dataesr/scientific-paragraphs-categorization huggingface dataset:
Model Construction We cannot know beforehand what model will be good for our problem or what configuration to use. By analyzing both datasets, we found that classes are partially linearly separable in some dimensions. Thus, we evaluate a mix of simple linear and non-linear algorithms. The best result are achieved by four supervised classification algorithms: Classification and Regression Trees (CART), k-Nearest Neighbors (KNN), Support Vector Machines (SVMs) and Random Forest (RF). Then, we optimize the hyper-parameters of each model (Table  7 ) by testing multiple settings using the GridSearchCV function from the Python Scikit-Learn library  (Pedregosa et al. , 2011). Subsequently, we perform training and validation for each model through a 5-fold cross-validation to obtain stable out-sample results.
Feature Analysis The advantage of our approach is that we can achieve some elements of interpretability. To this aim, we conduct an analysis to illustrate the importance of each feature set. We depict the first most 15 important features achieved by RF in Figure  5 , which shows that, for both datasets, visual characteristics are in the top-five features. The remaining features are a mix of text content and social context features. These results validate the effectiveness of the IQA image features, as well as the the importance of fusing several modalities in the process of rumor verification. Eventually, to illustrate the discriminating capacity of these features, we deploy box plots for each of the 15 top variables on both datasets. Figure  6  shows that several features exhibit a significant difference between fake and real classes, which explains our good results.
To apply late fusion, we train two Random Forest (RF) classifiers by learning separately the visual and textual features (Figure  7 ). To obtain the final classification results, the predicted probabilities of the both classifiers are combined with (1) equal weights, by assuming that the two models are equally skillful and make the same proportional contribution to the final prediction; and (2) averaging the (optimized) weights by feeding the classifiers' output to a logistic regression model. Rumor Classification by Multimodal Fusion and Ensemble Learning Figure  8  shows that, for both datasets, the early fusion method and the two late fusion strategies, i. e. , equal weight and optimized weight, boost the prediction with different rates using separately two sets of features. Early fusion has the highest performance score, while for both late fusion techniques, equal weight is slightly more efficient than optimized weight.
Late fusion's performance is lower than that of early fusion because, when we train two models separately on visual and textual features, some dependencies between features are lost. Practically, there are some correlations between features, e. g. , between BRISQUE and Num Mention or between PIQE and Text Length. The potential loss of correlation in the mixed feature space is a drawback of late fusion. Another disadvantage of late fusion is its cost in terms of learning effort, as every modality requires a separate supervised learning stage. Moreover, the combined representation requires an additional learning stage. Applied machine learning often involves fitting and evaluating models on a dataset. Given that we cannot know what model will perform best on the dataset beforehand, this may involve a lot of trial and error until we find a model that performs good enough.
This is akin to making a decision using the single expert we can find. A complementary approach is to prepare multiple, different models, and then combine their predictions using an ensemble machine learning model. Because ensemble learning strategies such as bagging and boosting typically involve a single machine learning algorithm (generally a decision tree), we use instead the stacking strategy (also called metalearning) that seeks for a diverse group of members by varying model types. Figure  9  summarizes the key elements of a stacking ensemble: • an unchanged training dataset; • various machine learning algorithms (base models) for each ensemble member; • a machine learning model (metamodel) to learn how to best combine predictions.
Voting Ensemble We construct two voting models. The first one is a soft voting model called MONITOR sv that sums the predictions made by the classification models listed in Table  8  and predicts the class label with the largest sum probability. The second model is a weighted average voting model called MONITOR wav where model votes are proportional to model performance. The performance of each ensemble model on the training dataset will be used as the relative weighting of the model when making predictions. Performance is calculated using classification accuracy as a ratio of correct predictions ranging between 0 and 1, with larger values meaning a better model and, in turn, more contribution to the prediction.
Canonical Stacking Ensemble Following Wolpert (1992)'s canonical stacking strategy (Figure  8 ), we construct a model called MONITOR st. Concretely, we use three repeats of a stratified 10-fold cross-validation on the four classification models to prepare the training dataset (predictions) with the logistic regression metamodel. Furthermore, we train the metamodel on the prepared dataset as well as the original training dataset using a 5-fold cross-validation. This aims to provide an additional context to the metamodel to better combine predictions.
Blending ensemble Blending was the term commonly used for stacking ensembles during the Netflix prize in 2009. The prize involved teams seeking movie recommendations that performed better than the native Netflix algorithm. A one million US dollar prize was awarded to the team achieving a 10% performance improvement. In this stacking-type ensemble, base models are fit on the training dataset and the metamodel is trained on predictions made by each base model on the validation dataset. At the time we are writing this paper, Scikit-learn does not support blending. Thus, we implement a blending model called MONITOR bld using scikit-learn models. To implement our model, we need to split the dataset, first into training and test sets. Then, the training set is split again into two subsets used to train base models and the metamodel, respectively.
We use a 50/50 split on the training and test sets and a 67/33 split on the train and validation sets (Figure  10 ). Furthermore, we choose logistic regression as a metamodel (the blender), for the same reasons we mentioned about canonical stacking. We summarise the key implementation steps of our model in Algorithm 1. Fit the model on the full training dataset and store 7: end for 8: Fit a metamodel on the out-of-fold predictions 9: Evaluate the model on a holdout dataset or use model to make predictions Table  9  summarizes the results achieved by the best individual machine learning model (RF) and the five stacking algorithms.
Result Analysis Our comparative analysis of experimental results shows that all metalearning models are more efficient than the best individual machine learning model (RF), because by combining multiple models, the errors from a single basemodel are likely compensated by the other models. As a result, the overall  prediction performance of the ensemble is better than that of any single basemodel. Moreover, for both datasets, the canonical stacking algorithm outperforms all models with 98. 4% and 93. 6% of accuracy on MediaEval and FakeNewsNet dataset, respectively. The stacking model indeed takes advantages from the diversity of predictions made by contributing models. That is, all algorithms are skillful on the classification problem, but in different ways. Figures 12 and 13 depicts the accuracy score box plot and the Receiver Operating Curve (ROC) for the canonical stacking ensemble model compared to the standalone machine learning algorithms (MONITOR-RF, CART, KNN and SVM) on MediaEval and FakeNewsNet, respectively.
Conclusion and Perspectives To assess the veracity of messages posted on social networks, most of the existing techniques ignore visual contents and use traditional machine learning models for classification, although ensemble approaches are considered the state-of-the-art solutions for many machine learning challenges. Thence, in this paper, to improve the performance of message verification, we propose a multimodal fusion framework called MONITOR that uses features extracted from the textual content of messages, the social context and image features that have not been considered until now. We compare the performance of MONITOR with five metalearning ensemble models by combining four basepredictors (KNN, CART, SVM and RF). Extensive experiments conducted on the MediaEval benchmark and the FakeNewsNet dataset show that: • the image features that we introduce play a key role in message veracity assessment; • no single homogeneous feature set can generate the best results alone; • all ensemble algorithms outperform the best single base-model (RF), and canonical stacking achieves the best performance on both datasets.
Our future research includes two directions. In the short term, we plan to experiment with other, larger datasets and vary the type, combination and number of base models in the ensemble. Second, we plan to compare MONITOR's performance with a deep learning-based approach for rumor classification, deepMONITOR  (Azri, Favre, Harbi, Darmont, & Noûs, 2021a) , with the aim of studying the tradeoff between classification accuracy, computing complexity and explainability.
Fit blending model on predictions from base models(meta_x,y_val) Algorithm 1 Blending Ensemble Require: Dataset(X,y) input variables and output label 1: meta_x, meta_y ← empty list 2: Split Dataset into X_train, y_train, X_val, y_val and X_test, y_test train, validation and test sets 3: Create base models 4: Fit the blending ensemble 5: for all base-model do 6: Fit base-model on training set (X_train, y_train) 7: Predict with base-model on X_val 8: Store predictions in meta_x 9: end for 10: Convert meta_x to 2D array as input for blending model 11: Define blending model 12: 13: Make prediction with blending ensemble 14: for all base-model do 15: Predict with base-model on X_test 16: Store predictions in meta_y 17: end for 18: Convert meta_y to 2D array as input for blending model 19: Predict with blending model on meta_y 20: Evaluate blending model on y_test Algorithm 2 Super learner ensemble 1: Select a k-fold split of the training dataset 2: Select m base-models or model configurations 3: for all base-model do 4: Evaluate using k-fold cross-validation 5: Store all out-of-fold predictions 6:.
In US data,  Halla and Scharler (2011)  find that the influence of idiosyncratic output-growth shocks on consumption is smaller in States where the percentage of married is higher. Bertocchi et al. (2011)  consider household investment decisions, and conclude from their analysis of 14 years of Italian SHIW data that the married invest more in risky assets (as marriage is considered to be a safe asset). Anderson and Ray (2019)  show that marriage protects against the risk of death, especially for women. With respect to the labour market, there is a considerable literature on marriage and the business cycle. Schaller (2013)  analyses 32 years of US State-level panel data: marriage is shown to be pro-cyclical, with unemployment being associated with less marriage. Lichter et al. (2006)  appeal to individual-level NLSY-79 data, and conclude that the probability of transition from cohabitation to marriage rises with partner's education and the partner working; it is lower for the unemployed.
Early labour-market experiences also seem to matter. Ekert-Jaffé and Solaz (2001 and 2002)  and  Landaud (2019)  analyse different French datasets to show that early-career unemployment and temporary jobs reduce the probability of forming a couple; De La  Rica and Iza (2005)  come to similar conclusions using Spanish data. One interpretation is that unemployment provides a negative signal about the potential partner's unobserved characteristics. Some work has explicitly looked not at events that have already taken place, but rather future risk on the labour market. Schneider et al. (2018)  discuss the role of economic resources in marriage, which they extend to include wealth and expected future earnings. Schneider and Reich (2018)  continue in the same line, and find that union membership (as an indicator of economic security) is a predictor of marriage in NLSY-79 data.
Data and Estimation Sample The data we use here come from the European Community Household Panel (ECHP), a nationally-representative longitudinal survey of households that covered 15 European countries (including France) between 1994 and 2001. The sample size in France was on average 12,000 adults per wave. The interviews mainly took place between November and December of each year. The ECHP contains detailed information on respondents' socio-economic characteristics, income, employment conditions, social relations and so on. 3 Respondents report their marital status at each ECHP wave. The 1999 rise in the Delalande tax only applied to firms with 50 or more employees. The ECHP records the number of employees in the firm in which the respondent works using the following categories: "None", "1 to 4", "5 to 19", "20 to 49", "50 to 99", "100 to 499" and "500 or more".
Participants and Procedure Data were collected between January 2018 and February 2020 as part of a large crosscultural project (see OSF hidden). Participants were undergraduate students who volunteered their time and (in most countries) received no compensation. IRB approval was obtained at each institution when required, and all participants gave informed consent. Participants completed a set of scales that measured more variables than those described here (see hidden for all variables). Order of measures was randomized and data were collected via SurveyMonkey or Qualtrics (in rare cases, participants completed paper surveys). From the initial sample (N = 34,023), we removed records from 5,185 individuals who failed more than 1 of 3 attention checks or provided incomplete data. This yielded a final sample of N = 28,640 respondents (37% self-identified men) from 62 countries.
Factor Structure and Measurement Invariance of Communality and Agency Confirmatory factor analyses (CFAs) on the total sample, and then for each country, testes the two-factor structure of communality and agency. To assess model fit using maximum likelihood estimation we examined the Bayesian information criterion (BIC; lower BIC indicates better fit), comparative fit index (CFI; ≥ 0. 90 indicates acceptable fit), and root mean square error of approximation (RMSEA; < 0. 08 indicates acceptable fit;  Kline, 2016). We used the lavaan package  (Rosseel, 2012)  in the R environment (R Core Team, 2020) for analyses. As shown in Table  2  (bottom row), the two-factor model demonstrated good fit to the data in the total sample. Although the RMSEA criterion was not met in China and Japan, and CFI fell below the cut-off in 42 countries, we had to select the same model in every country to test for invariance.
Limitations and Future Research Our dataset covers a large cross-cultural sample but our participants were all university students and we did not control for their employment status. While using university students helps standardize the samples and makes them more comparable in terms of age and socioeconomic status, we cannot generalize our findings to all or most residents of the countries we studied. Moreover, cultures differ in response biases  (Grimm & Church, 1999) , and we did not control for such biases. However, we know from other studies that sex differences in personality traits do not reflect differences in response bias  (cf. Connelly et al. , 2019; Wang & Degol, 2017). We thus assume that our gendered self-view data are similarly valid. Recall that analyses examining both GGGI and PDI in two models including countrylevel wealth (GNI) as covariate, suggest that PDI is the only measure of egalitarianism that uniquely predicts sex differences in gendered self-views.
Conclusions Social role theory predicts that sex differences should shrink as societies embrace the profits of progress of vertical gender segregation. Conversely, evolutionary theories anticipate larger sex differences in more egalitarian countries  (Schmitt, 2015). Here, results from a large, 62-country dataset, show that sex differences in gendered self-views correlate differently with cultural egalitarianism depending on the dimension under examination: Sex differences in agentic self-views are smaller, and sex differences in communal self-views are larger, in more egalitarian countries. These patterns emerged across two distinct, objective and subjective nation-level indices of egalitarianism, and were driven more robustly by variations in men's than women's self-views. Moreover, whereas women's more communal self-views appear universal, men's more agentic self-views vary considerably across countries.
This research was funded by a grant from the  National Science Centre in Poland  (grant number:  2017/26/M/HS6/00360 ) awarded to  Natasza Kosakowska-Berezecka. Data collection by the following researchers was supported by grants as follows:  Emma C. O'Connor  (grant  RL5GM118963  from  National Institute of General Medical Sciences  of the  National Institutes of Health) ;  Angel Gomez  (grant  RTI2018-093550-B-I00  from the  Universidad Nacional de Educación a Distancia, Spain) ;  Sylvie Graf  and  Martina Hřebíčková  (grant  20-01214S  from the  Czech Science Foundation , and grant RVO:  68081740  from the  Institute of Psychology, Czech Academy of Sciences );  Teri A. Kirby  (grant  ES/S00274X/1  from the  Economic and Social Research Council) ;  Soledad de Lemus  (grant  PSI2016-79971-P  from the  Spanish Ministry of Economy and Competitiveness) ;  Michelle K.
In addition, we draw on  Gauthier's (2011)  comparative family policy dataset to estimate whether the generosity of family transfers moderates the association between family size and wealth. We use logistic regression modelling to investigate the probability of zero or negative wealth and linear regressions to assess family size differences in net worth percentile rank across contexts with varying generosity in family transfers. This study contributes both theoretically and empirically to the literature on wealth accumulation and inequality. More specifically, we demonstrate that whether the number of children is associated with wealth depends on two factors that have been overlooked in past research. First, previous mixed findings on the relationship between family size and wealth may be attributable to measuring the number of children continuously.
Semyonov and Lewin-Epstein (2013)  use data from 13 countries from SHARE, HRS, and the English Longitudinal Study of Ageing, to estimate the associations between a number of factors and wealth. They report that household size is not associated with net wealth in a pooled sample of 16 countries, but they do find negative associations in France and the United Kingdom as well as positive associations in Denmark, Switzerland, Belgium, and the USA. More recently using data from the British Household Panel Survey (BHPS) and Socio-Economic Panel Study (SOEP),  Lersch and Dewilde (2018)  find that underage children in the household decrease the amount of money saved each month in both the UK and Germany (see also  Lersch et al. , 2017 for Germany; Dockery & Bawa, 2015a , 2015b  for Australia). Studies do not only vary according to the data they are based on, but also how the association between family size and wealth is modelled.
Sample To test our hypotheses, we draw on waves 1, 2, 4, 5, 6, and 7 of SHARE  (Börsch-Supan et al. , 2013). SHARE is a household panel study fielded on a biennial basis that collects a wide range of economic, social, demographic, and health data on and included three new countries. Note that we do not use observations from the third wave of SHARE, also referred to as SHARELIFE, and SHARELIFE observations in the most recent wave, because the life history modules did not collect a number of the variables used in our analyses. The fourth, fifth, sixth, and seventh waves were collected in 2011, 2013, 2015, and 2017, respectively, with minor country differences in timing. We restrict our sample to respondents and their partners aged 50-65, because we are interested in wealth accumulation leading up to retirement.
Dependent Variables We measure wealth as the total net worth of the household, which encompasses both household real assets and household net financial assets. Household real assets is the sum of the proportional value of the primary residence owned by the respondent, the proportional value of the respondent's business owned by the respondent, the values of automobiles and other real estate, minus the respondent's mortgage on the main residence. Household net financial assets is the sum of bank accounts, bonds, stock and mutual funds, and savings for long-term investments, minus financial liabilities. We convert household net worth to purchasing power parities equivalent to 2015 Euros in Germany. The frequency of missing values among wealth variables in SHARE is high. We therefore draw upon the imputed data that SHARE provides for observations that were not the designated household respondent, i.
Independent Variables Family size is measured as the number of biological or adoptive children of both the respondent and their spouse. We include family size as a categorical variable (childless, one child, two children, three children, or four plus children) to account for nonlinear associations between family size and wealth as postulated by our hypotheses H1c and H2c. We use  Gauthier's (2011)  comparative family policy database to create a countrycohort indicator on the generosity of family transfers. For all our study countries, we have an annual indicator for the total tax and benefit transfers for a two-parent, twochild, one-earner family expressed as the percent of average gross earnings of a production worker. We then calculate country-cohort specific values by averaging over the years that individuals were between age 20 and 45.
Additional Analyses One limitation of our analytical approach lies in macro-level confounding. Specifically, institutional or cultural differences that vary across cohorts within countries or across countries within cohorts will not be captured by our country and cohort fixed effects and may bias our estimates. Two factors are particularly worrisome: the generosity of pension schemes and housing regimes. We drew on the Comparative Welfare Entitlements Dataset  (Scruggs et al. , 2013)  to include an indicator of pension generosity for our respondents at age 50. This indicator summarizes aspects of pension replacement rates, required years of work, the ratio of employee to employer contributions, coverage, and retirement age. In addition, we estimated the countrycohort prevalence of home ownership using SHARE.
Further, we argued that if family transfers reduces the monetary costs of children, then either the negative association between wealth and family size is smaller in contexts with more generous family transfers (H2a) or the positive association between wealth and family size is larger in contexts with more generous family transfers (H2b). In addition, we speculated that the negative association between one child families and families with many children and wealth is smaller in contexts with more generous family transfers, while the positive association between two children and wealth is larger in contexts with more generous family transfers (H2c). We used data from the Survey of Health, Ageing, and Retirement in Europe (SHARE) to estimate the relationship between family size and wealth of men and women between ages 50-65, born 1939-1967 from 14 European countries, Family Size and Parental Wealth: The Role of Family Transfers… and  Gauthier's (2011)  comparative family policy dataset to estimate whether the generosity of family transfers moderates the association between family size and wealth.