dataesr/scientific-paragraphs-categorization huggingface dataset:
First, a Real-world COVID-19 Dataset is constructed. Second, four approaches exploiting user profile features and complex network measures are evaluated at the node and the community level. Third, various models are investigated, and the top four models among the best two levels are selected for best features selection using the model's feature importance. Fourth, the best model is further improved using a final set of features. The main contributions of this work are as follows. 1. We Build a twitter-based real-world COVID-19 dataset for fake news detection. This dataset includes distinct features. It is a dataset with communities, which have additional links between re-tweeters and posters in addition to their context. It provides a crucial resource for COVID-19 fake news detection research studies.
7. We give recommendations about the best features and the best approaches. There has never been a similar exploratory study conducted using source-based methods. 8. We perform a comprehensive comparative study with alternative methods. The remaining of the paper is organized as follows. Section II presents preliminaries on automatic fake news detection. Section III discuss briefly related works. Section IV reports the composition of the COVID-19 dataset. Detail methodology of the research is discussed in section V. Feature analysis is conducted in section VI. Section VII discusses the adequacy of various classification approaches to the problem. Section VIII presents fake classification modeling including model selection and features selection. Section IX starts digging the best two approaches further in detail.
IV. COMPOSITION OF COVID-19 DATASET We took 75 news articles related to COVID-19 (35 False and 40 True) from the Poynter  [74] , Politifact, and Lead Stories websites for this study. These news pieces are categorized into various types of disinformation such as false news, fake news, incomplete, false news, mislead, and so on, by specific websites. All these fake news about COVID-19 labeled as false, mostly false, pants on fire, misleading, and inaccurate by the fact-checking websites are considered fake news in our study. However, all the items of news labeled true or mostly true by fact-checking websites are labeled as True news in our study.
A. GETTING TWEET INFORMATION USING HOAXY The labeled items of news collected from fact-checking websites are provided to Hoaxy  [75]. It is an API to help users extracting fact-checking-based Twitter data. Out of two options, i. e. , tweet search and article search, article search is utilized in the data extraction process. All the articles which matched according to keyword searching are then used to extract data for specific news. The most relevant articles are then selected, and their spread is visualized on Twitter. Hoaxy shares little details of the tweet and retweets concerning Twitter data sharing policy. It includes Tweet Ids, Retweet Ids, publishing DateTime, Retweet DateTime, Author user id, Retweeter user id, and bot scores of each user. The graph extracted by Hoaxy provides a tree-like network structure with a level one hierarchy.
B. CREATING FOLLOWERS AND FRIENDS RELATIONSHIPS USING TWEEPY Tweepy  [76]  provides access to extract the Twitter data. For further experimentation and exploratory analysis, Tweepy is used for the remaining data extraction. We extract profile features and a list of followers and followings of all tweet and retweet users. The extracted data is not only helpful in our experiments, but it also facilitates analyzing the rate of propagation, influence ratio, and the epidemic spread analysis based on the SIR  [77] -  [81]  model. It is essential to mention that Hoaxy extracted data does not provide the users' relations. Therefore, Tweepy is used to build user-related relations as directed edges using followers and the following information. For each extracted news from Hoaxy, we get three user relationships (edges) between each other, exploiting followers and the following information.
C. CONSTRUCTION OF TRUE AND FAKE NEWS COMMUNITIES The true news networks and fake news networks have been constructed using the above-extracted dataset. The relationship between Tweeter and Re-tweeter provides the edge list for network construction. The directed networks for both true news and fake news articles are loaded using Python's NetworkX library. Each of the 75 news items contains a CSV file with all the relevant features/attributes. The same number of graph files are generated using Cytoscape, showing the links between the users who tweeted/retweeted particular news. Sample snapshots of two such graphs showing real and fake news item community structure from our dataset are presented in Appendix A.
B. DATASET A total of 75 news (35 Fake and 40 True News) about COVID-19 have been extracted. We managed to extract 33,248 tweets which were tweeted by 18,940 users. Table  1  shows the breakdown of news and tweets. It is worth mentioning that the community (only authors are considered) of fake news users and real news users only marginally overlap with only 1% users. All the other users are either fully involved in fake information dissemination or real information dissemination. Almost half of the users are engaged in retweeting/re-posting both types of news. These users are not authors/posters.
Correlation Analysis: We measure the Pearson correlations between features to determine the degree of association between features for both categories of features. We also use the Seaborn library in python to visualize the correlations. Figure  3  presents network-based features correlations, whereas figure  4 , presents the correlation matrix between the user-based profile features. A high correlation value is in darker shades, whereas a lighter color means a weaker correlation. Based on the correlation we selected our best features  [83] ,  [84]  (see column B in table 2) from both networkbased features and user-based profile features. In correlation analysis, features at S. No: 1,3,4, 8, and 19 in table 2 are highly correlated to other features, therefore, recommended to be replaced with their correlated ones.
Exploratory Data Analysis-ECDF Plot: The Python library offers the ECDF class to fit an empirical cumulative distribution function and compute the cumulative probabilities for data attribute selection. A method for efficiently plotting and applying percentile thresholds for data exploration to an empirical cumulative distribution function (ECDF). Figure  5  presents ECDF plots for our userbased profile features, and figure  6  shows ECDF plots for network-based features for both True and False news labels. The majority of previously shortlisted, through mean value distribution and correlation (total 17) features, are also discriminating in ECDF plots. It also appears that the network features are slightly more dominant than the user profilebased features.
The dependent variable is found to be binary when we are dealing with a Logistic Regression classifier. The main application of Logistic Regression is to predict and calculate success probability. Using Logistic Regression classifier, the random state we use is one and set Broyden-Fletcher-Goldfarb-Shanno (bfgs) algorithm as a solver. A hyperplane is created by a Support Vector Machine that distinguishes the data points into two distinct classes. The training data is mapped into kernel space by SVM  [85]. Out of many possibilities, we use the linear kernel for the SVM classifier. A requirement for Naïve Bayes classifiers, which are quite scalable, is that the relationship between numbers of parameters to the number of features is linear. The purpose of a k-Nearest Neighbors' algorithm is to store all accessible cases and classifying new cases on the criteria of a distance function.
Random Forest is a meta estimator that fits several decision tree classifiers on various sub-samples of the dataset. Furthermore, the predictive accuracy is improved, and overfitting is controlled using averaging. The maximum depth of the tree is one, and reinforced measures are ''Gini. '' The trees that conduct the classification of instances on the criteria of feature values are Decision Trees. A feature, which can be classified in an instance, is represented by each node of the Decision Tree, and the value assumed by the node is represented by a branch. The EVC classifier is used along with three estimators (Logistic Regression, Decision Tree, and SVM)to predict the class. Many weak learning models are combined, forming a strong predictive model. This is achieved by a group of machine learning algorithms called Gradient Boosting classifiers.
Fifty estimators are utilized in Gradient Boosting classifiers using a depth of 2. LightGBM can handle huge amounts of data with ease. This algorithm uses the Gradient Boosting framework, which in turn uses a tree-based algorithm allowing the tree to grow vertically by having a default learning rate of 0. 1. CATBoot is a classifier that is rated first and does not require tuning of the parameter. It is an extensible GPU version. This classifier helps in the reduction of overfitting and increase the accuracy of the model. As per the framework, we divide the dataset into two phases, the training phase: which learns how to classify data (constructs a model) based on the training set. Testing phase: in testing, we check those unseen data examples that do not come under the dataset we have considered for training and now the trained model predicts.
C. DEEP LEARNING CLASSIFICATION MODELS After testing the data with several conventional supervised methods, we test the deep learning model as it always gives an efficient output. The two most famous deep learning methods Recurrent Neural Network (RNN) and Convolution Neural Network (CNN) are implemented to see how well our data fit into the model. Such algorithms are ideal for various classifications based on datasets. Keras provides easy and reliable high-level APIs and follows the best methods to reduce the cognitive burden for users, using Keras as a neural network library. Unlike normal neural networks, RNNs rely on prior output knowledge to predict upcoming information. This role becomes extremely helpful when combined with sequential knowledge. An LSTM network is a recurrent neural network with LSTM cell blocks instead of the usual neural network layers.
Latent representation methods are not considered e. g. ,  [88]  and  [89]  because their performance are not much competitive. 5. Source-based models  [58]  that indirectly focuses on propagators to detect deceptive contents. The performance of the best-proposed model is compared with the baselines using three datasets (Politifact, Gossip-Cop, Covid-19). Table  11  reports the results of the comparative analysis. it outperforms all its alternatives whatever their type: content-based (linguistic), Propagation/ Networkbased, Hybrid, Style-based, and Source-based models. One can see that Network/Propagation-based model and Content-based (linguistic) model are far behind, with accuracy scores ranging from 64 % to 80%. Style-based and Source-based models perform better. The hybrid model is the best alternative.
Although content-based models are better for early detection. Remember that in the final phase of experimentation, conducted on the publicly available datasets of PolitiFact, GossipCop  [27]  and our COVID-19 dataset, only the best set of features are used. A supervised classifier is trained for deception detection. It is very encouraging that the proposed model outperforms all baselines. It is trained on different datasets covering three domains (politics, entertainment, and COVID-19 pandemics). The proposed model is implemented with minor modifications to better suit the political and entertainment domains. Table  11  shows that the accuracy scores for the proposed model are much higher than all the others. The proposed model's accuracy score for the COVID-19 dataset is 98. 4, and for PolitiFact, it is 92, whereas it is 91 for the GossipCop.
In this exploratory study, we perform a comparative analysis of different machine learning and deep learning models. The best results are produced by Approach II when these features are applied at the node level. CATBoost and RNN are the most effective algorithms with an AUC score of 98%. All non-linear models perform better than the others. The performance of deep learning models is similar to modern machine learning models. The best set of features is also explored and used for further model optimization. A comprehensive comparative study with baselines covering all spectrum of alternative approaches (content-based, Propagation/Network, Hybrid, Source-based) is also conducted using three datasets from various domains (COVID-19, political, entertainment). Results are very encouraging. Indeed, the proposed approach always produces the best results.
■ INTRODUCTION The current method used in hospitals for rapid bacterial identification rely on computational tools tailored toward matrix-assisted laser desorption ionization-mass spectrometry (MALDI-TOF MS). 1  The diagnostic is based on comparing an experimental spectrum obtained in the 2000-20 000 Da range generated from intact proteins from a bacterial colony and profiles of known bacteria stored in a database. 2 Although the approach can characterize bacterial pathogens in more than 80% of the cases, it fails when the database lacks appropriate profiles or when challenged with closely related pathogens that give similar protein patterns. Moreover, it does not bring any information on the presence of virulence of resistance determinants. There is, therefore, a need for new strategies that overcome these limitations.
We recently demonstrated that top-down proteomics (TDP) could be successfully employed to discriminate closely related pathogenic bacterial strains such as the enterobacteria Escherichia coli, Shigella, and Salmonella. 3  Our approach relied on LC-MS/MS analysis of intact proteins obtained from bacterial cultures and the subsequent identification of specific discriminative proteoforms. Identification was achieved using Prosight, a widely adopted database search engine,  4  and a proteoform database generated from Uniprot. We noted that the use of Uniprot was biased because of the high variability in the quality of the available microbial proteomes. For reference species, such as E. coli K12, many reviewed protein sequences are accessible and allowed reliable identification of proteoforms. In contrast, the databases available for poorly studied microbes contained mostly nonreviewed, missing, or misannotated sequences.
In addition, these protein databases usually contain only a little or no information on amino acid variations or modifications of proteins. These shortcomings justify why many high-quality MS/MS spectra remain unidentified and opens room for new tools to significantly increase the repertoire of discriminant proteoforms. To circumvent this limitation, we present a new computational pipeline, called DiagnoTop, that uses spectral clustering and pattern recognition to statistically list discriminant spectral clusters based on MS/MS data, without database search. Our pipeline relies on Top-Down Garbage Collector (TDGC) to serve as a gatekeeper and guarantee a high-quality spectra feed to DiagnoProt, 5 a software previously introduced by us, that shortlists discriminative spectral clusters  6  and tailored here for the analysis of TDP data.
■ EXPERIMENTAL SECTION Computer Setup. DiagnoTop is composed of TDGC and DiagnoProt; both components are programmed in C# and require. NET Framework 4. 8 to be installed in a computer with at least 16 GB RAM and Windows 10. TDGC is available at http://patternlabforproteomics. org/tdgc/, and DiagnoProt at http://patternlabforproteomics. org/diagnotop/. Our software support MS2, MGF format files, and can read directly from Thermo RAW files. Our pipeline can handle various activation approaches, e. g. , Higher-Energy C-trap Dissociation (HCD), Electron Transfer Dissociation (ETD), and ETD combined to HCD (EThcD). TDP Data Set. We used DiagnoTop on a TDP data set previously published by us  3  and available in ProteomeXchange 7 (PXD019247). Briefly, 12 enterobacterial strains from the Collection of Institut Pasteur were investigated: four Salmonella enterica enterica (serotype Enteritidis, Typhimurium, Newport, and Muenchen), one Shigella sonnei, two Shigella flexneri (serotypes 2a and 3), and five E.
Top 4 ions with an intensity threshold >1 × 10 5 were isolated with 1. 2 m/z width, fragmented with EThcD (10 ms, 10%) and then added to a dynamic exclusion window for 60 s. MS/MS scans were acquired at 60K resolving power, with 2 μscans, an AGC target value of 5 × 10 5 , and MIT of 250 ms. All experiments were performed in biological triplicates. The 36 corresponding raw files were processed with ProSight PC v4. 1 (Thermo Scientific) and Proteome Discoverer v2. 4 (Thermo Scientific) using the ProSight PD 3. 0 node. Spectral data were first deconvoluted and deisotoped using the cRAWler algorithm. Spectra were then searched using a two-tier search strategy with searches against an Uniprot XML database created from the sequences identified in the individual TDP analysis of the 12 strains after removing duplicates (1516 protein entries and 10 425 proteoforms).
For the task at hand, TDGC also helped speed up the processing and increase clustering precision (data not shown). In what followed, DiagnoTop was used to generate the Knowledge Base (KB). We define KB as a collection of spectral clusters derived from the various bacterial strains. We define a spectral cluster as a set of similar MS/MS spectra grouped according to a similarity function, in our case, the spectral angle. 8, 9 To compute the spectral angle, the spectra were first vectorized using bins of 0. 02 m/z width. Only spectra whose precursor ion retention time was within a 10 min tolerance and the precursor mass differ at most by 3. 5 Da were considered for belonging to the same cluster. This aimed to group all similar MS/MS spectra arising from the fragmentation of the same proteoform.
■ RESULTS AND DISCUSSION We processed the 36 raw files obtained for the TDP analysis of our 12 enterobacteria using the DiagnoTop pipeline described above. We determined the optimal spectral angle threshold for clustering by repeating the DiagnoTop routine by varying the spectral angle from 0. 25 to 0. 60 with a 0. 05 step. We selected the spectral angle that maximized the Bhattacharyya distance (BD)  10  applied to the set of points resultants from a 3D PCA mapping for different bacteria. We recall that the BD is a measure of the dissimilarity (or similarity) between different probability distributions that measures the relative closeness of the two samples. Figure  2  shows the BD resulting from the various spectral angles verified and suggests 0. 4 as optimal. We hypothesize 0. 4 should be adequate for most TDP data set using a similar setup as ours.
TDGC shortlisted 82 878 high-quality spectra when considering all our mass spectrometry runs. Table  1  summarizes the number of discriminant spectral clusters obtained using DiagnoTop and the discriminant proteoforms identified by ProsightPD 3. 0. For example, Table  1  indicates that Salmonella enteritidis contains 207 unique spectral clusters in at least two replicates, i. e. , spectra found in two or more biological replicates of Salmonella enteritidis and in no other strain. We considered as discriminant only spectral clusters (DiagnoTop) or proteoform identifications (ProsightPD) found exclusively for a bacterial strain. As shown in Table  1 , it is clear that DiagnoTop overperforms ProsightPD, whatever the conditions used, for the number of discriminant features. The DiagnoTop computing time was 6h30 while Prosight PD took approximately 10 days to create the proteoform databases and perform the searches.
■ CONCLUSION We introduced a computational pipeline named DiagnoTop that compares large TDP data sets and identifies discriminative spectral clusters without a database search. Applied to the analysis of 12 enterobacterial strains that cannot be differentiated with MALDI-TOF MS, DiagnoTop converged, in all cases, to a high number of exclusive spectral clusters that can be used to discriminate all strains from each other. We show that, in significantly less time, we reported many more discriminative spectral clusters than Prosight PD, a widely adopted commercial solution to analyze TDP data and characterization of proteoforms. DiagnoTop paves the way to classifying unknown bacterial strains (many times lacking sequence databases) by comparing discriminative spectral clusters to those previously stored in a spectral database.
Identification Procedure To extract all the parameters in the proposed model, a battery test system experimental procedure were designed to measure battery behavior conveniently ficiently. As shown in Figure  3 , an electric bench was developed in the laboratory in ration with EVE system company. The specific electric station allows charging a charging the battery cell, simulating the real operation conditions and measuring eral parameters of the cell (cell voltage, SOC, positive terminal temperature …). S was developed to ensure the data acquisition and also the test programming on t via the electrical bench. Therefore, the software monitors battery temperature an ples battery voltage and current once per time step to obtain charge and discharge  A LEV50N [31] Lithium Manganese Oxide (LMO/Graphite) battery was used to perform the electric circuit characterization; it has a nominal capacity of 50 Ah.
This is due to the response of the automatic controller (PID) used to cope with the voltage limitations in this study. Of course, it did not affect the identification parameters, as identifications are made with and without those overshoots, and finally they were avoided after processing. For the final test performed to identify the ECMs, the cell is initially fully charged and the profile in Figure  4  is applied. In order to obtain the optimal values of the ECM parameters, the Nelder-Mead method  [32, 33] , also called the simplex method, is used to estimate the parameters of each model. This simplex varies slowly with each parameter regarding the RMSD (cf. Equation (  9 )) between numerical and experimental results. A Matlab code has been elaborated to identify and optimize the values of the ECM parameters.
Details of this method can be found in  [33]. It is often implemented as a preprogrammed function in a lot of software. In our case, it is thus used under the Matlab software. As reported in Section 1, the parameters in the ECM models are multivariable functions of SOC, current, temperature, and number of cycles (ageing). This dependence provides a high nonlinearity in the model and make the model extraction hard and the test process long (i. e. , hundreds of cycle measurements at various temperatures). Therefore, some auxiliary parameters are simplified or ignored because they have only slight effects in Li-ion batteries, this is the case for usable capacity dependence and number of cycles, as suggested in  [20]. Cell temperature and SOC are important parameters; the ECM parameters dependent on the cell temperature and SOC are also identified.