dataesr/scientific-paragraphs-categorization huggingface dataset:
29, 30  We first retrained VGGFace to recognize individuals of the FaceScrub dataset using a classification task. The model achieved good performance (mean average precision mAP@3 accuracy: 97%); however, when examining the distribution of the faces within the face space, we noticed that female and male faces were largely overlapping (see Figure  S1B  in Supplementary Information). This contrasts with results in psychology showing that both sexes are likely represented in separate regions within humans' perceptual face space. 29, 30  When training to recognize sex, faces collected on the same individual did not cluster together (Figure  S1A ), again challenging empirical findings in psychology. 30  In order to build a face space that simultaneously separates sex and groups together portraits from the same individual (see Figure  S1C ), we thus retrained VGGFace to simultaneously recognize sex and verify individual identity.
Measures of femininity in the face space Studies on human faces traditionally investigate two measures of femininity: the femaleness (sexual dimorphism between male and female faces  39  ) and the averageness within the female faces' category. 28  We evaluated the ability of both measures to predict facial femininity using the publicly available Chicago Face Dataset (CFD hereafter). We first projected every image of CFD onto our human face space and then reduced the dimensionality of this face space using a principal-component analysis (PCA). The first principal component (PC1) explained a relatively small fraction of the variance in features of the face space (15% for CFD); however, alone, it allowed us to separate the sexes with virtually no overlap (See Figure  S2 ). Each face i was thus described by its PC1 i score.
e. , attractiveness rated by people), rather than perceived femininity. For example, Ryali and collaborators  41  recently analyzed the CFD dataset using the widely used active appearance model 42 of face space. They predicted femininity as statistical typicality (log likelihood of being a woman), a measure close (but inversely related) to our measure of averageness (dcf) when the probability density function is unimodal (as observed with CFD's female face  41  ), and they correlated their predicted femininity with the perceived attractiveness. They found a significant, positive correlation (R = 0. 19) between the two measures, close in magnitude to the correlation between dcf and perceived femininity we found (R = À0. 16). For a more accurate comparison, we also calculated the correlation between our predictions of femininity (dcf and dcm) and perceived attractiveness.
As expected, with dcf, the correlation is significant (R = À0. 14, p = 0. 038) but slightly weaker than that with perceived femininity. With dcm, the correlation with perceived attractiveness is markedly stronger (R = 0. 43, p = 2. 7e-15) compared to what was previously reported. 41  Last, we evaluated the performance of our method with non-standardized portrait pictures, as found in the mandrill portrait dataset but not in the CFD. To our knowledge, there is no publicly available dataset of non-standardized portraits associated with empirical scores of perceived femininity. We thus used SCUT-FBP5500,  43  a dataset of non-standardized portraits retrieved from the Internet that display American and Asian people with faces of varying poses, expressions, and haircuts, with different illuminations and portrait backgrounds, and labeled for their perceived attractiveness.
Impact of femininity on mandrills' socio-sexual interactions We estimated the femininity of adult females' faces in mandrills, using the same methodology as developed in humans. We first learned a mandrill face space by retraining VGGFace to simultaneously classify the sex (accuracy after training: 99%) and verify the identity (accuracy after training: 60%) with a dataset of ca. 13,000 photographic portraits collected on 169 wild individuals. We then projected the images of all adult males and females into the face space, reduced the dimensionality of their features using a PCA, and eventually generated dcm values (i. e. , the distance of a female face to the mean score of males along PC1; Figure  1 ) for each female as a measure of their femininity. Contrary to the human datasets (CFD and SCUT-FBP5500), most study mandrills are represented by several pictures taken at different ages and in different conditions (maximum time lapse between two pictures of the same individual: 10 years), with a different number of pictures collected per individual (Figure  S5 ).
DISCUSSION In this study, we investigated whether facial femininity influences different socio-sexual interactions in a non-human primate. Using datasets with empirical scores of perceived femininity in humans, we first developed an AI-based method to predict femininity scores applicable to non-standardized pictures. We found that femaleness, a measure of sexual dimorphism, is more strongly correlated with perceived femininity than averageness, a measure of similarity to a mean female face. In mandrills, femaleness significantly influences several behaviors received from both male and female conspecifics, and this effect is consistent across various socio-sexual contexts, in all groupmates.
DCNN to study faces in the wild Our method allowed us to explain 27% of the variance in perceived femininity ratings when considering all the studied ethnic groups and thus outperformed other face space-based methods for predicting femininity. In comparison, a previous study using a face space to calculate shape dimorphism in women explained a maximum of 20% of the variance in femininity. 61  Another study  41  used the active appearance model  42  to construct a face space that takes into account both shape and texture information but not color. With this model, the authors explained 4% of the variance in attractiveness (these authors did not predict femininity) using the same dataset as the current study (CFD), compared to 18% of the variance in attractiveness with our method. As for studies on men, predictors of masculinity explain 15%  62  to 25%  39  of variance in perceived masculinity.
Image datasets For retraining VGGFace to verify identity and classify sex, we used a subset of the FaceScrub dataset 37 that includes 37,193 portrait images representing 530 individuals. Femininity and attractiveness analyses were performed with Chicago Face Database (CFD  83  ) and SCUT-FBP5500 dataset. 43  CFD provides standardized (neutral expression, frontal view, same clothes) photographic portraits of 597 American people (one photo per individual) aged 17 to 65 years, of different ethnic backgrounds (number of Women-Men; East Asian: 57-52, Black: 104-93; Hispanic: 56-52; White: 90-91). The face photographs were scored for attractiveness (from 1 to 6) and femininity (from 1 to 7) by independent judges. SCUT-FBP5500 includes 5,500 non-standardized portrait images of Asian (women: 2,000; men: 2,000) and White (women: 750; men: 750) people (one photo per individual) of different ages (15-60 years), and their associated scores of attractiveness (from 0 to 5) obtained by independent judges.
Mandrill portraits have been retrieved from the Mandrillus Face Database (MFD), which includes pictures collected between January 2012 and December 2021. The pictures were taken by field assistants directly in the forest, using different models of DSLR cameras and long-focal lenses, and different settings. Moreover, distance to the camera, illumination, pose and facial expression vary between pictures. Picture of MFD are thus qualified ''non-standardized''. All images have been cropped manually to keep the face only (without the ear), and rotated to align the eyes horizontally. The quality of each image has been manually scored. Quality 1 corresponds to pictures of average quality for which experienced field assistants are able to recognize the individual from the picture alone. Quality 2 describes pictures of good quality for which individual recognition is straightforward but the portrait does not meet the criteria of quality 3.
90  Female dominance rank is estimated using outcomes of approach-avoidance collected during focal or ad libitum observations and calculated using standardized David's score. 91  Adult females are divided into three classes of rank of similar size over the study period (HR-high rank, MR-medium rank and LR-low rank) because female rank is stable in this matrilineal society. 92  For each month of behavioral data collected on the focal females and their social partners, we determined whether these females were observed cycling that month (yes or no) thanks to a daily monitoring of female sexual cycles in the study population. Cycling females present conspicuous inflation of their genitalia, until they reach a maximum size corresponding to a fertile period that may last a few days. 33.
Finally, we controlled for the season of sampling (breeding vs. birthing). We standardized all continuous predictors to compare estimates (dcm and age). The identity (id) of the focal female and the year of data collection were considered as two random effects to control for repeated monthly observations on focal females and yearly variation in sociality. For the rate of copulation, for example, the model is RateOfCopulation $ (1 | id) + (1 | year) + Femininity + Sexual status + Age + Dominance rank + offset(log(Nb_Scan_IndAllmal)). GLMMs were run using spAMM package with the fitme function (method = c("ML", "obs")). In preliminary analyses, we considered all possible first-order interactions involving dcm as well as the female age as a quadratic effect. None were significant, we thus removed these first-order interactions and the quadratic effect of age from final models.
ACKNOWLEDGMENTS We are grateful to  Francois Rousset  for his help with statistics, to  Alison Ashbury  for proofreading the manuscript, to past and present field assistants of the Mandrillus Project who took photographic portraits, and to the  SODEPAL-COMILOG society (ERAMET group)  for their longterm logistical support and contribution to the Mandrillus Project. This study was funded by several grants that allowed long-term collection of data:  Deutsche Forschungsgemeinschaft  (DFG,  KA 1082-20-1 ),  SEEG Lekedi  ( INEE-CNRS)  to MJEC, and  Agence Nationale de la Recherche  to  MJEC  ( ANR SLEEP 17-CE02-0002 ) and to  JPR  ( ANR-20-CE02-0005-01 ). This is a Project Mandrillus publication number XX and  ISEM 2023-XX-SUD.
Microblogs have become popular media platforms for reporting and propagating news. However, they also enable the proliferation of misleading information that can cause serious damage. Thus, many efforts have been taken to defeat rumors automatically. While several innovative solutions for rumor detection and classification have been developed, the lack of comprehensive and labeled datasets remains a major limitation. Existing datasets are scarce and none of them provide all of the features that have proven to be effective for rumor analysis. To mitigate this problem, we propose a big data-sized dataset called DAT@Z21, which provides news contents with rich features including textual contents, social context, social engagement of users and spatiotemporal information. Furthermore, DAT@Z21 also provides visual contents, i.
However, these techniques remain dependent on the availability of comprehensive and community-driven rumor datasets, which have become a major road block. Most of the publicly available datasets contain only news contents or linguistic features  [20, 12, 14]. Apart from their individual limitations, the common drawback of those datasets is the lack of social context and information other than the text of news articles. In addition to news contents, a few datasets also contain social context, such as user comments and reposts on social media platforms  [11, 19, 16]. Although these engagement-driven datasets are valuable for fake news detection, they have mostly not covered any user profiles, except FakeNewsNet  [16]. Detecting rumors on social media is a very challenging task because fake news pieces are intentionally written to mislead consumers, which makes it difficult to spot fake news from news content only.
Thus, we need to explore information in addition to news content, such as the social engagements and social behavior of users on social media  [16]. The existing datasets cannot address this challenges. Thus, in this study we collect a comprehensive and large-scale repository with multidimensional information, such as textual and visual content, spatiotemporal information, social engagements, and the behaviors of users. The main contributions of this work are summarized as follows: -We aim to construct and release a comprehensive rumor dataset. DAT@Z21 provides multidimensional information on news articles and social media posts, while our dataset includes rich features such as textual, visual, spatiotemporal, and network information. We provide the methodological details on how it is built.
-We perform various exploratory analyses (data statistics and distributions) on news articles and on the social diffusion (tweets) of the dataset to understand their key properties and characteristics. -We conduct extensive experiments on the rumor classification task using the DAT@Z21 data and several baselines to validate its usefulness, especially its visual components. Baselines are obtained using either single-modal or multimodal information of tweets, and we utilize either traditional machine learning algorithms or deep learning models. The rest of this paper is organized as follows. We first survey related works in Section 2. We detail the data collection process, and we present exploratory statistics, analysis and data distribution in Section 3. Experiments using DAT@Z21 dataset to classify rumor credibility are designed and conducted in Section 4.
Fake Health News Datasets With the emergence of the COVID-19 pandemic, political and medical rumors have increased to a level where they have created what is commonly referred to as the global infodemic. In a short amount of time, many COVID-19 datasets have been released. Most of these datasets either have no annotations at all, they employ automated annotations using transfer learning or semi-supervised methods, or are not specifically designed for COVID-19 misinformation. The earliest and most noteworthy dataset depicting the COVID-19 pandemic at a global scale was contributed by the John Hopkins University  [5]. The authors developed an online real-time interactive dashboard 2  , which was first made public in January 2020. This dashboard lists the daily number of positive cases, the number of cured patients, and the mortality rates at a country and state/province level.
In terms of datasets collected for COVID-19 infodemic analysis and detection, examples include CoAID  [3] , which contains automatic annotations for tweets, replies, and claims for fake news; ReCOVery  [21] , which is a multimodal dataset annotated for tweets sharing reliable versus unreliable news, annotated via distant supervision; FakeCovid  [15] , which is a multilingual cross-domain fake news detection dataset with manual annotations; and  [4] , which is a large-scale Twitter dataset that is also focused on fake news. A survey of the different COVID-19 datasets can be found in  [9, 18].
Fake News Datasets A high-quality dataset plays an extremely important role in the task of rumor classification. However, the lack of labeled fake news datasets is a major bottleneck when building an effective detection system for online misleading information. Generally, news data with ground truth are gathered by either expert journalists, fact-checking websites or crowdsourcing. Although, there is no consensual benchmark for fake news detection  [17] , some publicly available resources are worth mentioning. Most of them only contain news contents, and more particularly textual content information. The BuzzFeedNews 3  dataset contains URLs from posts produced by nine verified Facebook publishers over a week close to the 2016 U. S. election from September 19 to 23 and September 26 and 27.
Each post and linked article is manually fact-checked and annotated for veracity by BuzzFeed journalists. It contains 1,627 articles-826 mainstream, 356 left-wing, and 545 right-wing articles. LIAR 4 [20] dataset is collected from fact-checking website PolitiFact 5. It has 12. 8K human labeled short statements collected from PolitiFact and the statements are labeled into six categories ranging from completely false to completely true as pants on fire, false, barely true, half-true, mostly true, and true. It is useful to find misinformation in short statements, but cannot be applied for complete news articles. The BS Detector 6  is a web crawler with knowledge about fake news websites, which was used to build a dataset by monitoring these websites. Labels are output by the BS detector not by human annotators.
NELA-GT-2018  [12]  provides 714k news articles in general topics from 194 news producers. The labels are obtained from eight assessment sites. Among the 194 news sources, 40 of them are not found any labels from the assessments sites. FA-KES  [14]  is a fake news dataset that focuses on the Syrian war. It contains 804 articles that labeled as real or fake. However, the labels were annotated based on a database from the Syrian Violations Documentation Center with a cluster algorithm, so the reliability of these labels may be of concerns. Apart from their own limitations, the common drawback of the data listed here is the lack of social context and information other than the text of news articles. Besides the news content, other researchers have also collected user engagement features of the news, such as user engagement on online social media.
FakeNewsNet 7   [16]  is collected from fact-checking websites PolitiFact and GossipCop. News contents and the related ground truth labels are crawled from these two websites. The authors collected social engagement through Twitter's Advanced Search API. This dataset contains 1,056 articles from PolitiFact and 22,864 articles from GossipCop. Each article is labeled as fake or true. Overall, the dataset nearly contains two million tweets. CREDBANK 8   [11]  contains about 1000 news events whose credibility are labeled by 30 annotators who are sourced from Amazon mechanical Turk. This dataset contains 60 million tweets that were posted between 2015 and 2016. Although these datasets are valuable for fake news detection, they mostly have not cover any user profiles except FakeNewsNet. Moreover, only one dataset includes visual data.
The existing datasets often only focus on the textual content of news articles, and little attention has been paid to visual content of both news articles and user engagement of news on social media. However, fake images on media posts can easily go viral on social platforms, and cause serious social disruptions. Unlike this category of datasets, we propose in this paper a new dataset: DAT@Z21. It not only includes both true and fake news articles but also their diffusion on social media. It contains all possible features of interests, including multi-modal information (textual, visual and spatiotemporal information), and user engagement data (user profile, tweets, retweets, replies, likes) as shown in Table  1  that compares our new dataset to existing ones. In addition, DAT@Z21 can be updated automatically to fetch the latest news article and social media content.
Data Collection In this section, we present the process that we followed to build DAT@Z21. We show how we collected data with a valuable ground truth. We will also show how we collected news articles and messages that were posted on social media. The overall data collection process can mainly be divided into three stages, as shown in Figure  1 : (1) collecting news articles with ground truth labels; (2) preparing tweets collection; and (3) collecting content generated on social media. The following subsections will describe these stages in more detail.
After identifying the web pages, we access and retrieve them using Politifact API. We then pull Fig. 1 : Data Collection pipeline for DAT@Z21 data out from the content of these pages using Beautiful Soup Python library 10. To guarantee a high quality ground truth, and reduce the number of false positive and false negative in the news labels of our dataset, we select only news articles that are explicitly tagged as true, false, or grossly false. We are particularly interested in the information related to each news article, which are: Figures  2  and 3  show some of the textual features of news content generated from both the title and the body text. From Figure  2 , we can note that the number of words in articles follows a scaling long-tail distribution, with a mean value of ≃ 2, 513 and an average of ≃ 2, 418.
Furthermore, Figure  3  shows the word cloud of all articles. Because the news articles are statements, reports and claims, which could be potentially a fake or real rumors, they naturally frequently share some vocabularies, such as "said" (#= 11, 131), "state" (#= 10, 112), "stated" (#= 9, 373), and "according" (#= 9, 208). We can also capture the source of the rumor from the titles of the news articles. In PolitiFact, this source is usually a statement from a politician but it 10 https://pypi. org/project/beautifulsoup4/ Fig. 2 : Word Count Distribution Fig. 3 : Word Cloud could also be a message posted on a social media or a blog. Figure  4  illustrates the top 10 sources for both real and fake news articles. With respect to real news, we can notice that "Facebook posts", "Viral images" and "Blog posting" are the three main sources of fake news articles.
Preparing the Tweets Collection In this stage, we track the diffusion of the news articles on the Twitter microblog, by collecting tweets of users that discuss or interact with these events. Because we aim to build a multimedia dataset with images, we only keep news articles with images attached. We start by constructing two sets of the most representative keywords from (1) the title and (2) the body text of each article. This way of proceeding is motivated, on the one hand, by the fact that the titles used by the journalists are not always significant and, on the other hand, by the intention to capture all the keywords of the news articles appearing either in the title or in the body of the text. For the title, we develop a natural language processing pipeline to extract key words by removing special tokens and common stop words.
In this step, we kept at most six key words to avoid over general queries. Given the relatively large size of the body text, we first use the TextRank extractive text summarization algorithm  [10]. The advantage of using an extractive algorithm rather than an abstractive one is to keep the original keywords of the text and the general meaning. We then apply our natural language processing pipeline to clean up the text. Note that we keep the named entities in both sets of keywords, which plays an important role for the semantics of the search queries. For example, Figure  6  shows the keywords extracted from the title and the body text of a news article.
Tweets Collection We have prepared two keyword sets that serve as queries to search for matching tweets. We next use the Twitter Academic research API  11  with the Full-archive search endpoint to collect the complete history of public tweets from March 2006 (Twitter creation) until September 07, 2021 (for the current version of the dataset). With a 1024 character limit on the query rules, this endpoint also supports a more advanced query language (Boolean) to help yield more precise, complete, and unbiased filtered results. Thanks to these features, we were able to create a single search query that combines the two keyword lists that were prepared earlier for each news article. The Full-archive search endpoint returns matching tweets with complete information such as their IDs, text, date and times of being created.