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A concern among content moderation researchers is that hard moderation measures, such as banning content producers, will push users to more extreme information environments. Research in this area is still new, but predominately focuses on one-way migration (from mainstream to alt-tech) due to this concern. However, content producers on alt-tech social media platforms are not always banned users from mainstream platforms, instead they may be co-active across platforms. We explore co-activity on two such platforms: YouTube and BitChute. Specifically, we describe differences in video production across 27 co-active channels. We find that the majority of channels use significantly more moral and political words in their video titles on BitChute than in their video titles on YouTube. However, the reasoning for this shift seems to be different across channels. In some cases, we find that channels produce videos on different sets of topics across the platforms, often producing content on BitChute that would likely be moderated on YouTube. In rare cases, we find video titles of the same video change across the platforms. Overall, there is not a consistent trend across co-active channels in our sample, suggesting that the production on alt-tech social media platforms does not fit a single narrative., Comment: This is a MeLa Lab Technical Report

A recent highly-publicized study by Park et al. (Nature 613: 138-144, 2023), claiming that science has become less disruptive over recent decades, represents an extraordinary achievement but with deceptive results. The measure of disruption, CD-5, in this study does not account for differences in citation amid decades of exponential growth in publication rate. In order to account for both the exponential growth as well as the differential impact of research works over time, here we apply a weighted disruption index to the same dataset. We find that, among research papers in the dataset, this weighted disruption index has been close to its expected neutral value over the last fifty years and has even increased modestly since 2000. We also show how the proportional decrease in unique words (highlighted by Park et al. (2023) is expected in an exponentially growing corpus. Finding little evidence for recent decrease in disruption, we suggest that it is actually increasing., Comment: 6 pages, 3 figures, submitted to Advances in Complex Systems on 11 April 2023

In this paper, we present the fifth installment of the NELA-GT datasets, NELA-GT-2022. The dataset contains 1,778,361 articles from 361 outlets between January 1st, 2022 and December 31st, 2022. Just as in past releases of the dataset, NELA-GT-2022 includes outlet-level veracity labels from Media Bias/Fact Check and tweets embedded in collected news articles. The NELA-GT-2022 dataset can be found at: https://doi.org/10.7910/DVN/AMCV2H, Comment: Technical report documenting the NELA-GT recent update (NELA-GT-2022). arXiv admin note: substantial text overlap with arXiv:2102.04567

In order to better support researchers, journalist, and practitioners in their use of the MeLa-BitChute dataset for exploration and investigative reporting, we provide new psycho-linguistic metadata for the videos, comments, and channels in the dataset using LIWC22. This paper describes that metadata and methods to filter the data using the metadata. In addition, we provide basic analysis and comparison of the language on BitChute to other social media platforms. The MeLa-BitChute dataset and LIWC metadata described in this paper can be found at: https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/KRD1VS., Comment: This paper is a Metadata Supplement to The MeLa BitChute Dataset

Stopping the malicious spread and production of false and misleading news has become a top priority for researchers. Due to this prevalence, many automated methods for detecting low quality information have been introduced. The majority of these methods have used article-level features, such as their writing style, to detect veracity. While writing style models have been shown to work well in lab-settings, there are concerns of generalizability and robustness. In this paper, we begin to address these concerns by proposing a novel and robust news veracity detection model that uses the content sharing behavior of news sources formulated as a network. We represent these content sharing networks (CSN) using a deep walk based method for embedding graphs that accounts for similarity in both the network space and the article text space. We show that state of the art writing style and CSN features make diverse mistakes when predicting, meaning that they both play different roles in the classification task. Moreover, we show that the addition of CSN features increases the accuracy of writing style models, boosting accuracy as much as 14\% when using Random Forests. Similarly, we show that the combination of hand-crafted article-level features and CSN features is robust to concept drift, performing consistently well over a 10-month time frame., Comment: Preprint Version

In this paper, we present an updated version of the NELA-GT-2019 dataset, entitled NELA-GT-2020. NELA-GT-2020 contains nearly 1.8M news articles from 519 sources collected between January 1st, 2020 and December 31st, 2020. Just as with NELA-GT-2018 and NELA-GT-2019, these sources come from a wide range of mainstream news sources and alternative news sources. Included in the dataset are source-level ground truth labels from Media Bias/Fact Check (MBFC) covering multiple dimensions of veracity. Additionally, new in the 2020 dataset are the Tweets embedded in the collected news articles, adding an extra layer of information to the data. The NELA-GT-2020 dataset can be found at https://doi.org/10.7910/DVN/CHMUYZ., Comment: 6 pages, 4 figures. arXiv admin note: text overlap with arXiv:2003.08444

Benjamin D Horne,1,2 Matthew J Hegewald,3 Courtney Crim,4 Susan Rea,5 Tami L Bair,1 Denitza P Blagev3 1Intermountain Medical Center Heart Institute, Salt Lake City, UT, USA; 2Division of Cardiovascular Medicine, Department of Medicine, Stanford University, Stanford, CA, USA; 3Division of Pulmonary Medicine, Department of Internal Medicine, Intermountain Medical Center, Salt Lake City, UT, USA; 4Research and Development, GlaxoSmithKline, Research Triangle Park, NC, USA; 5Care Transformation, Intermountain Healthcare, Salt Lake City, UT, USACorrespondence: Benjamin D HorneIntermountain Medical Center Heart Institute, 5121 S. Cottonwood Street, Salt Lake City, UT 84107, USATel +1801-507-4701Email benjamin.horne@imail.org Twitter@DrBenjaminHorneIntroduction: Tobacco use and other cardiovascular risk factors often accompany chronic obstructive pulmonary disease (COPD). This study derived and validated the Summit Score to predict mortality in people with COPD and cardiovascular risks.Methods: SUMMIT trial subjects (N=16,485) ages 40– 80 years with COPD were randomly assigned 50%/50% to derivation (N=8181) and internal validation (N=8304). Three external COPD validations from Intermountain Healthcare included outpatients with cardiovascular risks (N=9251), outpatients without cardiovascular risks (N=8551), and inpatients (N=26,170). Cox regression evaluated 40 predictors of all-cause mortality. SUMMIT treatments including combined fluticasone furoate (FF) 100μg/vilanterol 25μg (VI) were not included in the score.Results: Mortality predictors were FEV1, heart rate, systolic blood pressure, body mass index, age, smoking pack-years, prior COPD hospitalizations, myocardial infarction, heart failure, diabetes, anti-thrombotics, anti-arrhythmics, and xanthines. Combined in the Summit Score (derivation: c=0.668), quartile 4 vs 1 had HR=4.43 in SUMMIT validation (p< 0.001, 95% CI=3.27, 6.01, c=0.662) and HR=8.15 in Intermountain cardiovascular risk COPD outpatients (p< 0.001, 95% CI=5.86, 11.34, c=0.736), and strongly predicted mortality in the other Intermountain COPD populations. Among all SUMMIT subjects with scores 14– 19, FF 100μg/VI 25μg vs placebo had HR=0.76 (p=0.0158, 95% CI=0.61, 0.95), but FF 100μg/VI 25μg was not different from placebo for scores < 14 or > 19.Conclusion: In this post hoc analysis of SUMMIT trial data, the Summit Score was derived and validated in multiple Intermountain COPD populations. The score was used to identify a subpopulation in which mortality risk was lower for FF 100μg/VI 25μg treatment.Trial Registration: The SUMMIT trial is registered at ClinicalTrials.gov as number NCT01313676.Keywords: clinical decision tool, risk score, randomized controlled trial, Intermountain risk score, IMRS

A major concern with text-based news veracity detection methods is that they may not generalize across countries and cultures. In this short paper, we explicitly test news veracity models across news data from the United States and the United Kingdom, demonstrating there is reason for concern of generalizabilty. Through a series of testing scenarios, we show that text-based classifiers perform poorly when trained on one country's news data and tested on another. Furthermore, these same models have trouble classifying unseen, unreliable news sources. In conclusion, we discuss implications of these results and avenues for future work., Comment: Published in ICWSM 2020 Data Challenge

In this paper, we present a dataset of 713k articles collected between 02/2018-11/2018. These articles are collected directly from 194 news and media outlets including mainstream, hyper-partisan, and conspiracy sources. We incorporate ground truth ratings of the sources from 8 different assessment sites covering multiple dimensions of veracity, including reliability, bias, transparency, adherence to journalistic standards, and consumer trust. The NELA-GT-2018 dataset can be found at https://doi.org/10.7910/DVN/ULHLCB., Comment: Published at ICWSM 2019

The complexity and diversity of today's media landscape provides many challenges for researchers studying news producers. These producers use many different strategies to get their message believed by readers through the writing styles they employ, by repetition across different media sources with or without attribution, as well as other mechanisms that are yet to be studied deeply. To better facilitate systematic studies in this area, we present a large political news data set, containing over 136K news articles, from 92 news sources, collected over 7 months of 2017. These news sources are carefully chosen to include well-established and mainstream sources, maliciously fake sources, satire sources, and hyper-partisan political blogs. In addition to each article we compute 130 content-based and social media engagement features drawn from a wide range of literature on political bias, persuasion, and misinformation. With the release of the data set, we also provide the source code for feature computation. In this paper, we discuss the first release of the data set and demonstrate 4 use cases of the data and features: news characterization, engagement characterization, news attribution and content copying, and discovering news narratives., Comment: Published at ICWSM 2018. Dataset: https://github.com/BenjaminDHorne/NELA2017-Dataset-v1 Feature Code: https://github.com/BenjaminDHorne/Language-Features-for-News

In today's news ecosystem, news sources emerge frequently and can vary widely in intent. This intent can range from benign to malicious, with many tactics being used to achieve their goals. One lesser studied tactic is content republishing, which can be used to make specific stories seem more important, create uncertainty around an event, or create a perception of credibility for unreliable news sources. In this paper, we take a first step in understanding this tactic by exploring verbatim content copying across 92 news producers of various characteristics. We find that content copying occurs more frequently between like-audience sources (eg. alternative news, mainstream news, etc.), but there consistently exists sparse connections between these communities. We also find that despite articles being verbatim, the headlines are often changed. Specifically, we find that mainstream sources change more structural features, while alternative sources change many more content features, often changing the emotional tone and bias of the titles. We conclude that content republishing networks can help identify and label the intent of brand-new news sources using the tight-knit community they belong to. In addition, it is possible to use the network to find important content producers in each community, producers that are used to amplify messages of other sources, and producers that distort the messages of other sources., Comment: Peer-reviewed by NECO 2018 Workshop

Background: Identifying COPD patients at high risk for mortality or healthcare utilization remains a challenge. A robust system for identifying high-risk COPD patients using Electronic Health Record (EHR) data would empower targeting interventions aimed at ensuring guideline compliance and multimorbidity management. The purpose of this study was to empirically derive, validate, and characterize subgroups of COPD patients based on routinely collected clinical data widely available within the EHR. Methods: Cluster analysis was used in 5,006 patients with COPD at Intermountain to identify clusters based on a large collection of clinical variables. Recursive Partitioning (RP) was then used to determine a preferred tree that assigned patients to clusters based on a parsimonious variable subset. The mortality, COPD exacerbations, and comorbidity profile of the identified groups were examined. The findings were validated in an independent Intermountain cohort and in external cohorts from the United States Veterans Affairs (VA) and University of Chicago Medicine systems. Measurements and Main Results: The RP algorithm identified five LIVE Scores based on laboratory values: albumin, creatinine, chloride, potassium, and hemoglobin. The groups were characterized by increasing risk of mortality. The lowest risk, LIVE Score 5 had 8% 4-year mortality vs. 56% in the highest risk LIVE Score 1 (p < 0.001). These findings were validated in the VA cohort (n = 83,134), an expanded Intermountain cohort (n = 48,871) and in the University of Chicago system (n = 3,236). Higher mortality groups also had higher COPD exacerbation rates and comorbidity rates. Conclusions: In large clinical datasets across different organizations, the LIVE Score utilizes existing laboratory data for COPD patients, and may be used to stratify risk for mortality and COPD exacerbations.

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