We would like to thank the reviewers for their valuable comments and suggestions.

1. Introduction

1Each aspect of language changes over time, but meaning is the one more susceptible to mutation. According to Blank (1999), change is only a side-effect of the speakers’ pragmatic goal, which is to achieve success in communication. This also means that change is a consequence of the human mind and social interactions: innovations are thus employed and adopted because they are judged to be the most successful strategy to communicate effectively.

2The study of meaning change was the focus of the first scholars of semantics but while they employed manual methods, nowadays many studies are conducted with automatic and semi-automatic tools stemming from computational linguistics and computer science.

3Lexical Semantic Change (LSC) detection, which aims at identifying the change in meaning of words over time using corpus data, is a Natural Language Processing (NLP) task pertaining to lexical and diachronic semantics. Recently, this field has seen an exponentially rising interest but work for languages other than English is still relatively scarce (Schlechtweg et al. 2020).

4The computational literature approaches the task in several ways and with different terminologies: Tahmasebi, Borin, and Jatowt (2021) define the field as “lexical semantic change detection”; this definition is also adopted by both Schlechtweg et al. (2020) and Basile et al. (2020b), which set the task as “identifying words that change meaning over time“. Kutuzov et al. (2018), instead, formalize the task as “detecting semantic shifts”. Finally, Del Tredici, Fernández, and Boleda (2019) employ “short-term meaning shift”, while Gonen et al. (2020) frame the task as “detecting usage change”: in this paper we follow this latter definition.

5Most of the work in this field studies meaning change across decades or even centuries, by leveraging data from different corpora of literary or newspaper data. Fewer studies investigate short-term usage change, by comparing texts produced in smaller time spans, from one to less than ten years apart. This kind of research often uses data from social media, like Twitter or Reddit. When considering such smaller time frames, it is more sensible to talk about “usage change“ rather than “meaning change“ of a word, as proposed by Gonen et al. (2020).

6The focus on use is motivated by the distributional method adopted to investigate the data (Harris 1954), which derives information about the meaning of a word from its context of use, and assumes that words with similar distributional properties have similar meanings (Sahlgren 2008; Ježek 2016; Lenci 2018; Jurafsky and Martin 2021). The kind of semantics that stems from the distributional hypothesis is called distributional semantics or, more specifically, vector space semantics, because it represents words and their meaning as vectors in a geometric space, and calculates the similarity between vectors using the cosine function. With cosine similarity, the nearest neighbors, i.e. the items with the highest similarity score with respect to the target word, can be identified (Lenci 2018). In distributional semantics, there are several types of vectors that are computed with different methods, in particular count-based vectors obtained by counting the co-occurrences of words, and embedded vectors (called embeddings) obtained with predictive neural models. The vectors we use in our experiment belong to the second type and are computed with the Skip-Gram with Negative Sampling (SGNS) version of the word2vec neural model (Mikolov et al. 2013).

7In our study we consider a short-term time span that represents a peculiar socio-cultural and chronological context, the pandemic. Our work starts from the hypothesis that an event such as the COVID-19 pandemic would bring forth changes in the use of words. We focus on Italian, since many other studies were done for the English language. To achieve our goal, we create a new corpus of texts from Reddit, and partition the corpus in two datasets, one for the year 2019 and one for the year 2020. After cleaning and lemmatizing the corpus, we apply the method outlined in Gonen et al. (2020) (§3.3) to our data to detect word candidates that may have undergone usage change from one dataset to the other.

8Our analysis of the proposed candidates indicates that some degree of usage change has occurred: specific word senses gained prominence and new words arose as the need to express concepts connected to the pandemic became more widespread.

9The paper is divided into five sections: Section 2 reviews the contribution of computational linguistics and Natural Language Processing (NLP) to the COVID-19 pandemic; it also formally defines the task of unsupervised meaning change detection and surveys different approaches. Section 3 details the methodology of this study and describes the features of our datasets. Section 4 presents the results of the work and analyzes them. Section 5 draws some conclusions.

Contributions

10Our work contributes to the research on usage change detection and on the impact of the COVID-19 pandemic on language as follows:

• • 1 https://github.com/edoardosignoroni/usage_change_ITA

  creating a new corpus of social media texts for Italian, focusing on short-term usage change. The corpus is available online;1

• testing the application of a relatively recent and computationally light method of usage change detection, previously untested for Italian;

• analyzing the impact of the pandemic on word use in a language different than English.

2. Related work

11In this section we first provide an overview of the work done by the Computational Linguistics and NLP community in response to the COVID-19 pandemic (§2.1). Then, we briefly survey previous studies and methods of computational detection of meaning change (§2.2), with a focus on short-term change (§2.2.1) and on Italian (§2.2.2).

2.1 Computational Linguistics and the COVID-19 Pandemic

12The Computational Linguistics and NLP community can support the research to fight the Coronavirus and its consequences by tapping into the great quantities of unstructured text and speech data; analyzing the countless published research papers, social media post and news articles can be critical to support best practices in clinical management; to understand the public response to the outbreak; to find and contrast spreading misinformation; to automatically identify and organize helpful information from the web.

14In July 2020, the 1st Workshop on Natural Language Processing for COVID-19 was held at the Association for Computational Linguistics (ACL) conference. The second part of the workshop was held in the same year at the Empirical Methods in Natural Language Processing Conference (EMNLP). Both workshops demonstrated the help that the NLP community can provide, mainly in navigating the literature on the virus, in identifying and fighting misinformation and in characterizing the public reaction through the analysis of data from social media, like Twitter and Reddit.

2.2 Automatic Language Change Detection

17Formally, the task of detecting meaning change can be formulated as follows: given corpora [\(C_1\), \(C_2\), ..., \(C_n\)] containing texts created in time periods [1, 2, ..., n], the task is to locate the same words with different meaning in different time periods, or to locate the words which changed the most. Related tasks are to discover general trends in meaning change or the dynamics of the relationships between words (Kutuzov et al. 2018).

18At the word level, most of change detection methods employ vectors, both count-based and neural ones (embeddings), for the words. This comes to the cost of representing all senses of a term with a single representation. Most of the count-based approaches start by building a co-occurrence matrix, often reducing its dimensions by SVD (Singular Value Decomposition). PMI (Pointwise Mutual Information) scores are used for co-occurrence strength rather than raw frequency, while vector similarity is measured with the cosine (Tahmasebi, Borin, and Jatowt 2021). Low similarity is understood as higher amount of change or polysemy.

19Sagi, Kaufmann, and Clark (2009) employ context vectors, that is, the combined vectors of the words in a context window around the word under examination, while Gulordava and Baroni (2011), and Rodda, Senaldi, and Lenci (2017) use also PMI. Kah-mann, Niekler, and Heyer (2017) compare changes in context similarity between ranked series at different points in time. Tang, Qu, and Chen (2013) and Tang, Qu, and Chen (2016) use contextual entropy and reduce dimensions on the fly rather than through SVD. Most of these methods are evaluated qualitatively on a random or manually selected sample.

20The works that use word embeddings train them independently over different time-sliced corpora and then compare them by projecting all representations onto the same space. More specifically, there are three main methods: i. vectors for the first time period are trained without any other information, then the representation for the successive time spans is initialized with the values of the previous interval to which they are then compared using cosine similarity to detect the change (Kim et al. 2014); ii. words are projected using linear mapping on the last time period (Kulkarni et al. 2014; Hamilton, Leskovec, and Jurafsky 2016); iii. mapping is avoided all together by comparing second order similarity, and meaning is modelled as the linear combination of the neighbors of a word from previous time points (Eger and Mehler 2016).

21Dynamic word embeddings are another embedding method for meaning change detection. While different techniques exist involving these vectors, all of them train this kind of word embeddings in the same original space, and then share data across all time periods to update the word representations. Dynamic word embeddings have been shown to be beneficial, because they reduce the need of aligning independently trained embeddings, and the necessity of large datasets, rarely available for historical corpora (Tahmasebi, Borin, and Jatowt 2021). This approach is employed by Bamler and Mandt (2017), Yao et al. (2018), and Rudolph and Blei (2018). Context vectors are shared across all the time slices, while the embeddings are trained only within a single time span. It was shown that dynamic word embeddings perform better than the baselines (Tahmasebi, Borin, and Jatowt 2021).

2.2.1 Short-term Change

22As mentioned in Section 1, some studies focus on investigating short-term meaning change, mostly employing textual data from social networks. Stewart et al. (2017) present a study on short-term change during the Russia-Ukraine crisis of 2014-2015, through data from VKontakte, a popular social media in the area. The aim of the research is to visualize and predict change in a word’s semantics over the weeks, leveraging distributional representations. First, the tf-idf score for each word is extracted and concatenated in a time series that represents a concept drift, a measure which the authors define as a combination of a word’s change in meaning and frequency. Then, temporal word embeddings are learned with the gensim implementation of word2vec: the vectors are initialized with the vocabulary of all the words in the data above a fixed frequency threshold, and then trained with tokenized posts for each weekly timestamp to generate a time series. Different word vectors are compared using cosine similarity and by looking at their neighbors. According to the authors, this study provides a generalizable proof of concept for future studies on short-term shift in social media.

23Del Tredici, Fernández, and Boleda (2019) present an exploration of meaning shift within a period of 8 years with data from online community of speakers (sports subreddits), which allows better observation of short-term meaning shift. Previous research by Del Tredici and Fernández (2018) showed that this and similar communities have features that favor linguistic innovation. The behavior of a standard distributional model is tested when applied to short-term shift, showing that the model is confused by contextual changes due to particular references to people and event. A large sample of community-independent language is used to initialize the word vectors; then, these representations are updated for a certain point in time with the subreddit data.

24Gonen et al. (2020) propose an alternative method to detect usage change. Specifically, they propose to work in the shared vocabulary space with the underlying intuition that words whose usage has changed are likely to be interchangeable with different sets of words. Thus, these words will have different neighbors in the embeddings spaces of the two time periods. Their algorithm first represents each word in a corpus as the set of its top k nearest neighbors; then, it computes the score for word usage change across corpora by considering the size of the intersection of the two sets of neighbors. This method will be further discussed in Section 3.3., as it is the method that we selected for our experiment.

Works on Italian

26While the majority of the experiments on meaning change detection focuses on English, there are also studies for other languages. For example, SemEval 2020 Task 1 (Schlechtweg et al. 2020) addresses the unsupervised detection of meaning change in text corpora of German, English, Latin and Swedish.

27As for Italian, some research has been conducted and presented at EVALITA 2020, under the DIACR-Ita: Diachronic Lexical Semantics task (Basile et al. 2020). The corpus from Basile et al. (2020a), divided into two sub-corpora for the years 1945-1970 and 1990-2014, was used for the DIACR-Ita task. Several methods were submitted: Post-alignment, Joint Alignment, Contextual Embeddings, Graph-based and PoS tag features. Post-alignment systems first train static embeddings and then align them, while Joint alignment does these two processes at the same time. Contextual embeddings systems are based on contextualized embeddings, such as BERT (Devlin et al. 2019). Graph-based systems rely on graph algorithms, while PoS tag features systems use the distribution of targets PoS tags across the time slices. The majority of these systems use cosine distance as a measure of meaning change, except for Contextual embedding representations and Graph-based methods (Basile et al. 2020). The best methods (Prazák, Pribán, and Taylor 2020; Kaiser, Schlechtweg, and Walde 2020) use Skip-Gram with Negative Sampling (SGNS) to compute word embeddings, which are then aligned. Cosine similarity and a threshold are used to detect changed words.

28Basile et al. (2016) employ Temporal Random Indexing, an embedding method first used in Basile, Caputo, and Semeraro (2014). The dataset is the Italian portion of the Google Books Ngrams corpus, split into 10-year period sub-corpora for the time between 1850 and 2012. The vocabulary of each split vocabulary is modeled as the sum of its random vectors and then normalized to give less weight to the most frequent words. To detect shifts, the method by Kulkarni et al. (2014) is used. The study also employs temporal indexing to detect the average span of change in years (Tahmasebi, Borin, and Jatowt 2021).

29Cafagna, De Mattei, and Nissim (2020) study how words are used differently in two Italian newspapers with diverging political opinions, La Repubblica (left-leaning) and Il Giornale (right-leaning). They focus on synchronic change, but the methodology is still relevant to the study of short-term usage change. The embeddings are first trained on La Repubblica texts and then updated with those from Il Giornale. The measure of the shift that the same word has undergone is then computed. A value for the frequency and a combination of both frequency and shift measure is also calculated. Starting from a shared vocabulary, the study features a top-down analysis, concerned with the change affecting the most frequent words in both newspapers; and a bottom-up analysis, that observes how a single word’s usage varies across the two spaces looking both at its embeddings and frequency. It is proposed that the most interesting cases are those whose relative frequency does not change much in the two datasets, but still exhibit a high degree of change.

3. Methodology

30This Section illustrates the methodology of our study: the creation of the corpus (§3.1) and its preprocessing (§3.2), as well as the details of the usage change algorithm we employed (§3.3).

3.1 Corpus

33Our script was run two times, one for each time span. The script proceeds in the following manner: it first retrieves from Pushshift.io the IDs of all submissions in r/italy from the newest to the oldest; it then uses PRAW to collect the title of the submission, its text, and comments. A typical submission includes a title and a more articulated text; the discussion in the comment section is nested, as every user can answer to each comment. During the scraping, the raw text is iteratively saved in a text file for each day of the time frame; the texts are organized in two symmetrical folders. To ensure anonymity, no metadata regarding the author of the submission or comment is requested or saved in any way.

34Despite being a very useful resource, that is, the basis for one of the few studies on Italian and the pandemic, and the first focusing on short-term usage change, the corpus we created has some limitations:

• Multiple languages: the majority of the downloaded texts are written in Italian, however there are some posts and comments in English. These tend to occur in the same context and submissions: most of them are posts from non-Italian users which are answered and discussed in English.

• • 21 https://www.journalism.org/2016/02/25/reddit-news-users-more-likely-to-be-male-young-and-digital-in (...)
  • 22 The total population of Italy as of 1 January 2019 was 59,641,488 inhabitants (http://dati.istat.it (...)

  Representativeness: as a 2016 American study showed, it should be noted that, as a whole, the userbase of Reddit is not representative of the overall population. Users of Reddit were once described as “offbeat, quirky, and anti-establishment“.21 This skewed demographic characterises also the number of users of r/italy: the userbase of the subreddit at the moment of the creation of the corpus was of 267,306 users, which is the 0.45% of the Italian population.22

• • 23 That is, post and comments that are fixed in place by the subreddit moderators at the top of the pa (...)

  Accuracy of the texts: the Pushshift.io project API copies the submission at the moment of its creation on Reddit and does not update it. However, users often modify their posts and comments. These so called “EDITs“ can be quite long and elaborate at times, and thus their absence may mean some loss of useful data. Moreover, some duplicate texts are present even if some specific restrictions were included based on the structure of Reddit discussions. For instance, text of stickied posts23 and comments are copied only once.

35Despite these limitations, the resulting corpus has proved very useful to our purpose of detecting word usage change. Table 1 gives the size of the corpus and its subcorpora, both in terms of number of raw tokens and unique lemmas.

Table 1. Statistics about the corpus. The first column reports the number of white space-separated entities in the raw text files, the second column the number of unique tokens in the lemmatized text.

Year	Days	Raw Tokens	N. of Lemmas	Size
2019	305	24,141,080	283,570	151MB
2020	306	39,728,203	380,146	250MB

3.2 Pre-processing

36We pre-processed the corpus following a two-step procedure: first we cleaned the texts and then we performed tokenization and lemmatization.

3.3 Usage Change Detection

40We adopted the method from Gonen et al. (2020), introduced in §2.2.1, to detect usage change. This method is perfectly in line with the aim of our study, that is to analyze differences between corpora by detecting words that are used differently across them. The task is defined by the authors as follows: given two corpora with substantial overlapping vocabularies, identify candidate words whose predominant use is different in the two corpora. The expected result is a ranked list of words, from the one that is most likely to have changed, to the least likely.

41In other words, Gonen et al. (2020) propose to work in the shared vocabulary space with the underlying intuition that words whose usage changed are likely to be interchangeable with different sets of words, and so to have different neighbors in the two embedding spaces. Their algorithm represents each word in a corpus as the set of its top k nearest neighbors. Then, it computes the score for word usage change across corpora by considering the size of the intersection of the two sets.

42Words with a smaller intersection are ranked higher as candidates for usage change. It is important to note that this method only considers the words in the intersection of both vocabularies, as words that are rare in one of the corpora are easily spotted by using their frequency in the two spaces, and do not fit the definition of usage change according to the authors. This method does not require extensive filtering of words; they instead filter words based on frequency, using a large value of \(k=1000^4\), because large neighbor sets are more stable.

43The advantages of this method are plenty: (i) simplicity, since there is no need for space alignment, hyperparameter tuning and vocabulary filtering; (ii) interpretability, provided by the intuitive ranking system used for providing the results; (iii) locality, with the score for each word determined only by its own neighbors (whereas in the projection methods the similarity depends on the projection itself, which implicitly takes into account all the other words and their relations); (iv) stability, because the method produces similar results across different embeddings trained on the same corpora (this is not the case for alignment-based approaches). As the authors note, however, their method still has some limitations: it assumes high quality embeddings, and so, a large corpus. This is somewhat mitigated by the fact that the minimal input required is raw text without the need of annotation. In fact these are just minimal requirements; as already mentioned in §3.2, we used lemmatized text, where each token was substituted for its lemma. Another limitation is the fact that like previous approaches, this method does not guarantee that the detected words have indeed undergone usage change but it at least aims to highlight candidates for later human verification and interpretation (Gonen et al. 2020).

44To adapt this method to our Italian corpus we firstly collated all the text of the two sub-corpora in two different text files, one for each year. The algorithm was then applied to these files, both in unlemmatized and lemmatized form, without altering any of its original parameters. The algorithm then computed the word2vec embeddings for both input files and returned the list of top-100 words which most likely have undergone usage change. Despite the imperfect results of lemmatization, we decided to focus our analysis on lemmatized text in order to reduce the problems connected to data sparsity and the morphological complexity of the Italian language. To visualize the output, we used t-SNE (t-distributed stochastic neighbor embeddings) as implemented in the method provided by Gonen et al. (2020) for the top-10 candidates, but we scaled down the number of represented neighbors of each word to enhance readability (see figure 1 as an example of the visualization).

4. Results and Discussion

45In this Section we present and analyses the results obtained by the application of the usage change detection algorithm to the two sub-corpora.

4.1 Top-10 detected words

46Table 2 lists all the top-10 neighbors in the 2019 and 2020 vector spaces for the top-10 candidate words detected by applying the Gonen et al. (2020) algorithm to our data.

Table 2. Neighbors of top-10 words. The upper line lists top-10 neighbors in the 2019 sub-corpus, while the lower line lists neighbors in the 2020 sub-corpus. Lemmatization errors, which are almost always easily understandable by a native speaker, are marked with a star (*) symbol.

	Neighbors
positivo	desiderato, decisivo, ottimista, lato, recensione, gradevole, normalità, attitudine, scaturire, deleterio
	tampone, positività, 40ena, contagiare, sintomatico, vaosta, asintomatico, \(\downarrow\), sierologico, infetto
virus	vulnerabilità, bios, resettare, bug, chiavetta, terminale, criptare, diabete, cancro, scansione
	sarscov2, coronavirus, covid19, covid, ebola, contagio, patogeno, infettare, sars, aerosol
intensivo	vegetale, integratore, combustione, cbd, thc, molecola, nicotina, proteina, intestino, hiv
	icu, rianimazione, ospedalizzato, ricovero, ricoverare, ricoverato, ospedalizzare, intubare, ospedale, ospedalizzazione
testare	arduino, centralina, plugin, usato, apportare, alterare, simulare, falla, fungere, lsd
	tampone, sintomatico, testato, asintomatico, ospedalizzare, ct, diagnostico, tamponare, screening, infetto
guarire	psicoterapeuta, astinenza, prescrivere, chirurgo, dottoressa, intestino, deprimere, malessere, psicologo, involontariamente
	guarito, ospedalizzare, asintomatico, guarigione, clinicamente, ricoverato, contagiare, decesso, decedere, ospedalizzato
bla	professorone, la’, sminuire, inerzia, passere, *ripartare, umiltà, contrattuale, pigrizia, *dirtelare
	superfluo, trito, figliare, *diciamocelare, egoistico, famigliare, etc, *smettilare, moralista, stufo
eco	intitolare, convegno, studentesco, seguace, sostenitore, invocare, *buongiorne, vocabolario, *altaforte, rivista
	asimov, pascolo, collana, lovecraft, mattone, divulgativo, *murakamo, microscopico, orwell, philip
leve	*capacitare, sara, immortale, magnetico, aggrappare, rivoluzionario, cosmico, *vadere, lentezza, nano
	inculare, perverso, rum, ftw, dinamico, trucchetto, proletariato, arrampicato, toppa, nervo
influenza	interferire, migratore, venezuelano, decisivo, presidenziale, caratterizzare, coinvolgimento, connotazione, competitività, oppressione
	influenzale, *polmonito, stagionale, sintomatologia, *polmonita, complicanza, ebola, mers, morbillo, contagiosità
gene	amministrare, neurone, tribù, bravura, portatore, risaputo, composto, azionista, squilibrio, prole
	dna, nomea, ariano, innato, land, sarscov2, mutazione, ceppo, cromosoma, mediocrità

47The top-10 candidates can be divided in three broadly defined classes according to their nearest neighbors, and to how they have changed between the two sub-corpora. The fist class, narrowing, denotes candidates which changed from a more general usage, to a more specific one, but which is already present in the language. This is the case with positivo, intensivo, guarire, gene. The second class, shift, refers to those candidates which usage switched between two different semantic fields. Candidates such as virus, testare, influenza fall under this category. The last class, not informative, comprises those candidates which neighbors in both corpora, either due to their low frequency or noise, do not allow for a clear indication of usage. bla, eco, and leve are examples of not informative candidates proposed by the algorithm.

49In the 2020 dataset, positivo has indeed narrowed its use to the medical semantic field: 8 out of its top-10 nearest neighbors are clearly connected with medicine and the pandemic. tampone (“swab”), positività (“positivity”), 40ena (an abbreviation of quarantena, “quarantine”), contagiare (“to infect”), sintomatico (“symptomatic”), asintomatico (“asymptomatic”), [test] sierologico (“antibodies test”) and infetto (“infected”) all indicate a meaning of positivo as pertaining to medicine: a diagnostic response that confirms the formulated hypothesis, unfavorable to the tested subject, who, by extension, is also called positivo.

50The other two nearest neighbors (NN) of positivo, vaosta and \(\downarrow\), are less interpretable, however, can still be connected to the pandemic: the former is a shortening of Valle d’Aosta (“Aosta Valley”), the smallest Italian region, bordering France. vaosta occurs with the names of other regions in the daily tables listing COVID-19 cases and deaths. As to why only vaosta figures as a neighbor of positivo there is no evident clue. The symbol \(\downarrow\) is present for the same reason: it occurs frequently in the periodic pandemic reports. The connection with the tallies of the pandemic is confirmed by the presence of numbers in the neighborhood of positivo.

51Figure 1 gives the visual representation of the top-30 nearest neighbors of positivo in the two spaces as an example of the plots created by the detection algorithm. Cyan is used for the 2019 neighbors, while pink is used for the 2020 ones. Shared neighbors are marked in purple. As can be seen for the visual representation, the only neighbors in common between the two sub-corpora are positività and negativo, marked in purple in the plot. The collocations of these two words in the 2019 sub-corpus reveal that they are almost never used in a medical sense.

54The usage of guarire (“to heal”) also underwent narrowing, from a broader use in the medical semantic field, to a more restricted use regarding intensive care. In the 2019 space, the neighbors of guarire feature words such as psicoterapeuta (“psychotherapist”), deprimere, (“to depress”) and psicologo (“psychologist”) which pertain to mental health and therapy. astinenza (“abstinence”, or “withdrawal”) and malessere (“discomfort”) can also be connected to this scope. chirurgo (“surgeon”), dottoressa (“female doctor”) and prescrivere (“to prescribe”) are more general. intestino and involontariamente (“unintentionally”) seem incidental and are not particularly informative.

55In the 2020 sub-corpus, the neighborhood of guarire is focused on hospital and intensive care: ospedalizzare, ospedalizzato, ricoverato, contagiare, and asintomatico return as neighbors. These words are common also in the surroundings of other pandemic-related words. Other terms are both positive, like guarito (“healed”) and guarigione (“healing”), and negative, like decesso (“death”) and decedere (“to die”). These last two are commonly used in a more formal setting, like news, so are likely connected to guarire because of the reports on cases, deaths, and recoveries from COVID-19. This is confirmed by the presence of numbers in the larger neighborhood. clinicamente (“clinically”) is more neutral, but still connected to the field of medicine. The only common neighbor between the two spaces is guarigione.

56gene is the last detected word in the top-10 candidates which could be ascribed to the narrowing class. The low number of occurrences does not allow for a coherent embedding representation in both spaces, even if the 2020 one seems slightly better than the one from the previous year’s dataset. While the neighborhood in the 2019 space is quite diverse, in the 2020 one can identify some connections: dna, ariano (“arian”), mutazione (“mutation”), ceppo (“strain”, meaning a variant of e.g. a virus), cromosoma (“chromosome”) relate to the genetic sense of gene. nomea (“reputation”), innato (“innate”), and by contrast mediocrità (“mediocrity”), loosely hint at sense of “genius”. However, the influence of the pandemic may have prompted more discussion that involved the biological gene: sarscov2 is listed as a neighbor in the 2020 space. Interestingly, there are no shared words in the neighborhoods for the two spaces.

58In the 2020 sub-corpus, the connection with the pandemic is explicit in the first four neighbors, which are all variations of COVID-19: sarscov2, coronavirus, covid19 and covid. The other neighbors are still correlated with specific diseases, like ebola and sars, and their spread, contagio (“contagion”), patogeno (“pathogen”), infettare. aerosol refers to the fact that COVID-19 is spread by droplets of saliva in the air. Even looking at a larger list of neighbors, there are no words related to the computer sense of virus during 2020. Common neighbors between the two corpora are infettare, contagioso, infezione, hiv.

60In the 2020 sub-corpus, testare is well represented and connected to the pandemic: tamponare arises as a specialized version of testare, with the specific meaning of “to test with a swab (tampone)”. This is connected to screening, a loanword that refers to medical testing. The acronym ct is used interchangeably for COVID-19 test and commissario tecnico (“sports coach”). diagnostico (“diagnostic”) seems to be used in connection with tampone and other testing methods and equipment, confirming the connection to the pandemic. sintomatico (“symptomatic”), asintomatico (“asymptomatic”), infetto, and ospedalizzare are clearly connected to the virus.

62The word influenza has shifted its use, too. Its neighbors in the 2019 sub-corpus point at the sense of “action done by one thing or person on another one”: interferire (“to interfere”), coinvolgimento (“involvement”), and caratterizzare (“to characterize”) are quite indicative in this sense. Some other hint at a more geo-political use of the same sense of influenza: presidenziale (“presidential”), venezuelano (“Venezuelan”), oppressione (“oppression”), and *migratore (maybe migratoria “migratory”, as in flussi migratori). Collocations shows that presidenziale is used referring to American politics, while venezuelano refers to the Venezuelan crisis in the beginning of 2019. connotazione (“connotation”), competitività (“competitivity”) and decisivo (“decisive”) are the remaining neighbors.

63In the 2020 sub-corpus, influenza shifts completely to a medical usage: some of its neighbors refer to diseases, such as ebola, mers, morbillo, *polmonito and *polmonita (correct form: polmonite, “pneumonia”). stagionale is coming from influenza stagionale (“common flu”), while influenzale (“flu-related”), sintomatologia (“symptomatology”), complicanza (“complication”), and contagiosità (“the ability or state to be infective”) relate to the effect of influenza. Even in the larger neighborhood there is no trace of the usages attested in 2019. Moreover, there are no neighbors in common between the two datasets.

64bla is the first of the three candidates which neighbors are not informative with respect to usage change. bla, usually repeated two or three times (bla bla), is a common onomatopoeia indicating useless conversations or futile chatter. The frequency of this word grew only slightly, in line with the overall increment in the size of the data. In both spaces the neighbors of bla are not informative. The only ones that can be somewhat connected with the common use of bla are found in the 2020 space: trito (“crushed”), can be used in the idiomatic expression trito e ritrito (“grounded and grounded again”) meaning something that is used or said too much, commonly known, prosaic and trivial. Others are superfluo (“excessive”) and etc (abbreviation of “etcetera”). The neighbors show some lemmatization issues: *ripartare instead of ripartire (“to start again”), *dirtelare most certainly derived from dirtelo (“to say to you”, -lo is an enclitic second person pronoun), *diciamocelare from diciamocelo (“to say to ourselves”, often said with the sense of “let’s be clear to/real with ourseves”) and *smettilare from smettila (“stop it!”).

65Also not very informative are the neighbors of eco, which can indeed be the common noun for “echo”, a reflection of sound; or, if one looks at its nearest neighbors, the famous writer Umberto Eco, at least in the 2020 sub-corpus. However, in the 2019 dataset the neighborhood is less clear even if they are clearly related to literature: for example, pascolo referring to poet Giovanni Pascoli and murakamo referring to Japanese writer Murakami Ryū. Other words connected with the literary world are collana, a series of books, mattone, a long and tedious book (lit. “a brick”), and divulgativo, usually a science or otherwise academic book intended for the general audience. philiph is referring to sci-fi author Philip K. Dick, as suggested by the presence of other writers of the same genre like Isaac Asimov (asimov) and George Orwell (orwell).

66leve is maybe the least informative entry in this list: it is a lemmatization error, since the lemmatizer did not use the citation form, the singular leva (“lever”). In addition, in some cases leve can derive from the name of Holocaust survivor and writer Primo Levi. The neighborhood of leve in both corpora emerges from limited interactions in peculiar discussions: just to cite one, the first neighbor of the 2020 list, a profanity that literally means “to sodomize”, is due to an exchange between two users on day 261 of 2020, where the word leve was used about 20 times.

67The presence of these three cases, eco, bla, leve, can be attributed to their inaccurate embedding representations, which are in turn due to their scarce frequency. Their word embeddings are overly influenced by some peculiar context of use, which renders their neighborhoods less informative to define their usage. Frequency-wise all three share a pattern of just a slight increase, in line with the growth of the data for the second corpus. A preliminary analysis of the other results in the top-100 detected words shows that this can be the case for many other relatively low-frequency words.

68Overall, in these top-10 candidates there are 6 informative results (positivo, virus, intensivo, testare, guarire, influenza, gene), and 3 less informative results (bla, eco, leve). These last three candidates have imprecise embedding representations, due to their low frequency of use. Also, wrong lemmatization may have had an impact. However, among all the 200 neighbors of the words listed in the top-10, just 11 are wrongly lemmatized, and even in these cases the errors are intelligible for a native speaker.

69Sometimes, the embedding representations of the top-10 candidates show some less specific neighbors, at least in the 2019 space, where their frequency is lower. It is interesting to note, however, that all the informative candidates had a noticeable growth in relative frequency in the 2020 sub-corpus. As seen with informative outputs, if the changed word is well represented, it is also detected by the algorithm. Table 3 gives frequency data for the top-10 candidate words proposed by the algorithm.

Table 3. Absolute and relative frequencies of top-10 words. Relative frequencies are calculated as the number of occurrences of a word divided by the total number of tokens in the lemmatized corpus for a specific year.

	Absolute	Frequency	Relative	Frequency(%)	Increase (%)
	2019	2020	2019	2020
positivo	2969	13688	0.52169	1.78806	1.26637
virus	270	16632	0.04744	2.17263	2.12519
intensivo	203	3716	0.03567	0.48542	0.44975
testare	439	3347	0.07714	0.43722	0.36008
guarire	238	2454	0.04182	0.32057	0.27875
bla	302	489	0.05307	0.06388	0.01081
eco	256	436	0.04498	0.05695	0.01197
leve	269	369	0.04727	0.04820	0.00093
influenza	996	4226	0.17501	0.55204	0.37703
gene	354	524	0.06220	0.06845	0.00625

70Even if the top-10 results are not totally devoid of problems, the output for an unlemmatized corpus seems worse, with only five terms (virus, bla, vaccino, positivo, and positivi) having some significant increase in frequency. As seen in the previously discussed words, low frequency leads to imprecise representations built only on a handful of particular discussions. This naturally leads to radically different neighborhoods over the two corpora, tricking the algorithm into thinking that these cases are instances of usage change. It can be argued that these words have in fact undergone usage change, that is, they have changed contexts of use, but their neighbors often give no clue to their meaning, calling into question their validity. While far from being perfect, lemmatization seems to smooth out at least some of these cases. Pertaining to specific results in the unlemmatized top-10, bla presents the same problems as explained above; positivo and positivi are two inflected forms of the same lemma (thus of low informative value when searching for changes in language use), but overall correctly labeled as changed; peste and vaccino have a clear enough representation only in the second sub-corpus; fico has sparse usage in both datasets, but it is clear only in the first one. capitano is the only case with decreased frequency: in the 2019 space it is related almost exclusively with the discussion around the incident involving NGO ship captain Carola Rackete and her antagonist, then Interior Minister Matteo Salvini, sometimes nicknamed “Il Capitano”. In the 2020 space many neighbors point to càpitano as a verb (“they happened”, as opposed to capitàno, “captain”).

71As for the top-10s detected with the method based on the alignment of the vector spaces (AlignCos, Hamilton, Leskovec, and Jurafsky (2016)), in both cases they are much worse than those found by the Nearest Neighbors method (Gonen et al. 2020): in the lemmatized version, only intensivo is significative, all the others being cases of representations skewed by low frequency. In the unlemmatized version two candidates are somewhat valid: fontana changed its use from “fountain” in the 2019 space to referring to Attilio Fontana, the governor of Lombardy, the Italian region hit the worst by the pandemic. The other significant candidate is vaccino, which has a low frequency in 2019 and an obviously good representation in 2020.

4.2 Other relevant words

72Other words in the top-100 candidates for usage change are relevant. These and their neighbors are listed in Table 4 and are briefly discussed below.

Table 4. Neighbors of other relevant words in the top-100. The upper line lists top-10 neighbors in the 2019 sub-corpus, while the lower line lists neighbors in the 2020 sub-corpus. Lemmatization errors, which are almost always easily understandable by a native speaker, are marked with a star (*) symbol.

	Neighbors
vaccino	omeopatico, prescrivere, biologo, dermatologo, ricoverare, cancro, esente, omosessualità, *asile, prescrizione
	antinfluenzale, antivirale, pfizer, morbillo, vaccinato, oxford, anticorpo, somministrare, cavia, gregge
riaprire	avviare, portone, archiviare, spostato, *accendare, riprovare, quirinale, rimbalzare, sbucare, avvio
	riapertura, richiudere, ripartenza, palestre, maggio, allentare, restrizione, *asile, allentamento, *contage
tappeto	muffa, vernice, soffitto, siringa, lavandino, cemento, ombrellone, rame, tavoletta, pallino
	testare, tampone, sierologico, screening, capillare, prericovero, isolare, quarantene, molecolare, test
normalità	maschilista, retrogrado, omofobia, immaturo, socialmente, bigotto, ansioso, omosessualità, geloso, deleterio
	riaprire, andata, allentamento, parvenza, gradualmente, autunno, riapertura, intimità, ricaduta, esodo
morto	persecuzione, attenuante, portatore, perseguitare, ignoto, terrorizzare, rapire, concentramento, stupratore, molestia
	ospedalizzato, decesso, contagiato, infettato, ricoverato, *contage, ospedalizzare, decedere, *muoiare, contagiare
curva	ultras, tifoso, tifoseria, vicolo, tir, marce, interista, lazio, hamilton, *filmetro
	esponenziale, appiattire, curvo, contage, accelerazione, pendenza, impennare, picco, r0, progressione
isolare	inadatto, interferire, sabotare, dovunque, volente, fomentare, deviare, bollare, nolente, quotidianità
	circoscrivere, *focolao, quarantenare, sintomatico, blindare, rintracciare, *diffondare, asintomatico, tamponare, contagio
ondata	antisemitismo, migratore, sovranismo, generazionale, retorica, buonismo, berlusconiano, reazionario, consumismo, apice
	epidemia, *focolao, impennata, lockdown, esodo, autunno, scongiurare, riapertura, pandemia, *tsuname
terapia	omeopatico, prevenzione, malessere, allergia, raffreddore, relazionale, stigma, erezione, molecola, ansioso
	rianimazione, ricovero, ospedalizzare, intubare, icu, ospedalizzato, farmacologico, ospedalizzazione, ricoverato, ormonale
rosso	guancia, muffa, romeo, cera, hamilton, mela, dannato, illuminare, adesivo, cappello
	zona, grigie, nembro, lodigiano, tonalità, *mantovo, tartufo, codogno, stemma, *cremono
scorta	saviano, divisa, domiciliare, proiettile, equipaggio, rinchiudere, digos, immunità, rimozione, vendicare
	rifornire, scarseggiare, *vivero, igienizzante, *amuchino, introvabile, assaltare, monouso, sottovuoto, ricambi
chiuso	finito, tappo, elefante, fogna, circolo, serratura, sbraitare, sfortunatamente, *tienire, cassetto
	blindare, palestre, richiudere, affollare, riapertura, blindato, battente, scappato, ammassare, confinare
fontana	basilica, sant’, cimitero, sottosegretario, virginia, passeggiare, *lucco, riva, villa, portone
	*gallero, cirio, *zaio, gallera, *bonaccino, *formigone, assessore, *camico, *umilansifonsifere, lombardia
emergenza	abitativo, naufrago, irregolarità, *rimpatro, rifugiare, irregolare, incidente, interruzione, *lampeduso, generatore
	pandemia, emergenziale, pandemico, epidemia, fronteggiare, proroga, imprevista, commissariamento, ripartenza, *covere
paziente	lucidità, malessere, dolore, nutrizionista, ossessivo, allergia, seduta, ansioso, erezione, perizia
	rianimazione, ospedalizzare, intubare, 38enne, anestesista, complicanza, *polmonita, oncologico, ricoverato, diagnosticato
malato	frustrato, sindrome, isterico, risvegliare, disordine, omeopatico, ossessivo, immaturo, retrogrado, daenerys
	oncologico, ammalato, infetto, ospedalizzare, contagiato, diagnosticato, immunodepresso, *ammalere, ricoverato, contagiare

73Notable cases of usage narrowing include vaccino, terapia (“therapy”), malato (“ill”), and paziente (“patient”). The neighbors of these words change from generally mild connotation (e.g. terapia is used in the 2019 sub-corpus with omeopatico “homeopathy”, allergia “allergy”, and raffreddore “common cold”), to a more severe one, related to the pandemic (e.g. terapia is used in the 2020 sub-corpus with rianimazione, intubare, and icu).

74Other instances of narrowing, or change to a specific usage, are those of curva (“curve“), rosso (“red“), and isolare (“to isolate“), which becomes to be specifically connected to the pandemic. Among the latter’s neighbors quarantenare (“to quarantine“) is found. The case of quarantenare is interesting, despite very low occurrences. This verb is found a dozen of times in the 2020 space, but only one in the 2019 one. It is also not present in the Treccani dictionary, not even as a neologism. quarantenare is used in the sense of “to quarantine“. The 2019 instance refers to the process with which Reddit administrators hide and close a subreddit deemed to be harmful or not in line with the platform’s rules. The sense in 2020 is similar, but the term is used always in connection with the pandemic: neighbors include quarantena (lemmatized as *quaranteno), fiduciario (“fiduciary“, in the more formal expression “quarantena fiduciaria“), infettare (“to infect“), autoisolamento (“self-isolation“), isolamento (“isolation“), precauzionale (“precautionary“).

75The neighbors of normalità (“normality“), tappeto (“carpet“), morto (“dead“), ondata (“wave“), scorta (both “security detail” and “stockpile”), emergency (“emergenza“) point to shifts in usage. normalità shift its usage from gender issues to the pandemic; tappeto moves from homes to “carpet testing“; morto’s usage chages from crimes to pandemic deaths; ondata from a geopolitical usage to describing the successive waves of the disease; scorta switches from “security detail“ to “stockpile“; and emergenza refocuses from the migrants crisis to the pandemic.

76Table 5 gives frequency data on other relevant words in the top-100, The words are ordered according to their position in the list proposed by the algorithm (not always contiguous), as already seen for the top-10 candidates; words which have indeed experienced change have also a noticeable increase in relative frequency, albeit less than for the terms in the top-10.

Table 5. Absolute and relative frequencies of other relevant words in the top-100. Relative frequencies are calculated as the number of occurrences of a word divided by the total number of tokens in the lemmatized corpus for a specific year

	Absolute	Frequency	Relative	Frequency (%)	Increase (%)
	2019	2020	2019	2020
vaccino	710	5677	0.12476	0.74158	0.61683
riaprire	311	3775	0.05465	0.49313	0.43848
tappeto	226	787	0.03971	0.10281	0.06309
normalità	265	1185	0.04656	0.15480	0.10823
morto	2201	7426	0.38675	0.97006	0.58331
curva	692	1670	0.12159	0.21815	0.09656
isolare	240	994	0.04217	0.12985	0.08767
ondata	233	1750	0.04094	0.22860	0.18766
terapia	738	4857	0.12968	0.63447	0.50479
rosso	2791	6457	0.49042	0.84348	0.35306
scorta	441	1047	0.07750	0.13677	0.05928
chiuso	1856	7148	0.32613	0.93374	0.60762
fontana	253	1264	0.04446	0.16512	0.12066
emergenza	1131	5399	0.19873	0.70527	0.50654
paziente	873	4125	0.15340	0.53885	0.38545
malato	954	3289	0.16763	0.42964	0.26201

5. Conclusions

77This work started from the hypothesis that a global crisis like the COVID-19 pandemic could impact language use. This was verified with computational means, leveraging both theoretical linguistics and NLP techniques. A corpus was created by scraping online text from the Italian Reddit community. The data was collected for the days between January 30 and November 30 of both 2020 and 2019, creating two sub-corpora. The raw text was then cleaned and lemmatized to allow further analysis. This dataset alone, both raw and preprocessed, could be a useful resource for other applications and it is publicly available. Future work may focus on the extension of the dataset’s timeframe.

78This research follows previous work and methodology in the field of computational language change detection, focusing on short-term usage change. To the best of our knowledge, this is the first work of this kind done for Italian, both in the field of COVID-19-related linguistic research and short-term language change detection. The latter is carried out with the neighborhood-based method outlined in , previously untested for the Italian language. The choice to lemmatize the data allowed to evaluate the impact of this pre-processing step on the method.

79The initial research questions were the following: has the pandemic impacted the usage of the Italian language? Can this impact be detected with computational means? In fact, the manual analysis of the results produced by the algorithm showed that, as expected, some degree of usage change has occurred. The computational method used to detect it has shown to be quite solid also for Italian. Our experiments have shown that lemmatization as a pre-processing phase is important for Italian, given that without this step the results were less informative, although it remains a challenging task for an inflectional language such as Italian. Future work may involve an improved lemmatization.

80A similar work for English by adopts different methodological choices, such as the alignment approach of to detect usage change, and the analysis of a selection of predefined keywords. Its results are therefore not comparable to ours. Nevertheless, it shows that, also for English, a shift in usage is detected towards COVID19 and healthcare related words.

81It remains to be seen if the change in usage will translate in actual lasting mutations in the language. The rise of of a new word as in the case of tamponare “to perform a swab“, may be more significant and enduring than the already existing, but domain-specific sense of positivo “a diagnostic response that confirms the formulated hypothesis, unfavorable to the tested subject“, which became widespread due the pandemic. These cases seem more typical of short-term usage change: more specific, or different senses of a word increase their use, and overtake the more established senses due to a plethora of factors, in this case the pandemic. The surge of these senses may well be temporary.