# Time of Your Hate: The Challenge of Time in Hate Speech Detection on Social Media
## 2 Related Works
The aforementioned survey [9] includes the more recent BERT model and introduces a modified and more transparent version of an SVM classifier that does not, however, outperform BERT. We focus on the Italian language on Twitter, building on AlBERTo [21], a BERT model pre-trained on the large-scale corpus of Italian Twitter data TWITA [22]. The authors of [21] show that BERT is a truly powerful tool when applied to training and test sets drawn from the same distribution, in particular for sentiment analysis and irony detection. This result is confirmed in [23], where AlBERTo is applied to hate speech detection on Italian social media. We trained AlBERTo on data that also encompassed the train and reference set from Haspeede [12], the first shared task on hate speech on Italian organized within EVALITA2018 evaluation campaign ([[http://www.di.unito.it/~tutreeb/haspeede-evalita18/index.html](http://www.di.unito.it/~tutreeb/haspeede-evalita18/index.html)]([http://www.di.unito.it/~tutreeb/haspeede-evalita18/index.html](http://www.di.unito.it/~tutreeb/haspeede-evalita18/index.html))). As stated in the introduction, this work aims to explore and compare the temporal robustness of hate speech detection models. This research goal emerged when reflecting on the setup of the project \"Contro l'odio\" (described in [1]), whose primary output consists of a web service dedicated to monitor, measure and visualize the rate of hate speech against immigrants in Italy, compared to the overall volume of messages of the Italian Twittersphere. In this context, it became evident that since the monitoring and classification of tweets are ongoing and the dataset on which the system is trained is fixed, the temporal robustness of the classification models is a non-trivial issue. Computational approaches to the diachronic analysis of language [24] have been gaining momentum over the last decade. An interesting analysis of the dynamics of language changes has been provided by [25]. The authors describe what happens from the language analysis point of view on words that change their meaning during the time. Most of them show a _social contagion_ where the meaning is changed by their common/wrong use on social media platforms. [PERSON] et al. in [26] discuss the changing of words meaning by the influence of immigrant languages, [PERSON] et al. [27], instead, try to quantify these changes in the common language. These studies support our idea about the possible difficulties of an automatic machine learning approach to classify new sentences that have been collected in a time distant enough from the one of training data. We suppose that the language of hate speech is very volatile and influenced by events, and it changes words meaning faster than usual: all these considerations have encouraged us to investigate the robustness of some machine learning models over time. The recent availability of long-term and large-scale digital corpora and the effectiveness of methods for representing words over time played a crucial role in the recent advances in this field. However, only a few attempts focused on social media [28; 29], and their goal is to analyze linguistic aspects rather than understanding how lexical semantic change can affect performance in sentiment analysis or hate speech detection. From this perspective, our work represents a novelty: for the first time, we propose to tackle the issue of diachronic degradation of hate speech detection by exploring the temporal robustness of prediction models. The closest works found in recent literature are [30], where the authors explore the diachronic aspect in the context of user profiling, and [31], who provides a broader view on diachronicity in word embeddings and corpora. Nevertheless, this is the first work investigating the diachronic aspect in the specific context of hate speech detection, which is a crucial issue, especially in application settings devoted to monitoring the spread of the hate speech phenomenon over time.