dataesr/scientific-paragraphs-categorization huggingface dataset:
Summary and outlook We analysed simultaneous XMM-Newton and NuSTAR data of Vela X-1 covering a broad X-ray range at orbital phase ∼0. 36-0. 52. For the spectral modelling, we used our partial covering model first described in  Diez et al. (2022). Thanks to the hard X-ray coverage permitted by NuSTAR and our results from previous work, we were able to constrain the continuum in order to focus on the absorption variability at lower energies with XMM-Newton EPIC-pn for this work. This is the first time that such a high-time-resolution absorption study of Vela X-1 has been carried out on a broad X-ray range from 0. 5 to 78 keV. We traced the onset of the wakes, which are characterised by a rise in the absorption column density N H,1 starting at orbital phase ∼0. 44 as well as local absorption variability due to the accretion of clumps.
The slope of the N H,1 rise is comparable, and is similar to previous observations, albeit with an orbital-phase lag indicating similar large-scale structures in the wind but with different orientation at different times of observation. We also compared our data with simulations from previous works in the literature but no strong match between observations and theoretical models could be found. This reflects the necessity for further and updated hydrodynamical simulations that account for the latest orbital parameter values (for example: eccentricity >0) and the complexity of the wind parameters (such as wind velocities and mass-loss rates). Through high-resolution spectroscopy of the multiple fluorescent lines present in Vela X-1, we performed X-ray photography of the material in the system.
The evidence of those lines at different absorption phases suggests sources of emission from local absorber to large-scale structures. This analysis also reveals strong photoionisation of the wind the presence highly ionised elements such as Lyα lines of O, Ne, Mg, Si, and S. However, these results have to be considered with caution as the XMM-Newton EPIC-pn energy resolution is not sufficient to perform an accurate spectral analysis of individual lines blending with can happen. This aspect is beyond the scope of this paper and the analysis of simultaneous XMM-Newton RGS data in a future work is needed to disentangle this. Moreover, we used a neutral absorber to describe the absorption from the wind, but the use of warmabs 5  for warm absorbers and photoionised emitters would be better suited, as would a higher resolution instrument (such as Chandra/HETGS).
A. 3. Cross-instrumental issues In comparison with XMM-Newton EPIC-pn data, we observe a soft excess in the NuSTAR data (see  Fig. A. 4). A possible explanation for this is the dust scattering effect. In this scenario, we suppose dense interstellar clouds located between the observer and the source. Those clouds will lead to deviation of the trajectory of low-energy photons that were not supposed to be observed. The low-energy photons will then be scattered towards the observer, producing a soft excess flux at low energy in the spectrum of the source. This scattering leads to the formation of the scattering halo as observed in Circinus X-1 in  Heinz et al. (2015). To check for this phenomenon, we extracted source regions of different sizes in the NuSTAR data from 10 arcsec to 50 arcsec is steps of 10 arcsec.
A. 4, we observe that the XMM-Newton EPIC-pn data are well described, albeit the residuals increase drastically for NuSTAR around 3 keV. Reciprocally, we tied CF XMM to CF NuSTAR and, as expected, the NuSTAR data are well constrained. However, there is a discrepancy up to a factor of 10 between the model and the XMM-Newton EPIC-pn data at 0. 5 keV as shown on the last residual panel of Fig. A. 4. None of the datasets are correctly described by an averaged CF. We conclude that the CF XMM is the most reliable value for the covering fraction and that the problem may be due to remaining calibration effects from NuSTAR at low energies. For the rest of this work, we assumed two independent CFs for each instrument as this is the best way to empirically compensate for the observed difference (see the first residual panel of Fig.
Acknowledgements. This work has been partially funded by the  Bundesministerium für Wirtschaft und Energie  under the  Deutsches Zentrum für Luftund Raumfahrt Grants   50 OR 1915. The research leading to these results has received funding from the  European Union 's  Horizon 2020 Programme  under the  AHEAD2020  project (grant agreement no. 871158 ) and from the  ESA Archival Research Visitor Programme. SMN  acknowledges funding under project  PID2021-122955OB-C41  funded by  MCIN/AEI/10. 13039/501100011033and  by ' ERDF A way of making Europe '. RA acknowledges support by the  CNES. Work at LLNL was performed under the auspices of the  US Department of Energy  under contract no. DE-AC52-07NA27344  and supported through  NASA  grants to LLNL. This work has made use of (1) the  Interactive Spectral Interpretation System (ISIS)  maintained by Chandra X-ray Center group at  MIT ; (2) the NuSTAR Data Analysis Software (NuSTARDAS) jointly developed by the  ASI Science Data Center (ASDC, Italy)  and the  California Institute of Technology (Caltech, USA) ; (3) the ISIS functions (isisscripts) (http://www.
sternwarte. uni-erlangen. de/isis/)provided by ECAP/Remeis observatory and MIT; (4) NASA's Astrophysics Data System Bibliographic Service (ADS); (5) the  User's Guide  to the XMM-Newton Science Analysis System, Issue 17. 0, 2022 (ESA: XMM-Newton SOC). We thank  John E. Davis  for the development of the slxfig (http://www. jedsoft. org/fun/slxfig/) module used to prepare most of the figures in this work. Others were created with the Veusz (https://veusz. github. io/)package. WebPlotDigitizer (https://automeris. io/WebPlotDigitizer/)( © Ankit Rohatgi ) has been used to digitise data from figures in older publications. A147, page 15 of 18 A&A 674,  A147 (2023 ).
ARTICLE Negativity bias in the spread of voter fraud conspiracy theory tweets during the 2020 US election Mason Youngblood 1,2 ✉ , Joseph M. Stubbersfield 3 , Olivier Morin 1,4 , Ryan Glassman 5 & Alberto Acerbi 6   During the 2020 US presidential election, conspiracy theories about large-scale voter fraud were widely circulated on social media platforms. Given their scale, persistence, and impact, it is critically important to understand the mechanisms that caused these theories to spread. The aim of this preregistered study was to investigate whether retweet frequencies among proponents of voter fraud conspiracy theories on Twitter during the 2020 US election are consistent with frequency bias and/or content bias. To do this, we conducted generative inference using an agent-based model of cultural transmission on Twitter and the Voter-Fraud2020 dataset.
g. , how many and which users replied, liked, and retweeted). Our ABM simulates a population of Twitter users with follower counts, activity levels, and probabilities of tweeting original tweets from real users in the observed data. Every six hours, a subset of users become active and either compose a new tweet or retweet an existing tweet. The probability of an existing tweet being retweeted is based on four factors:  (1)  the attractiveness of the content in the tweet (e. g. , emotional valence and/or intensity), (2) the follower count of the user who tweeted it, (3) how many times it has already been retweeted, and (4) the age of the tweet. The influence of each of these factors on retweet probability is controlled by separate parameters which correspond to content bias (c), follower influence (d), frequency bias (a), and age dependency (g), which we fitted to real data using the random forest version of ABC  (Raynal et al.
The data used in this study comes from a team of researchers at Cornell Tech, who retrieved millions of tweets and retweets relating to voter fraud conspiracy theories between October 23 and December 16, 2020  (Abilov et al. , 2021). After iteratively building a set of search terms from the seeds "voter fraud" and #voterfraud and using them to collect data in real-time, they estimate that they collected ~60% of tweets about voter fraud conspiracy theories during that period. An anonymized version of the VoterFraud2020 dataset is publicly available (https://github. com/sTechLab/VoterFraud2020), and Abilov et al. (Abilov et al. , 2021)  generously provided us with access to their full disambiguated dataset. Importantly, this dataset includes tweets from users who were "purged" from Twitter following the US Capitol attack  (Romm & Dwoskin, 2021).
Beyond the quality of the data and its intrinsic historical importance, the choice of this dataset was motivated by its exceptional scale and focus. Few other datasets gather such a large number of social media actors engaging in one conversation topic over a period of several months. Additionally, we conducted secondary analyses with general linear mixed models (GLMM) to assess the potential targets of content bias, while accounting for other factors such as follower count and whether the account holder was verified. Twitter verifies some accounts to make sure they are authorized by the person they claim to represent but only undertakes this costly verification for high-profile accounts whose status is signaled by a "blue check mark" icon (the data from this study predate Twitter's paid verification policy that began in the fall of 2022).
The emotional valence of tweets was measured using the valenceaware dictionary and sentiment reasoner (VADER), a sentiment analysis model trained for use with Twitter and other social media data  (Hutto & Gilbert, 2014). The output of VADER includes a compound score of the overall emotional valence and intensity from strongly negative to positive, in addition to the proportion of words that are negative, positive, and neutral. A large body of research suggests that content with negative valence has an advantage over content with positive valence across several domains  (Baumeister et al. , 2001; Rozin & Royzman, 2001). In digital media, evidence of negativity bias has been suggested within online "echo chambers"  (Asatani et al. , 2021; Del Vicario et al. , 2016)  and for tweets about political events both from individual users  (Schöne et al.
Methods Our methods, models, and predictions were preregistered in advance of data analysis (https://osf. io/jnvyf), except for the post hoc comparison between tweets and quote tweets. Departures from our preregistration that arose during peer review can be found in the SI. The data for this study comes from the VoterFraud2020 dataset, collected between October 23 and December 16, 2020, by  Abilov et al. (Abilov et al. , 2021). This dataset includes 7. 6 million tweets and 25. 6 million retweets that were collected in real-time using Twitter's streaming API. The VoterFraud2020 dataset was collected according to Twitter's Terms of Service and is consistent with established academic guidelines for ethical social media data use  (Abilov et al. , 2021). Abilov et al. (Abilov et al. , 2021)  started out with a set of keywords and hashtags that co-occurred with "voter fraud" and #voterfraud between July 21 and October 22, and expanded their search with additional keywords and hashtags as they emerged (e.
g. , #discardedballots and #stopthesteal). They estimate that their dataset includes at least 60% of tweets that included their search terms. Abilov et al. (Abilov et al. , 2021 ) also applied the infomap clustering algorithm to the directed retweet network to identify different communities that engaged with voter fraud conspiracy theories. We ran our analysis using only the user and tweet data from cluster #2, the "proponent" community that tweets primarily in English and does not have significant connections to members of the "detractor" community. We restricted our analysis to cluster #2 so that retweets would be indicative of the spread of the conspiracy theories among proponents, as opposed to discourse and debate between both proponents and detractors. The activity levels and original tweet probabilities from these data only reflect users' interactions with conspiracy theory content.
By using these as input to our agentbased model, we are only simulating the subset of users' behavior that pertains to the voter fraud conspiracy theory and is relevant for producing the observed retweet distribution. The agent-based model (ABM) we used has elements from Carrignon et al. (Carrignon et al. , 2019) , Lachlan et al. (Lachlan et al. , 2018) , and Youngblood and Lahti  (Youngblood & Lahti, 2022) , and is available as an R package on GitHub (https://github. com/masonyoungblood/TwitterABM). The ABM is initialized with a fully connected population of N users and is run for 216 timesteps, each of which corresponds to a 6-h interval in the real dataset (the highest resolution possible given computational limits). Each simulated user is assigned a follower count (T), an activity level (r), and a probability of tweeting an original tweet (µ) from a real user in the observed data.
Once the active users are done each tweet increases in age by 1 and the next timestep begins. Lastly, we should note that we chose to exclude "top n" dynamics (e. g. , trending topics) from our ABM, because they did not improve the fit of neutral models of cultural transmission on Twitter in Carrignon et al. (Carrignon et al. , 2019)  and hashtags/ keywords related to voter fraud rarely made it into the top trending topics during our study period (https://www. exportdata. io/trends/united-states). In summary, the following are the dynamic parameters in this ABM that we estimated using approximate Bayesian computation (ABC): All other parameters in the ABM were assigned static values based on the real dataset. The output of this ABM is a distribution of retweet frequencies (see Fig. 1 ), which was used to calculate the following summary statistics: (1) the proportion of tweets that only appear once, (2) the proportion of the most common tweet, (3) the Hill number when q = 1 (which emphasizes more rare tweets), and (4) the Hill number when q = 2 (which emphasizes more common tweets).
We used Hill numbers rather than their traditional diversity index counterparts (Shannon's and Simpson's diversity) because they are measured on the same scale and better account for relative abundance  (Chao et al. , 2014; Roswell et al. , 2021). The same summary statistics were calculated from the observed retweet distribution of the real dataset. For purposes of the summary statistic calculations, quote tweets were treated like original tweets, as they themselves can be retweeted. Then, the random forest version of ABC  (Raynal et al. , 2019)  was conducted with the following steps: • 500,000 iterations of the ABM were run to generate simulated summary statistics for different values of the parameters: c, d, a, and g.
4 (conformity bias), a = 0. 6 (anticonformity bias), c = 1 (content bias), and d = 1 (follower influence). The x-axis is the identity of each tweet ranked by descending retweet count, and the y-axis shows the number of times each of these tweets was retweeted. Both axes have been log-transformed. statistics and the optimal minimum node size were tuned to minimize prediction error using the tuneRanger package in R  (Probst et al. , 2018).
g. , negation, capitalization, punctuation). The main output of VADER is a compound score that sums and normalizes the weighted valences of the words in a tweet to give an overall score of emotional valence and intensity between -1 (strongly negative) and +1 (strongly positive)  (Fig F in the SI). VADER also outputs the proportion of words in a tweet that are identified as neutral, positive, or negative. VADER was specifically trained to handle emojis, URLs, hashtags, and tagged users during sentiment analysis so we did not remove those from our dataset. Upto-date details about VADER can be found in the GitHub repository (https://github. com/cjhutto/vaderSentiment). To determine the potential targets of content bias we conducted GLMM using the lme4 package in R  (Bates et al. , 2015). Retweet frequency was used as the outcome variable.
9% of tweets from users with missing verification statuses and follower counts were assigned verification statuses of "false" and follower counts of 0. Finally, we added the compound score, the proportion of negative words, and the proportion of positive words to see which measure of content best improves the model. All continuous predictor variables were scaled and centered prior to analysis. Model choice and residual diagnostic tests were conducted using a random 10% of observations, but the bestfitting model was run using the entire dataset. The Poisson family was used since our outcome variable was count data and did not appear to have over-or underdispersion issues (Fig H in the SI). To ensure that our decision to treat quote tweets like original tweets did not bias our results related to the content, we did a second round of GLMM to determine whether quote tweets have different emotional valence compared to the original tweets that they are quoting.
Results The VoterFraud2020 dataset is divided into several sub-communities, including both detractors and proponents of the conspiracy theories. We chose to focus on cluster #2, the "proponent" community that tweets and retweets content in English and does not have significant connections to the "detractor" community (see Methods). After subsetting the VoterFraud2020 data to only include user and tweet data from cluster #2, we ended up with 3,982,990 tweets from 341,676 users. Note that we calculated the number of users as all unique users that either tweeted or retweeted content from cluster #2. The agent-based model was initialized with a population size (N) of 341,676. The model and methods were preregistered in advance of the analysis (see Methods). The posterior distributions for content bias (c), follower influence (d), frequency bias (a), and age dependency (g) can be seen in Fig.
This is much more consistent with our personal experiences on Twitter, where the timeline includes a balance of new tweets from followers and trending tweets that have already been heavily retweeted. According to this interpretation, the wide 95% CI for a could reflect departures from neutrality resulting from conformity bias, variation in user behavior, or how Twitter's recommendation algorithm weights trending tweets. The posterior distribution for age dependency is also converging towards zero but has high out-of-bag error and probability mass across the entire prior. We chose to make no conclusions about this parameter. Chaotic posterior distributions such as this one can sometimes result from misspecified priors, but we feel that this is unlikely to be the case here as higher values of g generate retweet distributions that are further away from the observed data (Fig E in the SI).
Twitter's recommendation algorithm has just been made publicly available and has not yet been systematically studied (Twitter, 2023), so it is very difficult to differentiate between a bias produced by the algorithm or user behavior. However, we do know that the algorithm primarily uses author and engagement data  (Koumchatzky & Andryeyev, 2017)  and does not include information about the emotional valence of tweets (https://github. com/twitter/the-algorithm-ml/blob/main/projects/home/recap/ FEATURES. md), making this issue most relevant for the results related to follower influence and frequency bias. Based on the null models for the GLMM, the user appears to be the only grouping variable that explains a high level of variance in the data (ICC user = 0. 610, ICC date = 0. 109, ICC hour = 0.
The algorithm has just been made public and has not yet been systematically studied (Twitter, 2023), so we do not know how much it prioritizes tweets that are popular or from followed users. In the case of follower count, this makes it impossible to disentangle the effect of network structure from a demonstrator bias. Luckily, we do know that the recommendation algorithm does not use information about the emotional valence of tweets (https://github. com/twitter/thealgorithm-ml/blob/main/projects/home/recap/FEATURES. md). We were unable to collect the follower network because of account suspensions following the January 6 th attack  (Abilov et al. , 2021) , but even if we had access to the network structure the relevance of it would depend upon user behavior. If users primarily share information that they see passively on their timeline, then network structure is much more important, whereas if users are searching with keywords and hashtags, then it is less so.
The sentiment analysis in this study has several limitations that should be highlighted. First, some new phrases that emerged during the 2020 election (e. g. , "stop the steal") may differ in emotional valence compared to their constituent words in VADER's lexicon  (Hutto & Gilbert, 2014). Second, several of the common words and phrases used to create the VoterFraud2020 dataset (e. g. , "fraud") have negative valence in VADER's lexicon. Luckily, this only appears to introduce a slight skew in our valence measure (Fig F in the SI), which is common in studies of emotion and sentiment on social media  (Burton et al. , 2021; Ferrara & Yang, 2015b , 2015a; Pröllochs et al. , 2021)  and is unlikely to affect our results as GLMMs make no distributional assumptions about predictors  (Stroup, 2013).
Acknowledgements We would like to thank  Anne Kandler  for providing us with feedback on our agent-based modeling. This research was supported, in part, under  National Science Foundation  Grants  CNS-0958379 ,  CNS-0855217 ,  ACI-1126113 , and the  City University of New York High Performance Computing Center at the College of Staten Island. This work has received funding from the " Frontiers in Cognition" EUR  grant,  ANR-17-EURE-0017 EUR.
Data availability The agent-based model, analysis code, and processed data (everything required for replication) used in this study can be found on Zenodo (https://doi. org/10. 5281/zenodo. 8311560) and GitHub (https://github. com/masonyoungblood/TwitterABM). The full anonymized VoterFraud2020 dataset can be found on Abilov et al. 's website (https://github. com/sTechLab/VoterFraud2020). The full disambiguated dataset with tweet text is available from Abilov et al. upon request (mor. naaman@cornell. edu) to respect users' privacy.
Failing to adapt the model to cater for those individual and cultural differences hinges both the accuracy of the predictions and put the group of individuals under a threat of disparate impact of such predictions. However, the complex structure of the various factors that influence the disease may not be evident from the accessible health databases. Observational datasets coming from public information access systems can be fragmented, coming from diverse sources and may not necessarily include all the attributes relevant for the analysis. This paper showcases potential risks of biased or incomplete data and how causality can be put into practice as part of a risk management strategy to avoid discriminating systems. In this paper we focus on analysing the difference in causal and fairness impacts of different categories of variables linking sex or gender and COVID-19 severity.
The rest of this paper is organized as follows. First we present the most recent literature on gendered and sex-related effects of COVID-19 in Section 2. In Section 3 we conducted a data analysis on publicly available big datasets to investigate the possible impact of gender-lifestyle-related confounding factors on the COVID-19 outcomes. Section 5 presents a prioritized check-list for identifying and including confounding and mediating variables into the data. Next we introduce the necessary preliminaries for discussing causal fairness frameworks and mediation analyses 4. We provide a synthetic data based causal analysis illustrating the necessity to consider confounders and mediators to avoid discrimination in Section 6. Finally, we discuss results and open research directions for the future in Sections 7 and 8 respectively.