[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-84896-en":3,"doc-seo-84896-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":13,"seo_description":14,"update_tm":28,"read_time":29},84896,1099514068035,"Ezra","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","Stable Sentiment and Persistent Dynamics in U.S. Economic News over 45 Years","Collective emotion is often inferred from mass media tone, yet it is not directly observed. This study extracts sentiment from text and uses daily sentiment indexes as proxies to investigate the temporal organization of U.S. economic news sentiment. Using a daily index derived from 24 newspapers (1980–2025), it finds that while average positive–negative balance stays broadly stable, sentiment states become far more persistent. Volatility falls, reversals become rarer, and negative bursts last longer, consistent with a minimal endogenous-memory model where long-lived latent sentiment weakens short-run corrections.","arXiv :2607 .06220v1 [physics .soc-ph] 7 Jul 2026  \nStable Sentiment and Persistent Dynamics in U.S. Economic News  \nover 45 Years  \nLuis E. C. Rocha 1,2  \n1 Department of Economics, Ghent University, 9000 Ghent, Belgium  \n2 Department of Physics and Astronomy, Ghent University, 9000 Ghent, Belgium  \nAbstract  \nCollective emotion is often inferred from the tone of mass media, but such emotion is not directly observed. One approximation is to extract sentiment from text and use sentiment indexes as proxies to study the temporal organization of news sentiment. Using a daily index of U.S. economic news sentiment from 24 newspapers (1980–2025), we examine whether the response time of this sentiment process has changed. Although the average balance of positive and negative coverage has remained broadly stable, the persistence of news sentiment states has increased substantially. In dynamical terms, this implies longer residence times in optimistic or pessimistic regimes and weaker short-run correction of sentiment shocks. Complementary statistics show declining sentiment volatility, fewer reversals, and increasing bimodality, i.e. a stronger separation between positive and negative sentiment states. We also find an asymmetry between bursts of negative and positive sentiment, with negative bursts tending to last longer. These patterns are consistent with a minimal endogenous-memory model in which a slowly evolving latent sentiment component becomes more persistent while short-range corrective feedback weakens. The findings indicate a change in the temporal response of the U.S. economic newspaper sentiment index over the last 45 years, with sentiment shocks leaving longer traces than expected under short-memory exponential decay. News-based sentiment is thus better modeled as persistent episodes rather than as daily reactions that reset after each event.  \nKeywords: news sentiment; sentiment persistence; memory; economic news; detrended fluctuation analysis  \nCorrespondence: Luis E. C. Rocha, [luis.rocha@ugent.be](luis.rocha@ugent.be)  \nIntroduction  \nCollective emotion, proxied by the tone of mass media and digital communication streams, is a central driver of attention and choice in complex socio-economic systems [1] . Narratives generated by news reflect and shape belief dynamics. They can become contagious stories that coordinate expectations and behavior, and sometimes detach from underlying structural drivers [2–5] . In large-scale information ecosystems, these narratives contribute to shaping how citizens perceive society, political conflict, crises, and institutional performance. Fluctuations in news sentiment thus act as a macroscopic state variable supporting the interpretation of new events rather than merely tracking them [6–8] . Text-based indicators derived from news outlets are shown to move systematically with macroeconomic conditions, policy actions, and geopolitical shocks [9, 10] . Digital traces of collective emotion and online communication further show that responses can be long-lived, socially reinforced, and organized in clustered avalanches [11–16] .  \nThe modern news ecosystem is not a passive mirror of the underlying state of the system. It is embedded in evolving media infrastructures that couple editorial decision-making, platform algorithms, and audience feedback loops [17, 18] . Text from news articles and social media has been used to construct measures of sentiment, attention, and uncertainty, which have been linked to macroeconomic forecasting, financial volatility, and variation in political perceptions across social groups [9, 19–23] . These couplings create feedback loops in which local coverage can amplify or dampen the impact of shocks on beliefs and investor trading [24] . Ranking and recommendation systems, on the other hand, reshape user exposure to diverse viewpoints and the diffusion of misinformation [25–31] . Observed news sentiment can thus be understood as an emergent signal shaped by t","cbCaik6XoZ3UUae6","https://ap.wps.com/l/cbCaik6XoZ3UUae6","pdf",474371,2,1,18,"English","en",105,"# Introduction\n## Rationale and prior work\n## Research objective and approach\n## Key findings and interpretation","[{\"question\":\"What data and method are used to build the U.S. economic news sentiment index?\",\"answer\":\"The study uses sentiment extracted from text to construct a daily sentiment index based on 24 U.S. newspapers covering 1980–2025. It applies a lexicon-based sentiment index approach to represent sentiment levels and daily sentiment change.\"},{\"question\":\"What changes over the last 45 years: the average sentiment level or its dynamics?\",\"answer\":\"The average balance of positive versus negative coverage remains broadly stable, but the persistence of sentiment states increases substantially. Sentiment shocks leave longer traces, indicating longer residence times in optimistic or pessimistic regimes.\"},{\"question\":\"How do the results relate to volatility, reversals, and sentiment state structure?\",\"answer\":\"Complementary statistics show declining sentiment volatility, fewer reversals, and increasing bimodality, meaning stronger separation between positive and negative sentiment states. Negative sentiment bursts also tend to last longer than positive ones.\"}]",1784199159,45,{"code":4,"msg":31,"data":32},"ok",{"site_id":25,"language":24,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"stable-sentiment-and-persistent-dynamics-in-us-economic-news-over-45-years","",{"@graph":36,"@context":85},[37,53,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,47,50],{"item":41,"name":42,"@type":43,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":20},"https://docshare.wps.com/document/","Document",{"item":48,"name":12,"@type":43,"position":49},"https://docshare.wps.com/document/research-report/",3,{"item":51,"name":13,"@type":43,"position":52},"https://docshare.wps.com/document/stable-sentiment-and-persistent-dynamics-in-us-economic-news-over-45-years/84896/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":24,"description":14,"dateModified":61,"datePublished":62,"encodingFormat":60,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":41,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-07-23","2026-07-16",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What data and method are used to build the U.S. economic news sentiment index?","Question",{"text":75,"@type":76},"The study uses sentiment extracted from text to construct a daily sentiment index based on 24 U.S. newspapers covering 1980–2025. It applies a lexicon-based sentiment index approach to represent sentiment levels and daily sentiment change.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What changes over the last 45 years: the average sentiment level or its dynamics?",{"text":80,"@type":76},"The average balance of positive versus negative coverage remains broadly stable, but the persistence of sentiment states increases substantially. Sentiment shocks leave longer traces, indicating longer residence times in optimistic or pessimistic regimes.",{"name":82,"@type":73,"acceptedAnswer":83},"How do the results relate to volatility, reversals, and sentiment state structure?",{"text":84,"@type":76},"Complementary statistics show declining sentiment volatility, fewer reversals, and increasing bimodality, meaning stronger separation between positive and negative sentiment states. 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