3.1 Description of the dataset

The HaSpeeDe2020 dataset contains tweets and news headlines: a part gathered from other existing corpora and another collected in the last years from social media and newspapers online. All these data were annotated using the guidelines defined for IHSC.Footnote f

Considering the aim of the organizers of HaSpeeDe of encouraging the development of more robust systems of detection in cross-genre contexts, they proposed two test sets in the competition: one composed of tweets (Test_TW) and another composed of news headlines (Test_TW), whereas the training set (Train_TW) of HaSpeeDe2020 consists only of tweets.

About the composition of Train_TW and Test_TW, a part of tweets comes from the Twitter dataset released in the first edition of HaSpeeDe in 2018 (and partially derived from IHSC); the rest instead has been gathered for the Italian hate speech monitoring project “Contro l’Odio” (Capozzi et al. Reference Capozzi, Lai, Basile, Musto, Polignano, Poletto, Sanguinetti, Bosco, Patti, Ruffo, Semeraro and Stranisci2019). In particular, only data posted between September and December 2018 were included in Train_TW, whereas Test_TW contains the tweets posted between January and May 2019.

The news headlines about immigrants related events were used only in the second test set (Test_NW). These data were retrieved between October 2017 and February 2018 from online newspapers such as La Stampa, La Repubblica, Il Giornale, Liberoquotidiano.

Taking into account this composition of HaSpeeDe2020 and the fact that some tweets are already annotated as ironic and non-ironic (iro and non-iro), we harmonized the annotation of HaSpeeDe2020 labeling the missing instances, and providing also the annotation of sarcastic and non-sarcastic (sarc and non-sarc) texts. For this last label, we followed the schema of annotation used for creating the dataset released for the IronITA shared task organized by Cignarella et al. (Reference Cignarella, Frenda, Basile, Bosco, Patti and Rosso2018) at EVALITA 2018.

In IronITA, irony is defined as a figurative language device that conveys a meaning that is secondary or opposite to the literal one. Linguistic literature places irony among metalogic figures that are the figures that modify the logic value of the utterance, breaking the maxim of quality (Grice Reference Grice1975) and affecting the literal meaning (Garavelli Reference Garavelli1997). Instead, sarcasm is conceived as a type of irony that aims to mock and scorn a victim, and that, differently from other forms of irony, is used to ridicule a specific target (Lee and Katz Reference Lee and Katz1998).

Finally, Train_TW was extended with other instances with the same annotation from IHSC, to have a longer sample for the analyses. This extended version is called here HaSpeeDe2020_ext.

Table 1 shows the data distribution in HaSpeeDe2020_ext (as well as the increment of tweets from Train_TW to Train_TW_ext) and the average of number of tokens per instance in each class (#token/text). Taking into account the different textual genres of our dataset, we calculated the average separately for tweets and news headlines. Table 2 reports some examples from Train_TW_ext.

Table 1. Distribution of Labels in HaSpeeDe20_ext

3.2 Statistical analysis

Exploiting the various labels for each instance, we applied a statistical analysis to study the association between ironic language (irony and sarcasm) and abusive language (hate speech and stereotype) interpreted as nominal variables of a population. In particular, we computed:

• the $\chi^2$ test of independence that, by means of the interpretation of p-value, gives information on the existence or not of significant relations between nominal variables;

• the Yule’s Q to indicate if the association between two binary variables is positive (values close to 1), negative (values close to -1), or null (values close to 0).

To reject the null hypothesis (hypothesis that the variables are independent) of the $\chi^2$ test of independence, the p-value should be minor than the significance level set by convention to 0.05, and to calculate the p-value, we considered a degree of freedom based on the number of observations. The results of this analysis are reported in Table 3.

Table 2. Examples from HaSpeeDe20_ext

Table 3 shows that in tweets, the association between sarcasm and abusive language reports high scores, especially in the case of hate speech. Differently from sarcasm, the values related to the relation between irony and stereotypes are lower and in the case of non-sarcastic irony the relation is even absent.

Table 3. p-values/Yule’s Q values between Ironic and Abusive Language

About the genre of news headlines, the association between hateful and ironic language appears stronger than in tweets, especially in the cases where irony is not sarcastic. Although the values related, particularly, to news headlines are based on very few data (Table 1), this analysis gives us a first look of the possible characteristics of indirect language in messages containing hate speech and stereotypes in different textual genres.

We propose the same analysis between the two dimensions of abusive language analyzed in this work.

Table 4 shows a strong association between hate speech and stereotypes, confirming the fact that, especially in implicit contexts such as news headlines, abusive language is characterized mainly by negative stereotypes that support the intolerance towards specific groups (see Table 2).

Table 4. p-values/Yule’s Q values between Hate Speech and Stereotypes

4.1 Neural network architecture

In the first set of experiments (FT_model), we simply fine-tuned the LMs on hate speech and stereotypes classification tasks, taking into account the pooled output of the BERT-based model, adding a dropout layer to prevent the over-fitting, a dense layer with standard ReLU activation, and a final dense layer to get the class-related probability employing a Sigmoid function. As optimizer, we used Adam with a really low learning rate (0.00001) found by means of a specific callback function.Footnote j Finally, to minimize the loss function during the training, we used the binary cross-entropy function for binary classification provided by keras library.

The same structure and hyperparameters are applied for the second set of experiments (MTL_model). For the MTL context, on the top of the previous network, we added two final dense layers that employ Sigmoid function to get one output for each task. In accordance with the standard BERT input representation (Devlin et al. Reference Devlin, Chang, Lee and Toutanova2018), the text is represented in both networks as tokens, segments, and masked input. To load the pre-trained models for TensorFlow environment and tokenize the texts for creating tokens-input, we used transformers libraryFootnote k.

In the last set of experiments (MTL_model+Features), we employed the same network of MTL_model, adding a concatenate-layer that combines the pooled output of BERT-based models with a features’ vector representation input-layer. Before the concatenation, we applied the batch normalization technique to the features’ input-layer to standardize the layer and stabilize the learning process. For the MTL context, also here we have two output layers, one for each task.

To weight the majority of extracted features, we used the TF-IDF (term frequency-inverse document frequency) measure calculated for each word of the vocabulary (TF-IDF_dict). Other semantic and pragmatic features are weighted considering the scores of polarity and the cosine similarity. These weights have been standardized using MinMaxScaler of scikit-learn with default range of scaling.

To create the TF-IDF_dict and the word embedding model used to compute the cosine similarity, we preprocessed the texts: deleting URLs and symbols like @ and # to maintain the lexical information of hashtags and usernames; tokenizing and lemmatizing words using the implementation of TreeTaggerFootnote l for pythonFootnote m; and removing stopwordsFootnote n to retain lexical significant words.

4.2 Linguistic features

To create the set of features, we took inspiration from previous works that define specific patterns to identify irony, sarcasm (Hernández Farías, Patti, and Rosso Reference Hernández Farías, Patti and Rosso2016; Cignarella et al. Reference Cignarella, Basile, Sanguinetti, Bosco, Benamara and Rosso2020), and abusive language (Frenda et al. Reference Frenda, Banerjee, Rosso and Patti2020).

Style related features. This set contains punctuation marks and patterns of negation. Punctuation (punct) is commonly used to express the intended meaning of the message. For instance, users use quotation marks to point out the opposite of the literal meaning of a word, such as “risorsa” (“resource”). Negation (negation) proved to play an important role in the process of comprehension of figurative language (Giora, Givoni, and Fein Reference Giora, Givoni and Fein2015; Karoui et al. Reference Karoui, Farah, Moriceau, Patti, Bosco and Aussenac-Gilles2017). In the features’ vector, they are represented by the sum of their TF-IDF values in the text.

Syntax related features. This set involves specific syntactic dependencies expressing adverbial locutions (adv_loc), intensifiers (intens), discourse connections (disc_conn), mentions (mention), and nominal phrases (and the number of nominal phrases in the tweet) (nom_phrase and num_nom_phrase). To extract these features, we used spacy-udpipe library with TWITTIRò model specified for short texts in ItalianFootnote o (Cignarella et al. Reference Cignarella, Bosco and Rosso2019), and to retrieve their weights, we exploited TF-IDF_dict.

Lexical semantics-related features. This set is composed of:

• Lexical information about offensive language extracted exploiting the HurtLexFootnote p multilingual lexicon (Bassignana, Basile, and Patti Reference Bassignana, Basile and Patti2018). HurtLex was created from the Italian lexicon “Le Parole per Ferire” by Tullio de Mauro, and the words in the lexicon are classified in 17 types of offenses (see Table 5) enclosed in two macro-categories: conservative, that are the words with literally offensive sense; and inclusive, that are the words with not literally offensive sense but that could be used with negative connotation. To extract these features, we used the featurizerFootnote q created specifically for this lexicon that put the attention on the categories. Therefore, to represent them in the features’ vector, we computed the sum of the TF-IDF values of all words in the text belonging to each category.

• Semantic information about incongruities and similarities revealed by words and pairs of words in the text. These features are extracted considering the variability of the TF-IDF weights of the words in the text by means of the standard deviation ( $\sigma$ ) and the coefficient of variation (cv), the average of weights (avg), and the maximum (max), minimum (min), and median (med) values of the list of the TF-IDF values of words (W) and bigrams of words (B) in a text. The values related to bigrams are computed using the weights’ normalization on the scores of maximum and minimum (C1) and of standard deviation and average (C2). Moreover, we calculated the similarity ( $\cos(\theta)$ ): 1 between pairs of words (vector of bigram of words) and the sentence context (corresponding to sentence vector) ( $\mathtt{\cos(\theta)\_BS}$ ), and 2 between the bigrams of words within the sentence ( $\mathtt{\cos(\theta)\_BB}$ ). To represent them in the features’ vector, we computed $\sigma$ , the coefficient of variation, the average, and maximum, minimum, and median scores of lists of cosine similarity values. The word embedding model, created for computing these values has been created following the methodology presented in Frenda et al. (Reference Frenda, Cignarella, Basile, Bosco, Patti and Rosso2022).

Table 5. HurtLex Categories

Pragmatics related features. This set consists of affective information extracted exploiting Sentix and Emolex dictionaries.

• To extract the sentiment of the text, we used SentixFootnote r (Basile and Nissim Reference Basile and Nissim2013), that contains for each lemma scores about positive and negative sentiment, polarity, and intensity. Using this information, we calculated the average of positive and negative score of words in the text (avg_positive and avg_negative), $\sigma$ of polarity, and the intensity average (avg_intensity).

• To capture emotions and feelings, we used EmoLexFootnote s (Mohammad and Turney Reference Mohammad and Turney2013), a multilingual lexicon containing for each lemma information about the 8 principal emotions of Plutchik (Plutchik and Kellerman Reference Plutchik and Kellerman2013). Inspired by Plutchik (Reference Plutchik2001), we exploited the wheel of emotions to capture in the message the principal emotions (anger, anticipation, disgust, fear, joy, sadness, surprise, trust), the primary dyads or feelings (aggressiveness, optimism, love, submission, awe, disapproval, remorse, contempt), and the variability of opposite emotions/feelings by means of $\sigma$ . Emotions and feelings are represented in the features’ vector by the sum of the TF-IDF values of the words related to each emotion/feeling in the text.

4.2.1 Features relevance

Figure 1 shows the relevance of the sets of features analyzed for each phenomenon, computed by means of $\chi^2$ values. In particular, we report the most relevant features with a $\chi^2$ greater than 3.

Figure 1. Relevant Characteristics in Train_TW_ext.

Looking at Figure 1, we can notice that Hate Speech and Stereotypes are characterized by very similar features. In general, both are featured by negative emotions and feelings (anger, awe, disgust, aggressiveness, fear) and by offensive words with conservative and inclusive interpretation. Some categories of offenses related especially to animals, physical disabilities or diversity, behaviors/morality, and general swear words are more relevant in hate speech, whereas in stereotyped messages the offenses more significant are linked in particular to economic and social issues, cognitive, and ethnic sphere, even if in a more indirect way.

At semantic level, the minimum score of similarity between the bigrams of words within the sentence ( $\mathtt{\cos(\theta)\_BB}$ ) appears to be relevant in stereotypes recognition. This specific feature brings out the semantic incongruity in the text: a common technique used to express irony (Riloff et al. Reference Riloff, Qadir, Surve, De Silva, Gilbert and Huang2013; Joshi, Sharma, and Bhattacharyya Reference Joshi, Sharma and Bhattacharyya2015; Pan et al. Reference Pan, Lin, Fu and Wang2020).

Finally, both phenomena appear characterized by specific syntactic patterns, such as negation and adverbial locutions. From a manual examination, we found that the former are used especially to mark some characteristics of outgroup, juxtaposing it sometimes with the in-group, while the latter tend to increase the intensity of some beliefs or make the sentences mainly nominal. Here are some examples of the presence of these markers:

1. (6) Ora ankio andrò ad emigrare dato che qui sarà tutto occupato da stranieri. tanto di noi italiani non gliene frega nessuno…. vergognaaa

   Now me too I will go to emigrate since everything here will be occupied by foreigners. so much of us Italians do not give a damn…. shame on

2. (7) I nostri migranti non erano assolutamente come questa gentaglia qui! Continuare a fare questo paragone un’offesa per tutti quelli che si sono rotti mani e schiena nelle miniere e nelle fabbriche e vivevano nascosti il resto del tempo.

   Our migrants were absolutely not like this scum here! Continuing to make this comparison is an offense to all those who broke their hands and backs in mines and factories and lived in hiding the rest of the time.

4.3 Experimental setting

Our computational analyses, interpreted in this work as classification tasks of the texts containing hate speech (Task A) and stereotypes (Task B), are performed using the same setting and hyperparameters for all the sets of experiments.

To evaluate the performance of the models during the training, we used 20% of Train_TW_ext as validation set. The systems have been trained with a maximum of 20 epochs for each run and a batch size of 32 for each epoch. To avoid problems of over-fitting, we used the early stopping function, monitoring the loss value obtained on the validation set, and to obtain reproducible results, we set a seed function.

In regard to MTL_model+Features, we evaluated the performance using both the designed features (Feat) and selected features (SelectedFeat) for each detection task. To select the best features, we considered their $\chi^2$ value (greater than 10) for a total of 27 features for hate speech and 25 for stereotype detection.

Exploiting the HaSpeeDe framework, each model, for both Task A and Task B, has been evaluated using the test sets (Test_TW and Test_NW) and evaluation measure proposed by the organizers of the shared task: F1-macro as average score of F1 value of each class. Moreover, to assess the characteristics of hate speech and stereotypes in tweets and news headlines, we compared the obtained results with the straightforward baselines provided by the organizers and the best scores obtained by the teams first ranked in the competition:

• Baseline_MFC (Most Frequent Class) that assigns to each instance the majority class of the respective task.

• Baseline_SVC that classifies the texts using an SVM algorithm with unigrams and char-grams (2-5) weighted with TF-IDF.

• TheNorth team (Lavergne et al. Reference Lavergne, Saini, Kovács and Murphy2020) used a simple neural network that fine-tunes UmBERToFootnote t LM using specifically a linear layer with a softmax on top of the CLS token, and applying a novel technique of layer-wise learning rate. TheNorth submitted two runs for each task. The first obtained simply fine-tuning UmBERTo and the second using a multitasking approach that exploits the possible correlation between texts containing hate speech and texts expressing stereotypes about the targets.

• CHILab team (Gambino and Pirrone Reference Gambino and Pirrone2020) experimented transformer encoders in the first run, creating specifically two transformer/convolution blocks for each input (texts and Part-of-Speech or PoS tags) averaged through max pooling and processed finally by a dropout and dense layer to obtain the predictions, and a depth-wise Separable Convolution techniques in the second one. CHILab also used additional tweets taken from twita by means of some keywords extracted from the provided training set to extend the embedding layer of their model.

4.4 Results and discussion

Observing the results in Table 6 Footnote u obtained within the first set of experiments, we can notice interesting correlations in both tasks: the systems using the ItBERTXXL model perform well in tweets (0.788 and 0.764), whereas the systems using ItBERT in news headlines (0.674 and 0.733). AlBERTo, differently from what we expected, contribute to improve the detection of stereotypes in the news genre when the classifier is aware of irony (0.752). Evidently, the generalization about the style of short texts coming from AlBERTo and the knowledge derived from the training on ironic texts help the system to catch some indirect dependencies that make it able to recognize the stereotypes in a very difficult context such as news headlines.

Table 6. Results for Task A and B in Tweets and News Headlines

In order to quantify the impact of linguistic and cognitive aspects, we computed the percentage of $\Delta$ between the values of F1-score of best models (underlined in Table 6) and the best baseline (Baseline_SVC). For a better visualization, we report these values in Table 7.

Table 7. Percentages of Variation

About the expression of stereotypes, differently from the statistical results reported in Table 3, the percentage of variation from baseline model (12.41%) suggests that it is characterized by specific patterns typical also of ironic language, such as the reference to secondary or indirect meanings, principally in less spontaneous texts like news headlines. The opposite trend is visible in hate speech detection, that shows higher values of $\Delta$ when the classifier is aware of sarcasm in both genres, confirming the previous statistics about the use of a sharper form of irony especially in tweets [RQ1].

Looking at the contribution of the mutual information between hate speech and stereotypes, we can notice that, actually, only the detection of hate speech takes advantage of knowledge derived from the simultaneous learning of stereotype detection. And, as we can see in Table 7, the values of $\Delta$ in Task A (12.07% and 11.59%) are very high regardless the textual genre. During the HaSpeeDe shared task, only TheNorth team experimented an approach multi-task learning, showing that the knowledge about stereotypes could improve the identification of hate speech in tweets ( $\Delta$ = 12.20%).

These results allow us to interpret better the strong association emerged in Table 4, proving that even if hate speech could be expressed using negative stereotypes to reinforce or justify the message, the same is not true in reverse [RQ2].

Finally, observing in Table 6 the role of features in the different experiments, we notice that specific linguistic information contributes to increase above all the performance of ItBERT-based models in news headlines. News headlines, in general, are characterized by a different style of writing, less spontaneous than texts coming from social media. To examine their characteristics, we exploited the coarse-grained annotation of NUs provided for Task C, extracting from them the most frequent trigrams of words: “basta balle ecco” (“no more lies here”), “via i migranti’’ (“out the migrants”), and “immigrati la verità” (“immigrants the truth”). These are involved in specific syntactic contexts, like:

1. (8) Immigrati, ammiraglio brutale: ora basta balle. “Ecco chi trama contro l’Italia, serve una guerra”

   Immigrants, brutal admiral: no more lies now. “Here is who is plotting against Italy, we need a war”

2. (9) C’è la scuola, via i migranti: “Siamo contrari allapartheid ma ora serve più sicurezza” There is the school, out the migrants: “We are against apartheid but now we need more security”

The identified NUs remember a peculiar political rhetoric that aims to feed the intolerance against immigrants, called specifically Slogan-like NUs by Comandini and Patti (Reference Comandini and Patti2019). The style of news headlines, thus, makes the detection especially of hate speech harder, and although the designed linguistic features seem to help the systems to go beyond the stylistic aspects, the best performance on hate speech detection in news headlines is obtained by the CHILab team exploiting the syntactic representation of text.