dataesr/scientific-paragraphs-categorization huggingface dataset:
COVID-19 Mask Stance Dataset A total of 8,795,633 tweets have been extracted for the one-year period starting from 9 January 2020-8 January 2021 and have been included in the entire dataset. After performing the discarding of the duplicated tweets and retweets, a cleaned dataset containing 1,692,437 tweets has been obtained. As the information extracted from both datasets is useful in better shaping the evolution of the users' opinion regarding the use of marks in times of COVID-19, the analysis performed in Section 5 is made in parallel on the two datasets. The number of extracted tweets in each month of the above-mentioned period is presented in Table  2. It should be noted that as the first tweet regarding the use of masks as a protection measure against the was posted on 9 January 2020, in the following, we will divide the considered period into 12 equal months (noted with Mi, where i is the index of the month), each month is starting in the 9th day of the calendar's month, and it is ending on the 8th day of the next calendar's month.
In this context, the tweets mentioned the authorities' recommendations of not wearing a mask as a precaution measure and the fact that panic has conducted to the increase in mask purchases, with a visible effect in their price increase. As for the first retweets, it has been observed that it has taken up to 5 days from the initial tweet until the first retweet has been posted (Table  4 ). As for the second and third retweets, they are both referring to a tweet that mentioned that the upcoming flu season might be unpleasant and advises the readers to start wearing a mask. As mentioned in the methodology section, from the entire dataset, a random sample of 29,613 tweets (representing approximately 1. 75% of the cleaned dataset) was extracted and annotated by three persons. Some examples of tweets in each of the three categories are presented in Table  5 , while the distribution of the tweets into the three considered categories (in favor, neutral, against) is given in Table  6.
COVID-19 = 60-140 nanometers in diameter. Smoke = 2. 5-20 nanometers in diameter. Pollen = 90-100 nanometers in diameter. Mask = 300-320 nanometers in diameter. Your mask is an incubator for the very thing your trying to prevent https://t. co/KXZc8xEXf9 It can be observed that most of the tweets in the annotated dataset belong to the in favor category, followed by the neutral and against categories. A balanced dataset is then extracted from the annotated dataset, containing 9426 tweets equally distributed among the three categories. For determining the best classifier for the mask dataset, five classifiers have been considered, and their performance has been evaluated using four indicators (please see Section 3). The considered classical machine learning algorithms are Multinomial Naive Bayes (MNB)  [66] , Random Forest (RF)  [67]  and Support Vector Machine (SVM)  [69] , while the selected deep learning algorithms are Bidirectional Encoder Representations from Transformers (BERT)  [71]  and Robustly Optimized BERT Pretraining Approach (RoBERTa)  [72].
Vectors obtained in the case of the l1 norm have the sum of the elements square equal to 1, while in the case of the l2 norm, the sum of the absolute values of the elements is 1. The performance of the classifier has been evaluated both while keeping all the features and when limiting the maximum number of features to 1500, 2000 and 3000, respectively. Given the fact that stop words can sometimes affect the performance of the classifiers, we have also analyzed the results of removing the common English stop words, using the list included in the Natural Language Toolkit Library (https://nltk. org/, accessed on 13. 01. 2021) (NLTK)  [64] , as well as removing only corpus-specific stop words using as document thresholds 0. 5, 0. 75 and 1.
As recommended by Liu et al. [72] , the considered values for the hyperparameters stayed the same as in the case of the BERT model, except for the learning rates, for which the considered values were 10 , 2 10 and 3 10. The best values for the parameters associated with each classifier have been determined using a grid search approach. Afterwards, a 10-fold cross-validation evaluation has been performed, during which the dataset has been randomly divided into 10 folds  [75]. The model is then trained on 9 folds, while the remaining fold is used for validation. The procedure is repeated 10 times, and the results from the folds are then averaged. The obtained results for the above-mentioned classifiers are presented in Table  8 , where the accuracy of the best-performing classifier in each category has been marked using bold.
COVID-19 Mask Stance Detection The results of the mask stance detection performed through the use of RoBERTa  [72]  are discussed in this section on each of the two considered datasets (cleaned and entire). We decided to present the results on both the datasets as we believe, similarly to D'Andrea et al. [61] , that the retweeting action is a sign of support for the information provided in the original tweet, showing that the person retweeting a tweet shares the same point of view with the content of the message.
Mask Stance Detection on Cleaned Dataset Considering the results of the stance analysis on the cleaned dataset, it has been determined that from the 1,692,437 tweets comprised in this set, the highest number of tweets have been encountered in the in favor category (821,522 tweets, representing 48. 54%), followed by neutral (675,693 tweets, 39. 92%) and against (195,222 tweets, 11. 54%). The daily evolution of the in favor tweets is depicted in Figure  3. It can be observed that the lowest number of in favor tweets was in the first month of the considered period (6779 tweets). Since then, the number of in favor tweets increased, reaching the highest value of posted tweets in M7 with 110,982. After this increase, in the following months, M8-M10, the number of in favor tweets stabilized, having an average of 58,329 tweets/month, while in M11, the number of in favor tweets increased to 163,672 tweets.
Mask Stance Detection on Entire Dataset On the entire dataset, after performing the stance analysis, we can observe that most of the tweets are in the neutral category (4,184,300 tweets, representing 47. 57%), followed by the in favor category (4,004,533 tweets, 45. 53%) and the against category (606,800 tweets, 6. 90%). The daily evolution of the in favor tweets is presented in Figure  7. Based on the figure, it can be observed that the first two months considered have a comparable number of in favor tweets, being the lowest among the months in the analyzed period: M1 with 75,124 tweets and M2 with 77,650 tweets. The highest number of in favor tweets was recorded in M6 (791,092 tweets), M7 (683,752 tweets) and M11 (498,670 tweets)-the same months in which the number of in favor tweets in the cleaned dataset surpassed 100,000 tweets/month.
The evolution of the number of neutral tweets was smoother than in the case of in favor tweets in the entire dataset-Figure  8 , as in all the months (except for M7 with 641,830 tweets), the number of neutral tweets was under 500,000 tweets/month. The against tweets evolution is depicted in Figure  9. The highest number of against tweets has been recorded in M11 (120,035 tweets), representing 19. 78% of the against tweets, followed by M7 (88,083 tweets), representing 14. 52%, and M12 (66,362 tweets), representing 10. 94%. As observed even in the case of the against tweets in the cleaned dataset, the last months of the considered period have been characterized by an increase in the number of against tweets-approximatively one-third of the number of against tweets being recorded in M11 and M12.
Mask Stance Detection Evolution in Both Cleaned and Entire Datasets The evolution of the in favor, neutral and against tweets in the cleaned dataset is depicted in Figure  11 , while the proportion of tweets by category in each month is presented in Figure  A1. Based on the figure, it can be observed that in all the considered months, the number of the against tweets is the lowest in comparison with the other two considered categories, increasing in the last 2 months of the analyzed period. As for the in favor and neutral tweets evolution, in the first 4 months of the analysis, the number of neutral tweets (192,409 tweets) exceeded the number of in favor tweets (122,224 tweets). In M5, the number of in favor and neutral tweets is almost the same (55,317 vs. 55,228 tweets), while starting from M6, the number of in favor tweets (643,981 tweets) exceeds the number of neutral tweets (428,056 tweets).
Similar to the cleaned dataset, in the case of the entire dataset, the number of against tweets does not overpass the number of in favor and neutral tweets in none of the considered months-Figure  12. The proportion of tweets in the entire dataset by category in each month is presented in Figure  A2. Furthermore, it can be observed that the number of neutral tweets is above the number of the in favor tweets in most of the months, except for M6, M7, M8, M10 and M11.
Analyzing Mask-Wearing Opinions As in the tweets' stance detection, periods with increased activity have been identified; in this section, these periods have been extracted and analyzed along with the days with the highest number of in favor, neutral and against tweets. In order to perform this analysis, the tweets from the cleaned dataset have been considered. For the selected periods and days, the news posted online has been extracted using the "News" section provided by Google. The list of news in each period or day has been refined by searching the keywords "COVID" and "mask". The most important news has been put in connection with the stance of the tweets in the periods and days taken into account. Additionally, an n-gram analysis has been performed for validating that the selected news is representative for the respective period.
Periods with High Tweet Activity In order to identify the periods with high tweets activity, an analysis has been performed on the cleaned dataset. As a result, the periods with an average of more than 9500 tweets/day have been selected. For determining the start/end day of the period to be selected, an additional constraint has been imposed, namely the number of tweets in the start and end day to be higher than 9500 tweets. In the case in which the imposed constraints are met for a day but not for any previous or following days in the analyzed dataset, the period will include only that day. A total of eight periods (noted with Pi, i = 1, …, 8) have been identified that match the imposed constraints. These periods are analyzed in the following.
P1: 4 April 2020 The first considered period consists of a single day, April 4, 2020, in which there are 10,704 tweets reported in the cleaned dataset. As for the entire dataset, a number of 45,853 tweets have been collected. The proportion of the tweets expressing each of the considered three stances can be observed in Figure  13. Based on the information in Figure  13 , it can be observed that in this period, most of the tweets are in the neutral category-64% in the cleaned dataset and 75% in the entire dataset. The proportion of in favor tweets has represented almost one-third of the tweets included in the cleaned dataset (31%), while in the entire dataset, which also contains the retweets, the percentage has been smaller (22%). Only 5% of the tweets in the cleaned dataset and 3% of the tweets in the entire dataset have been classified in the against category.
P2: 2 July 2020 The second period considered in the analysis is composed from a single day, July 2, 2020, when 9845 tweets have been recorded for the cleaned dataset, with 1550 tweets more than the previous day, and 78,461 for the entire dataset, with 36,647 more tweets than the previous day. In terms of stances, it has been observed that in the cleaned dataset, most of the tweets posted in this day are in the in favor category (56%), while 36% are in neutral and 8% in the against category, as shown in Figure  14. As for the entire dataset, the proportion between the in favor and neutral tweets is almost the same (48% vs. 47%), while the against tweets only represent 5%. Looking for the news posted in this period, it shall be mentioned that the main news revolved around President Donald Trump's gradual acceptance of mask-wearing (https://edition.
Compared to the previous period, P1, a shift between the in favor and neutral tweets has been observed. In P2, the users have tried more to present their opinion regarding mask-wearing and its advantages when posting on Twitter rather than sharing the news associated with this period. As a result of the increase in the in favor messages, it can be observed that even the against group has become "more vocal", standing for its points of view. Regarding the stance of the tweets, it can be mentioned that most of them were in the in favor category (48% in the cleaned dataset and 54% in the entire dataset), followed by neutral with 42% in both datasets and against with 10% in the cleaned dataset and, respectively, 4% in the entire dataset (Figure  15 ). In the considered period, one of the most influential news was also from the political area where President Donald Trump started wearing a mask in public for the first time (https://www.
latimes. com/politics/story/2020-07-12/infections-soar-trump-finally-wearsmask-will-it-help, accessed on 11. 04. 2021). As observed for the P2 period, the tweeters have been very responsive to the actions and speeches of the political figures, which stimulated to engage in discussion related to their own opinions related to mask-wearing. Compared to P2, in P3, slight changes can be observed in the proportion of the in favor and against tweets in the cleaned dataset, with a diminishing percentage of in favor tweets and an increasing percentage of against tweets. For confirming the effect of the news of President Donald Trump wearing a mask for the first time in public, the most frequent 4-g has been extracted. It has been observed that "mask public first time" was mentioned 976 times, while "trump wears mask public" was mentioned 681 times, and "wears mask public first" was mentioned 603 times.
P4: 29 July 2020-30 July 2020 A new spike in tweets can be seen on 29 July 2020 and 30 July 2020, when the average number of tweets in the cleaned dataset was approximately 11,017 tweets/day, and the average number of tweets in the entire dataset was approximately 89,722 tweets/day. The stance of the tweets is reported in Figure  16. Most of the tweets are in the in favor category (43%) in the cleaned dataset and in the neutral category (53%) in the entire dataset. An important piece of news during this period was the fact that republican Texas congressman Louie Gohmert, who had previously refused to wear a mask, had tested positive for COVID-19. This news animated the Twitter users who have engaged in presenting their opinions related to this situation while stating their own point of view regarding mark wearing in COVID-19 times.
In this context, once again, it has been observed that the tweets are connected with the news. The most frequent 4-g is "wear mask tests positive", mentioned 2100 times, followed by "refused wear mask tests", mentioned 1922 times. The third most frequent 4-g was "gohmert refused wear mask", mentioned 1896 times, while the sixth one was "louie gohmert refused wear", mentioned 1664 times. The bigram "louie gohmert" is also the fourth most frequent one, being mentioned 3688 times, while the fifth most frequent one was "louie gohmert refused", with 1666 mentions. In terms of stance, it can be observed that the in favor category represents 50% of the cleaned dataset, while the neutral category holds 56% of the entire dataset, as shown in Figure  17. dataset, it seems that the news engaged more users who decided to post the information online rather than expressing their own point of view regarding this situation.
The impact of this event on the tweets published is demonstrated by the most frequent trigram in the period-"trump removes mask". It was mentioned 8776 times. Correspondingly, the most frequent 4-g was "trump removes mask upon", mentioned 8367 times, while the most 5-g was "mask upon return white house", mentioned 8233 times. 6. 1. 6. P6: 9 November 2020-24 November 2020 P6 is a period characterized by a large number of days compared to the other periods considered in the study, days in which the average tweets/day was 11,801 in the cleaned dataset and 39,953 in the entire dataset. Another difference observed for this period, aside from its length, is the increased number of against tweets compared to the other periods, namely 17% in the cleaned dataset and 13% in the entire dataset, as shown in Figure  18.
grandforksherald. com/newsmd/coronavirus/6762637-North-Dakota-enacts-statewide-mask-mandaterestrictions-on-businesses-as-COVID-19-outbreak-rages, accessed on 11. 04. 2021). Another piece of news that captured the attention of Twitter users is the publication of a study performed in Denmark, which concluded that masks only provide limited protection to the wearer (https://www. reuters. com/article/us-health-coronavirus-facemasks-idUSKBN27Y1YW, accessed on 11. 04. 2021). This is confirmed by the presence of the bigram "danish study", referenced 1080 times. In terms of stance, the predominant opinion is in favor (55% in cleaned dataset and 50% in entire dataset), followed by neutral (with 30% in the cleaned dataset and 43% in the entire dataset), as shown in Figure  19. The against tweets continue to have a higher percentage than in P1-P5 periods and are comparable to the one recorded in P6 (15% in P7 vs.
17% in P6) for the cleaned dataset. As for the entire dataset, the percentage of tweets with an against stance is significantly reduced compared to P6 (7% in P7 vs. 13% in P6)-Figure  18. During this period, one of the most relevant pieces of news was the United States' president Joe Biden's plan to ask Americans to wear masks during the first 100 days of his presidency (https://www. cbsnews. com/news/biden-call-for-masks-first-100-days-in-office-inauguration/, accessed on 11. 04. 2021). This is emphasized by the fifth most frequent 4-g, "ask americans wear mask", mentioned 284 times, and by the seventh, "masks first 100 days", referred 242 times. The third most popular 5-g, "wear masks first 100 days", mentioned 231 times, is also related to this news. Another popular piece of news related is the obituary of a COVID-19 victim, which criticizes people who refuse to wear a mask (https://www.
newsweek. com/obituary-kansas-covid-victim-who-died-isolation-blasts-anti-maskers-1552332, accessed on 11. 04. 2021). The impact of this news on Twitter is highlighted by the presence of the eleventh most frequent 4-g of "kansas COVID 19 victim", mentioned 219, and by the twelfth most frequent 5-g, "obituary kansas COVID 19 victim", with 204 mentions. The stance analysis shows that for the selected day, the in favor tweets had the highest weight (61%, Figure  20 ) in the cleaned dataset when compared to the P1-P7 periods. The against tweets have had similar percentages in both cleaned and entire datasets as in P7. A piece of news that has drawn particular was the death of congressman-elect Luke Letlow (https://apnews. com/article/louisiana-coronavirus-pandemic-shreveport-bd0de82f39d856ef262f81fd66dec1d8, accessed on 11.
Discussions and Limitations of the Study Based on the extracted dataset for the one-year period since the first tweet related to mask-wearing in the COVID-19 context was posted, it can be observed that the online users of social networks have been interested in the subjects associated with this topic and have contributed to distributing either their own opinion-related to the choice to wear or not wear a mask-or news related to the efficiency of mask-wearing, behavior of public authorities, mask mandates, types of masks, proper use of masks, symptoms associated with a prolonged mask-wearing, etc. The reactions on social networks to the events that surround us in our everyday life have been previously acknowledged in other Twitter studies, especially in the vaccination area  [14, 61, 76]. In terms of stance analysis, it has been observed, as expected, that in the first period, comprising approximatively the first 5 months of the study, the number of tweets in the neutral category is higher than the number of tweets in the in favor and against categories.
Conclusions The present paper considered the one-year period from when the first mask-related tweet on Twitter was posted about mask-wearing in the COVID-19 context and analyzed the evolution of people's opinions on masks by dividing the tweets into three categories (in favor, neutral and against). Based on the classification made through the use of RoBERTa, it can be observed that most of the tweets in the cleaned dataset are in the in favor category (48. 54%), followed by neutral (39. 92%) and against (11. 54%) categories. As for the entire dataset, a switch is noticed between the neutral (47. 57%) and in favor (45. 53%) categories. The against tweets represent 6. 9% of the entire dataset. By connecting the periods of increased tweet activity to the news, we determined that the number of tweets and their content follow the topic of mainstream news.
P3: 12 July 2020-17 July 2020 The 12 July 2020-17 July 2020 period is characterized by an increased number of tweets compared to the previous days, having an average of approximatively 10,443 tweets/day in the cleaned dataset and approximatively 70,097 tweets/day in the entire dataset. Most of the days included in this period have been marked by high tweet-posting activity, with a peak of 93,788 tweets on 15 July 2020, in the entire dataset.
6. 1. 3. 13 P3: 12 July 2020-17 July 2020 The 12 July 2020-17 July 2020 period is characterized by an increased number of tweets compared to the previous days, having an average of approximatively 10,443 tweets/day in the cleaned dataset and approximatively 70,097 tweets/day in the entire dataset. Most of the days included in this period have been marked by high tweet-posting activity, with a peak of 93,788 tweets on 15 July 2020, in the entire dataset.
The current study The present study investigates the relation between home numeracy practices and math skills of a sample of French early elementary schoolers (2 nd and 3 rd grades) who underwent more comprehensive behavioral testing than what is usually achieved in the HNE literature  [10, 30, 41, 46]. Specifically, children were tested on a range of standardized math competences (nonsymbolic quantity processing, symbolic number understanding, transcoding, counting, arithmetic calculation, and arithmetic fluency), as well as on measures of general cognitive functioning. Parents were also tested on a proxy measure of math skills (i. e. , arithmetic fluency) and reported the frequency of informal, formal basic and formal advanced home numeracy (and literacy) practices. This extensive data collection allowed us to address two main questions.
Data analysis First, we tested whether math skills increased with the frequency of home numeracy practices (informal, formal basic and formal advanced) using one-tailed bivariate Pearson correlations. We then used scores on math subtests as outcomes of multiple regression analyses that included the predictors frequency of informal, formal basic and formal advanced practices. To test whether any of the relations observed were explained by other measured variables, we adjusted these regressions for children's overall cognitive functioning, parental SES, parental math skills, and time spent with the child at home. Finally, we performed a series of supplemental analyses in which (i) we controlled for the fact that a variable number of participants were presented with advanced items, (ii) we dissociated present practices with parent from present practices without parent and past practices, and (iii) we assessed the discriminant validity of numeracy practices (by investigating literacy practices).