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Biden, Israel, Gaza, COVID, Russia, Ukraine, Trump, Putin, track mass media sentiment on any topic.

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Media Keywords Tag Cloud

News and mainstream media tag cloud of the hour, generated with our AI and updated in real time.

This tag clouds from media and news sites is mined in real time. It shows what global media are writing this hour. You can view main topics and media keywords to discover hidden patterns and systemically repeating content behind numerous news publications. Updated: Apr 21 13:05 UTC Try here.

yellowstone alzheimer ms entire view fusion japan post sinking snake betts als brain earthquake park oil lunar kindergarten alaska store shepherd myanmar band symptoms guns rentals ozempic jurors county gun uaw austin models record modi rosario disease afp photo daniels trump proteins emergency argentina kharkiv movement union weight india iran johnson hospital products adults album carolina cancer defense field care land indian school club sanctions construction israeli hush columbia blood national data file thailand utah temperature michael trial lake champions african shooting africa patients crimes moore housing weather workers ohio protesters species energy nba billion league stage driver records term jury girls staff voting rafah feet ll package speaker games professor gangs ban anti iranian army china hospitals swift ukraine music boat judge students ukrainian french biden court police laws husband cohen voters mass administration lawsuit team republican gaza list lawyers miles final troops study david russian program pregnant idaho officers incident season medical bills criminal film sports california researchers coast palestinians treatment special london japanese meeting jewish aid john carry injuries hamas co passed israel rep supreme signs manchester gang gov ministry block donald communities killed campaign olympic money children natural extreme uk legislation colorado wildlife love scientists earth paris election military common day north future historic industrial prepared parents climate drone violent research attack tax exchange former missiles action increase russia mike woman france official age tennessee defence office companies federal attacks oct governor bill win iraq fox democrats rights region development chinese system palestinian pro district discovered bodies am cities changes industry law tokyo olympics infrastructure texas tiktok taiwan bars schools found service base secretary star drones largest department wounded sex

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Who's dominating media and news landscape right now?

Updated: Apr 21 13:05 UTC :>

View trends of leading news topics. Our text-mining analysis is drawn from a comprehensive array of global English-language news sources and powered by AI for enhanced understanding of where the world is headed, in real time. Access data.

Donald Trump and Joe Biden

Trump (181.00) shows 94.62% higher Frequency of mentions than Biden (93.00).

Historically, Trump (mean: 277.52) tends to have 30.21% more Frequency of mentions than Biden (mean: 213.13).

Narrative centrality

Trump (5.50) shows 80.46% higher Narrative centrality than Biden (3.05).

Historically, Trump (mean: 4.87) tends to have 27.78% more Narrative centrality than Biden (mean: 3.81).

Trump (5.50) gains narrative centrality.


Biden (0.35) shows 268.51% higher Backgroundness than Trump (0.10).

Historically, Biden (mean: 0.22) tends to have 80.25% more Backgroundness than Trump (mean: 0.12).

Russia and Ukraine

Ukraine (177.00) shows 124.05% higher Frequency of mentions than Russia (79.00).

Historically, Ukraine (mean: 142.78) tends to have 8.47% more Frequency of mentions than Russia (mean: 131.63).

Narrative centrality

Ukraine (3.11) shows 43.27% higher Narrative centrality than Russia (2.17).

Historically, Ukraine (mean: 3.48) tends to have 18.32% more Narrative centrality than Russia (mean: 2.94).

Ukraine (3.11) gains narrative centrality.


Russia (0.50) shows 231.51% higher Backgroundness than Ukraine (0.15).

Historically, Russia (mean: 0.50) tends to have 36.86% more Backgroundness than Ukraine (mean: 0.37).

Gaza and Israel

Israel (213.00) shows 147.67% higher Frequency of mentions than Gaza (86.00).

Historically, Israel (mean: 161.31) tends to have 16.02% more Frequency of mentions than Gaza (mean: 139.03).

Narrative centrality

Gaza (2.93) shows 13.81% higher Narrative centrality than Israel (2.57).

Historically, Israel (mean: 3.72) tends to have 9.71% more Narrative centrality than Gaza (mean: 3.39).

Israel (2.57) gains narrative centrality.


Gaza (0.40) shows 203.55% higher Backgroundness than Israel (0.13).

Historically, Gaza (mean: 0.33) tends to have 7.57% more Backgroundness than Israel (mean: 0.31).

Tracking BIGCRUT: Biden, Israel, Gaza, COVID, Russia, Ukraine, Trump, Putin

Updated: Apr 21 13:05 UTC

We've selected keywords: Biden, Israel, Gaza, COVID, Russia, Ukraine, Trump, Putin (we call them BIGCRUT words) to observe how mainstream media (MSM) discusses these topics. We monitor these and many other keywords, and apply AI to mine textual data into the media's handling of significant global issues and political figures. We do it in order to explore future perspectives, narratives, and agendas shaping public discourse. For each keyword we automatically measure Frequency of mentions, Narrative centrality, Backgroundness, Polarity sentiment, Sentiment inconsistency, and Subjectivity sentiment. All plots are interactive (clickable).


Frequency of mentions

Term frequency measures how often BIGCRUT keywords appear in press. Simple and evident, this basic metric helps to understand the importance of keyword in media. A higher frequency suggesting potential significance or relevance across mainstream media (MSM) content.

Updated: Apr 21 13:05 UTC | Method: TF

Narrative centrality

Narrative centrality, derived from estimations of term frequency distribution parameters, measures the degree to which keyword is articulated and nuanced within news discourse. It assesses if there is coherence and focus in discussions on the topic. Is it Trump or Biden in the focus of media discussion at the moment? The higher the line, the more likely the word is in the spotlight of mainstream media attention, not simply mentioned.

Updated: Apr 21 13:05 UTC | Method: 1/sqrt_kp


Backgroundness, derived from Fisher information, highlights keywords that are indispensable, commonly referred to as "amen" words, and topics heavily fixated upon by the media. Uncovers how much the topic or keyword gets into the backdrop of discussions as the time goes on. The higher the line, the more the word is part of background story, and not in the spotlight.

Updated: Apr 21 13:05 UTC | Method: 1/fisher

Sentiment analysis of news

Sentiment analysis automatically extracts emotions from mainstream media news texts. Presented here are average scores that summarize sentiment for each BIGCRUT keyword (Biden, Israel, Gaza, COVID, Russia, Ukraine, Trump, Putin) across hundreds of media contexts in a given moment. On the plots you can see how measurements change in time. These time series plots are interactive and clickable.

Polarity sentiment

Polarity sentiment score shows overall emotional tone within a time frame, associated with keywords that we track. A higher score indicates a generally optimistic or favorable context, while a negative polarity suggests a pessimistic or unfavorable sentiment. So the higher the polarity sentiment score, the more positive is the sentiment expressed in the context around the word. The lower the line on graph, the more negative is the context of the mainstream talk. Is it Trump or Biden mentioned in a positive contex by MSM? Sentiment averages that fall below zero represent doom or public outcry.

Updated: Apr 21 13:05 UTC | Method: sentiment

Sentiment inconsistency or heterogeneity

When the curve on the plot is higher, it shows more disagreement among media sources when discussing the topic. Conversely, lower values of this score suggest a more unified opinion (i.e. media consensus). This analysis measures variance values with polarity sentiment scores. It quantifies how sentiments vary across different instances of a keyword in analyzed news texts. It allows to make conclusion about the spread of sentiment. For instance, within the same hour across various media outlets, sentiments towards an individual can vary significantly. Such is an example of sentiment inconsistency. The measure is similar to opinion spread, sentiment volatility, opinion variance, divergent sentiment, attitudinal diversity, or opinion heterogeneity. It captures sentiment variability across different contexts and media outlets, reflecting the statistical concept of variance in sentiment analysis.

Updated: Apr 21 13:05 UTC | Method: sentiment

Subjectivity sentiment

Subjectivity measures the degree of personal opinion or bias in the text. A higher subjectivity score suggests a more opinionated or biased viewpoint, while a lower score indicates a more objective or factual presentation. The higher the line on the plot, the more opinionated is media discussion of the topic. Each data point represents the mean sentiment score for a given timestamp and keyword, calculated from hundreds of media contexts that are being analyzed by AI.

Updated: Apr 21 13:05 UTC | Method: sentiment

Tutorials and tools

Text mining and exegesis

Text mining is a computational technique to analyze and extract not so evident information from large bodies of text. By applying advanced algorithms and statistical models, text mining can help uncover hidden knowledge: patterns, relationships, and insights not apparent to the human reader. Read how to mine religious and literary texts.