1,301 research outputs found
I rifugiati ucraini nei quotidiani italiani, francesi e inglesi
No full textDifferences in the attitude of the public opinion towards migrants and refugees based on their origin and cultural backgrounds have been widely investigated, especially with reference to northern Europe (De Coninck 2020; De Coninck et al., 2019). The study presented in this article aims, on the one hand, to expand the investigation by considering three different areas represented by France, Italy and the United Kingdom; on the other hand, it aims to focus specifically on how mainstream media framed the information about the arrival of refugees from Syria (2014) and Ukraine (2022). The study, therefore, compares six datasets along two different dimensions: the nationality of the media outlets under analysis and the nationality of refugees.
A selection of articles taken from widely distributed national and international newspapers were analysed with the aim to foreground implicit subtexts and bias, with ultimate aim of shedding light on the role of mainstream press discourse in influencing the public opinion about refugees. A mixed corpus was collected from online sources, with reference to the first weeks of mass flows from Syria and Ukraine, respectively March-April 2014 and June-July 2022. The articles were analysed with particular attention to the strategies and discursive patterns in the language used by the media to express emotions (Plutchik, 2001), and the implicit and explicit appraisal of people and facts (Martin and White, 2005). Since communication is not limited to verbal texts but is intrinsically multimodal (Kress, 2010), the analysis also involved images, if present, which were analysed according to Kress and Van Leeuwen's (2006) grammar of images
Do Linguistic Features Help Deep Learning? The Case of Aggressiveness in Mexican Tweets
No full text[EN] In the last years, the control of online user generated content is becoming a priority, because of the increase of online aggressiveness and hate speech legal cases. Considering the complexity and the importance of this issue, this paper presents an approach that combines the deep learning framework with linguistic features for the recognition of aggressiveness in Mexican tweets. This approach has been evaluated relying on
a collection of tweets released by the organizers of
the shared task about aggressiveness detection in the
context of the Ibereval 2018 evaluation campaign. The
use of a benchmark corpus allows to compare the
results with those obtained by Ibereval 2018 participant
systems. However, looking at the achieved results,
linguistic features seem not to help the deep learning
classification for this task.The work of Simona Frenda and Paolo Rosso was partially funded by the Spanish MINECO under the research project SomEMBED (TIN2015-71147-C2-1-P).Frenda, S.; Banerjee, S.; Rosso, P.; Patti, V. (2020). Do Linguistic Features Help Deep Learning? The Case of Aggressiveness in Mexican Tweets. Computación y Sistemas. 24(2):633-643. https://doi.org/10.13053/CyS-24-2-3398S63364324
EMit at EVALITA 2023: overview of the categorical emotion detection in Italian social media task
No full text
First person – Simona Amodeo
No full textABSTRACT
First Person is a series of interviews with the first authors of a selection of papers published in Journal of Cell Science, helping early-career researchers promote themselves alongside their papers. Simona Amodeo is the first author on ‘Characterization of the novel mitochondrial genome replication factor MiRF172 in Trypanosoma brucei’, published in Journal of Cell Science. Simona is a PhD student in the lab of Torsten Ochsenreiter at the Institute of Cell Biology, University of Bern, Switzerland, investigating mitochondrial genome anchoring, replication and inheritance in Trypanosoma brucei.</jats:p
The unbearable hurtfulness of sarcasm
No full text[EN] In the last decade, the need to detect automatically irony to correctly recognize the sentiment and hate speech involved in online texts increased the investigation on humorous figures of speech in NLP. The slight boundaries among various types of irony lead to think of irony as a linguistic phenomenon that covers sarcasm, satire, humor and parody joined by their trend to create a secondary or opposite meaning to the literal one expressed in the message. Although this commonality, in literature sarcasm is defined as a type of irony more aggressive with the intent to mock or scorn a victim without excluding the possibility to amuse. The aggressive tone and the intent of contempt suggest that sarcasm involves some peculiarities that make it a suitable type of irony to disguise negative messages. To investigate these peculiarities of sarcasm, we examined the dataset of the IronITA shared task. It consists of Italian tweets about controversial social issues, such as immigration, politics and other more general topics. Each tweet is annotated as ironic and non-ironic, and, at a deeper level, as sarcastic and non-sarcastic. Qualitative and quantitative analyses of the dataset showed how sarcasm tends to be expressed with hurtful language revealing the aggressive intention with which the author targets the victim. While irony is characterized by being offensive in hateful context and, in general, moved by negative emotions. For a better understanding of the impact of hurtful and affective language on the detection of irony and sarcasm, we proposed a transformer-based system, called AlBERToIS, combining pre-trained AlBERTo model with linguistic features. This approach obtained the best performances on irony and sarcasm detection on the IronITA dataset.The work of S. Frenda, A.T. Cignarella, C. Bosco and V. Patti was partially funded by VolksWagen Stiftung and Compagnia di San Paolo, Italy under the call "Challenges for Europe'' for the research projects "STudying European Racial Hoaxes and sterEOTYPES'' (STERHEOTYPES, S129542). The work of V. Basile, A.T. Cignarella, C. Bosco and V. Patti was partially funded by Google, Italy under the call "Google.org Impact Challenge on Safety'' for the project "Be Positive!''. Finally, the work of P. Rosso was partially funded by the Spanish Ministry of Science and Innovation, Spain under the research project MISMIS-FAKEnHATE on MISinformation and MIScommunication in social media "FAKE news and HATE speech'' (PGC2018-096212-BC31) and by the Generalitat Valenciana under DeepPattern, Spain (PROMETEO/2019/121).Frenda, S.; Cignarella, AT.; Basile, V.; Bosco, C.; Patti, V.; Rosso, P. (2022). The unbearable hurtfulness of sarcasm. Expert Systems with Applications. 193:1-18. https://doi.org/10.1016/j.eswa.2021.116398S11819
APPReddit: a Corpus of Reddit Posts Annotated for Appraisal
No full textDespite the large number of computational resources for emotion recognition, there is a lack of data sets relying on appraisal
models. According to Appraisal theories, emotions are the outcome of a multi-dimensional evaluation of events. In this
paper, we present APPReddit, the first corpus of non-experimental data annotated according to this theory. After describing
its development, we compare our resource with enISEAR, a corpus of events created in an experimental setting and annotated
for appraisal. Results show that the two corpora can be mapped notwithstanding different typologies of data and annotations
schemes. A SVM model trained on APPReddit predicts four appraisal dimensions without significant loss. Merging both
corpora in a single training set increases the prediction of 3 out of 4 dimensions. Such findings pave the way to a better
performing classification model for appraisal prediction
Recognizing Hate with NLP: The Teaching Experience of the #DeactivHate Lab in Italian High Schools
No full textMateriali e discussioni per l'analisi dei testi classici. Indici 1-60
No full textIl volume raccoglie e riassume tutti gli articoli apparsi nei volumi 1-60 della rivista «Materiali e discussioni per l’analisi dei testi classici» («MD»), nel periodo compreso tra il 1978 e il 2008. Esso è diviso in tre parti. Nella prima sono riportati in successione i singoli numeri della rivista, con il relativo sommario. Nella seconda compaiono gli articoli riuniti in sequenza cronologica, sotto il nome dell’Autore e ognuno con un proprio numero d’ordine: di ogni articolo è fornito il sunto, con i concetti, le argomentazioni salienti, i principali luoghi discussi. Nella terza parte sono elencati gli Autori antichi e i Nomi e parole notevoli, con il rinvio al numero d’ordine dell’articolo. Le schedature dei numeri 1-30 sono curate da Andrea Cucchiarelli, quelle dei numeri 31-60 da Simona Fortini.The volume collects and summarizes all the articles published in Volumes 1-60 of the journal "Materiali e discussioni per l’analisi dei testi classici” ("MD"), in the period between 1978 and 2008. It is divided into three parts. In the first part the individual issues of the journal are summarized with the table of contents. In the second part the articles appear in chronological order, under the name of the author and each with its own serial number: each item is provided with a summary including the main points of argumentation and the loci discussed. The third part contains the indexes of the ancient authors, of the names and of the most relevant things and words, with reference to the number of the item. The profiling of the numbers 1-30 is cared for by Andrea Cucchiarelli, those of the numbers 31-60 by Simona Fortini
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