metaphors in the gun debate. Constructions and Frames, 8(2), 214-255. [16] Citron, F. & Goldberg, A.E. Metaphorical sentences are more emotionally engaging than their literal counterparts. Journal of Cognitive Neuroscience 26, 11,
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