wanyu/IteraTeR_v2 huggingface dataset:
Original: We investigate the influence of fake and traditional, fact-based, news outlets on Twitter during the 2016 US presidential election . Using a comprehensive dataset of 171 million tweets covering the five months preceding election day, we identify 30 million tweets, sent by 2.2 million users, which are classified as spreading fake and extremely biased news , based on a list of news outlets curated from independent fact-checking organizations, and traditional news from right to left.
Revised: The dynamics and influence of fake and traditional, fact-based, news outlets on Twitter during the 2016 US presidential election . Using a comprehensive dataset of 171 million tweets covering the five months preceding election day, we identify 30 million tweets, sent by 2.2 million users, which are classified as spreading fake and extremely biased news , based on a list of news outlets curated from independent fact-checking organizations, and traditional news from right to left.
Edit intent: clarity
Original: We investigate the influence of fake and traditional, fact-based, news outlets on Twitter during the 2016 US presidential election . Using a comprehensive dataset of 171 million tweets covering the five months preceding election day, we identify 30 million tweets, sent by 2.2 million users, which are classified as spreading fake and extremely biased news , based on a list of news outlets curated from independent fact-checking organizations, and traditional news from right to left.
Revised: We investigate the influence of fake news on Twitter during the 2016 US presidential election . Using a comprehensive dataset of 171 million tweets covering the five months preceding election day, we identify 30 million tweets, sent by 2.2 million users, which are classified as spreading fake and extremely biased news , based on a list of news outlets curated from independent fact-checking organizations, and traditional news from right to left.
Edit intent: clarity
Original: We investigate the influence of fake and traditional, fact-based, news outlets on Twitter during the 2016 US presidential election . Using a comprehensive dataset of 171 million tweets covering the five months preceding election day, we identify 30 million tweets, sent by 2.2 million users, which are classified as spreading fake and extremely biased news , based on a list of news outlets curated from independent fact-checking organizations, and traditional news from right to left.
Revised: We investigate the influence of fake and traditional, fact-based, news outlets on Twitter during the 2016 US presidential election . Using a comprehensive dataset of 171 million tweets in the five months preceding election day, we identify 30 million tweets, sent by 2.2 million users, which are classified as spreading fake and extremely biased news , based on a list of news outlets curated from independent fact-checking organizations, and traditional news from right to left.
Edit intent: clarity
Original: We investigate the influence of fake and traditional, fact-based, news outlets on Twitter during the 2016 US presidential election . Using a comprehensive dataset of 171 million tweets covering the five months preceding election day, we identify 30 million tweets, sent by 2.2 million users, which are classified as spreading fake and extremely biased news , based on a list of news outlets curated from independent fact-checking organizations, and traditional news from right to left.
Revised: We investigate the influence of fake and traditional, fact-based, news outlets on Twitter during the 2016 US presidential election . Using a comprehensive dataset of 171 million tweets covering the five months preceding the election day to identify 30 million tweets, sent by 2.2 million users, which are classified as spreading fake and extremely biased news , based on a list of news outlets curated from independent fact-checking organizations, and traditional news from right to left.
Edit intent: coherence
Original: We investigate the influence of fake and traditional, fact-based, news outlets on Twitter during the 2016 US presidential election . Using a comprehensive dataset of 171 million tweets covering the five months preceding election day, we identify 30 million tweets, sent by 2.2 million users, which are classified as spreading fake and extremely biased news , based on a list of news outlets curated from independent fact-checking organizations, and traditional news from right to left.
Revised: We investigate the influence of fake and traditional, fact-based, news outlets on Twitter during the 2016 US presidential election . Using a comprehensive dataset of 171 million tweets covering the five months preceding election day, we identify 30 million tweets, from 2.2 million users, which are classified as spreading fake and extremely biased news , based on a list of news outlets curated from independent fact-checking organizations, and traditional news from right to left.
Edit intent: clarity
Original: We investigate the influence of fake and traditional, fact-based, news outlets on Twitter during the 2016 US presidential election . Using a comprehensive dataset of 171 million tweets covering the five months preceding election day, we identify 30 million tweets, sent by 2.2 million users, which are classified as spreading fake and extremely biased news , based on a list of news outlets curated from independent fact-checking organizations, and traditional news from right to left.
Revised: We investigate the influence of fake and traditional, fact-based, news outlets on Twitter during the 2016 US presidential election . Using a comprehensive dataset of 171 million tweets covering the five months preceding election day, we identify 30 million tweets, sent by 2.2 million users, which contain a link to news outlets. Based on a classification of news outlets curated from independent fact-checking organizations, and traditional news from right to left.
Edit intent: clarity
Original: We find that 29 \% of these tweets disseminate fake or extremely biased news.
Revised: We find that 29 \% of these tweets spread either fake or extremely biased news.
Edit intent: fluency
Original:  We fully characterize the networks of users spreading fake and traditional news and find the most influential users.
Revised:  We characterize the networks of users spreading fake and traditional news and find the most influential users.
Edit intent: clarity
Original:  We fully characterize the networks of users spreading fake and traditional news and find the most influential users.
Revised:  We fully characterize the networks of information flow to find the most influential users.
Edit intent: clarity
Original:  To understand how fake news influenced Twitter opinion during the presidential election , we perform a Granger-causality test between the time series of activity of influencers and the supporters of each presidential candidate: Trump and Clinton.
Revised:  To understand how fake news influenced the presidential election , we perform a Granger-causality test between the time series of activity of influencers and the supporters of each presidential candidate: Trump and Clinton.
Edit intent: clarity
Original:  (ii) Remarkably , this causality is reversed for the fake news: the opinion of Trump supporters largely Granger-causes the dynamics of influencers spreading fake and extremely biased news .
Revised:  (ii) Remarkably , this causality is reversed for the fake news: the activity of Trump supporters largely Granger-causes the dynamics of influencers spreading fake and extremely biased news .
Edit intent: clarity
Original:  (ii) Remarkably , this causality is reversed for the fake news: the opinion of Trump supporters largely Granger-causes the dynamics of influencers spreading fake and extremely biased news .
Revised:  (ii) Remarkably , this causality is reversed for the fake news: the opinion of Trump supporters influences the dynamics of influencers spreading fake and extremely biased news .
Edit intent: clarity
Original:  (ii) Remarkably , this causality is reversed for the fake news: the opinion of Trump supporters largely Granger-causes the dynamics of influencers spreading fake and extremely biased news .
Revised:  (ii) Remarkably , this causality is reversed for the fake news: the opinion of Trump supporters largely Granger-causes the dynamics of the top fake news spreaders .
Edit intent: clarity
Original:  Despite the large body of theoretical work on parallel geometry algorithms , efficient implementations can be complicated.
Revised:  Despite the significant theoretical work on parallel geometry algorithms , efficient implementations can be complicated.
Edit intent: clarity
Original:  Despite the large body of theoretical work on parallel geometry algorithms , efficient implementations can be complicated.
Revised:  Despite the large body of theoretical work on these problems , efficient implementations can be complicated.
Edit intent: clarity
Original:  In this paper we focus on simple and efficient parallel algorithms and implementations for range and segment queries .
Revised:  We focus on simple and efficient parallel algorithms and implementations for range and segment queries .
Edit intent: clarity
Original:  We propose using a simple framework (the augmented map) to model the problem.
Revised:  We propose to use a simple framework (the augmented map) to model the problem.
Edit intent: fluency
Original:  For the sweepline algorithms, we also propose a parallel paradigm and show corresponding cost bounds.
Revised:  For the sweepline algorithms, we propose a parallel paradigm and show corresponding cost bounds.
Edit intent: clarity
Original:  Our sequential range query implementations outperforms the CGAL implementation by more than a factor of 2, and gets an additional 63-fold speedup on all cores .
Revised:  Our sequential implementation outperforms the CGAL implementation by more than a factor of 2, and gets an additional 63-fold speedup on all cores .
Edit intent: clarity
Original: Deep learning, a rebranding of deep neural network research works, has achieved  remarkable success in recent years.
Revised: Deep learning, a rebranding of deep neural network research works, has achieved a remarkable success in recent years.
Edit intent: fluency
Original:  With multiple hidden layers, deep learning models aim at computing hierarchical features or representations of the observational data.
Revised:  With multiple hidden layers, deep learning models aim at computing the hierarchical feature representations of the observational data.
Edit intent: clarity
Original:  Meanwhile, due to its severe disadvantages in data consumption, computational resources, parameter tuning efforts and the lack of result explainability, deep learning has also suffered from lots of criticism.
Revised:  Meanwhile, due to its severe disadvantages in data consumption, computational resources, parameter tuning costs and the lack of result explainability, deep learning has also suffered from lots of criticism.
Edit intent: clarity
Original:  In this paper, we will introduce a new representation learning model, namely "Sample-Ensemble Genetic Evolutional Network" (SEGEN), which can serve as an alternative approach to deep learning models.
Revised:  In this paper, we will introduce a new representation learning model, namely "Sample-Ensemble Genetic Evolutionary Network" (SEGEN), which can serve as an alternative approach to deep learning models.
Edit intent: fluency
Original:  Instead of building one single deep model, based on a set of sampled sub-instances, SEGEN adopts a genetic-evolutional learning strategy to build a group of unit models generations by generations.
Revised:  Instead of building one single deep model, based on a set of sampled sub-instances, SEGEN adopts a genetic-evolutionary learning strategy to build a group of unit models generations by generations.
Edit intent: fluency
Original:  The unit models incorporated in SEGEN can be either traditional machine learning models or the recent deep learning models with a much " smaller " and "shallower" architecture.
Revised:  The unit models incorporated in SEGEN can be either traditional machine learning models or the recent deep learning models with a much " narrower " and "shallower" architecture.
Edit intent: clarity
Original:  From the computational perspective, SEGEN requires far less data, fewer computational resources and parameter tuning works , but has sound theoretic interpretability of the learning process and results.
Revised:  From the computational perspective, SEGEN requires far less data, fewer computational resources and parameter tuning efforts , but has sound theoretic interpretability of the learning process and results.
Edit intent: clarity
Original:  Extensive experiments have been done on  real-world network structured datasets, and the experimental results obtained by SEGEN have demonstrate its advantages over the other state-of-the-art representation learning models.
Revised:  Extensive experiments have been done on real-world network structured datasets, and the experimental results obtained by SEGEN have demonstrated its advantages over the other state-of-the-art representation learning models.
Edit intent: fluency
Original:  Extensive experiments have been done on  real-world network structured datasets, and the experimental results obtained by SEGEN have demonstrate its advantages over the other state-of-the-art representation learning models.
Revised:  Extensive experiments have been done on real-world network structured datasets, and the experimental results obtained by SEGEN have demonstrate its advantages over the state-of-the-art representation learning models.
Edit intent: clarity
Original: We consider a multi-agent framework for distributed optimization where each agent in the network has access to a local convex function and the collective goal is to achieve consensus on the parameters that minimize the sum of the agents' local functions.
Revised: We consider a multi-agent framework for distributed optimization where each agent has access to a local convex function and the collective goal is to achieve consensus on the parameters that minimize the sum of the agents' local functions.
Edit intent: clarity
Original:  When the local functions are strongly-convex with Lipschitz-continuous gradients, we show that a subsequence of the iterates at each agent converges to a neighbourhood of the global minimum, where the size of the neighbourhood depends on the degree of asynchrony in the multi-agent network.
Revised:  When the local functions are strongly-convex with Lipschitz-continuous gradients, we show that the iterates at each agent converges to a neighbourhood of the global minimum, where the size of the neighbourhood depends on the degree of asynchrony in the multi-agent network.
Edit intent: clarity
Original:  When the local functions are strongly-convex with Lipschitz-continuous gradients, we show that a subsequence of the iterates at each agent converges to a neighbourhood of the global minimum, where the size of the neighbourhood depends on the degree of asynchrony in the multi-agent network.
Revised:  When the local functions are strongly-convex with Lipschitz-continuous gradients, we show that a subsequence of the iterates at each agent converge to a neighborhood of the global minimum, where the size of the neighbourhood depends on the degree of asynchrony in the multi-agent network.
Edit intent: fluency
Original:  When the local functions are strongly-convex with Lipschitz-continuous gradients, we show that a subsequence of the iterates at each agent converges to a neighbourhood of the global minimum, where the size of the neighbourhood depends on the degree of asynchrony in the multi-agent network.
Revised:  When the local functions are strongly-convex with Lipschitz-continuous gradients, we show that a subsequence of the iterates at each agent converges to a neighbourhood of the global minimum, where the size of the neighborhood depends on the degree of asynchrony in the multi-agent network.
Edit intent: fluency
Original:  We propose an algorithm wherein each agent operates asynchronously and independently of the other agents in the network .
Revised:  We propose an algorithm wherein each agent operates asynchronously and independently of the other agents .
Edit intent: clarity
Original:  When the local functions are strongly-convex with Lipschitz-continuous gradients, we show that the iterates at each agent converge to a neighborhood of the global minimum, where the size of the neighborhood depends on the degree of asynchrony in the multi-agent network.
Revised:  When the local functions are strongly-convex with Lipschitz-continuous gradients, we show that the iterates at each agent converge to a neighborhood of the global minimum, where the neighborhood size depends on the degree of asynchrony in the multi-agent network.
Edit intent: clarity
Original:  Numerical experiments demonstrate that Asynchronous Subgradient-Push can minimize the global objective faster than state-of-the-art synchronous first-order methods, is more robust to failing or stalling agents, and scales better with the network size.
Revised:  Numerical experiments demonstrate that Asynchronous Gradient-Push can minimize the global objective faster than state-of-the-art synchronous first-order methods, is more robust to failing or stalling agents, and scales better with the network size.
Edit intent: clarity
Original: Social Media offers a unique window into measuring attitudes like racism and homophobia, which could be relevant social determinants in human immunodeficiency virus (HIV) risk.
Revised: Social Media offers a unique window into attitudes like racism and homophobia, which could be relevant social determinants in human immunodeficiency virus (HIV) risk.
Edit intent: clarity
Original: Social Media offers a unique window into measuring attitudes like racism and homophobia, which could be relevant social determinants in human immunodeficiency virus (HIV) risk.
Revised: Social Media offers a unique window into measuring attitudes like racism and homophobia, exposure to which are potentially important social determinants in human immunodeficiency virus (HIV) risk.
Edit intent: clarity
Original:  However, individual Tweets can be noisy and existing areas by which exposures are measured, like Zip codes, average over  administratively-defined boundaries , limiting the use of the precise geo-information linked to social media .
Revised:  However, existing areas by which exposures are measured, like Zip codes, average over administratively-defined boundaries , limiting the use of the precise geo-information linked to social media .
Edit intent: coherence
Original:  However, individual Tweets can be noisy and existing areas by which exposures are measured, like Zip codes, average over  administratively-defined boundaries , limiting the use of the precise geo-information linked to social media .
Revised:  However, individual Tweets can be noisy and existing areas by which exposures are measured, like Zip codes, average over irrelevant administratively-defined boundaries , limiting the use of the precise geo-information linked to social media .
Edit intent: coherence
Original:  However, individual Tweets can be noisy and existing areas by which exposures are measured, like Zip codes, average over  administratively-defined boundaries , limiting the use of the precise geo-information linked to social media .
Revised:  However, individual Tweets can be noisy and existing areas by which exposures are measured, like Zip codes, average over administratively-defined boundaries .
Edit intent: coherence
Original:  Hence, we need a method to identify relevant, homogeneous areas of social attitudes .
Revised:  Hence, there is a need for a method to identify relevant, homogeneous areas of social attitudes .
Edit intent: coherence
Original:  Hence, we need a method to identify relevant, homogeneous areas of social attitudes .
Revised:  Hence, we need a method to define regions with consistent underlying degree of social media attitudes from noisy, geographically-inconsistent, individual Tweets .
Edit intent: clarity
Original:  Our approach (called Socio-spatial-SOMs, " SS-SOMs " ) uses neural embedding for text-classification and neural networks for clustering, to best identify regions of consistent social attitudes, semantically and geographically.
Revised:  Our approach (called Socio-spatial-SOMs, " SS-SOM " ) uses neural embedding for text-classification and neural networks for clustering, to best identify regions of consistent social attitudes, semantically and geographically.
Edit intent: fluency
Original:  Our approach (called Socio-spatial-SOMs, " SS-SOMs " ) uses neural embedding for text-classification and neural networks for clustering, to best identify regions of consistent social attitudes, semantically and geographically.
Revised:  Our approach (called Socio-spatial-SOMs, " SS-SOMs " pipeline to best identify regions of consistent social attitudes, semantically and geographically.
Edit intent: coherence
Original:  We demonstrate the impact of this new way to spatially represent social attitudes using mobility data from a cohort of men at high risk for HIV , finding that their exposure to racism and homophobia as measured using SS-SOMs differs by up to 42\% as compared to using Zip code based measures.
Revised:  We demonstrate the impact of this new way to spatially represent social attitudes using mobility data from a cohort of men at high risk for HIV to racism and homophobia as measured using SS-SOMs differs by up to 42\% as compared to using Zip code based measures.
Edit intent: clarity
Original:  We demonstrate the impact of this new way to spatially represent social attitudes using mobility data from a cohort of men at high risk for HIV , finding that their exposure to racism and homophobia as measured using SS-SOMs differs by up to 42\% as compared to using Zip code based measures.
Revised:  We demonstrate the impact of this new way to spatially represent social attitudes using mobility data from a cohort of men at high risk for HIV , finding that their exposure to racism and homophobia changes by up to 42\% as compared to using Zip code based measures.
Edit intent: clarity
Original:  We demonstrate the impact of this new way to spatially represent social attitudes using mobility data from a cohort of men at high risk for HIV , finding that their exposure to racism and homophobia as measured using SS-SOMs differs by up to 42\% as compared to using Zip code based measures.
Revised:  We demonstrate the impact of this new way to spatially represent social attitudes using mobility data from a cohort of men at high risk for HIV , finding that their exposure to racism and homophobia as measured using SS-SOMs differs by up to 42\% as compared to using Zip code-based measures.
Edit intent: fluency
Original: Social Media offers a unique window into attitudes like racism and homophobia, exposure to which are potentially importantsocial determinants in human immunodeficiency virus (HIV) risk .
Revised: Social media offers a unique window into attitudes like racism and homophobia, exposure to which are potentially importantsocial determinants in human immunodeficiency virus (HIV) risk .
Edit intent: fluency
Original: Social Media offers a unique window into attitudes like racism and homophobia, exposure to which are potentially importantsocial determinants in human immunodeficiency virus (HIV) risk .
Revised: Social Media offers a unique window into attitudes like racism and homophobia, exposure to which are important, hard to measure and understudied social determinants of health .
Edit intent: clarity
Original:  Hence,  there is a need for a method to define regions with consistent underlying degree of social media attitudes from noisy, geographically-inconsistent, individual Tweets .
Revised:  Hence, there is a need for a method to define the collective, underlying degree of social media attitudes from noisy, geographically-inconsistent, individual Tweets .
Edit intent: clarity